Science Science
Scientific Research in School Volume 8 Number 1 October 2026
Extension Journal
Honor Non Honores
Mission An Anglican community inspiring every learner every experience every day
Vision To be a leader in Christian education that is characterised by a global vision that inspires hope
Values Commitment Compassion Courage Integrity Respect
Senior Editor Dr Matthew Hill Creative Direction Mrs Susan Layton Dr Matthew Hill Research Supervisors Mrs Kathy Haigh Dr Matthew Hill Mrs Virginina Ellis Mrs Kirsten Prior
The Barker Institute
About the Scientific Research in School Journal. When the New South Wales Education Standards Authority announced a new course “Science Extension” to commence in 2019 we were thrilled that there was an opportunity for a formally asessed capstone experience in Science for our students. From the perspective of the Barker Institute it was an exciting chance to support students doing academic research, alongside other subjects such as History Extension, Music Extension and English Extension 2. Where many capstone project courses fail is the at final step of the research process – dissemination. Research is not merely the process of conducing an investigation and writing a report, but sharing it with the wider community so that people can learn, critique, have other student researchers at multiple schools build on the projects published. I am so glad to be able to publish this journal each year now celebrating 133 articles, each representing genuine contributions to science.
Dr Matthew Hill Director, Barker Institute Senior Editor, Scientific Research in School
Introduction Students are inherently curious, and the curious persist until they find the answers they seek. I have observed that more and more of their questions are those which are yet to have answers. In these 18 projects, our Science Extension students have pursued their questions with diligence, imagination, and courage, contributing their own expertise to create new knowledge. This spirit of inquiry is present throughout the school. I see curiosity in our youngest students, just beginning their school journey, who with scientific minds investigate the world around them as they play and interact. It continues through the primary years, culminating in the exhibition of personal interest projects of Year 6 students. Year 12 major works are another set of opportunities for students to follow their curiosity wherever it leads, and the Science Extension cohort exemplifies this so well. Under the expert mentorship of our own research professionals and supported by wider team of Science teachers, these students have demonstrated rigorous methodology, analytical thinking, and innovative problem-solving that define genuine scientific research. They have learned that research is more than just finding answers, but about being curious, asking meaningful questions, testing assumptions, and contributing thoughtfully to ongoing areas of exploration through the publication of their work.
“The important thing is not to stop questioning. Curiosity has its own reason for existing. One cannot help but be in awe when he contemplates the mysteries of eternity, of life, of the marvellous structure of reality. It is enough if one tries merely to comprehend a little of this mystery every day.” Albert Einstein, Life magazine, May 2, 1955 I am immensely proud of these young researchers and how they have fulfilled their curiosity in such a way that contributes to the scientific community. Their achievements remind us that when students are given the opportunity to conduct authentic research and share their discoveries with others, they learn that science is not simply a body of knowledge to be understood, but a shared endeavour to which they too must contribute.
Mr Phillip Heath AM Head of Barker College
One of the greatest strengths of the Science Extension course is that it gives students the opportunity to transcend learning about science, to experiencing and doing scientific research. This authentic process requires them to experience both the excitement and uncertainty that are fundamental to research. This year I had the privilege of experiencing the excitement and uncertainty first-hand as I joined the teaching and supervisory team rather than supporting just one or two students as I usually do. Based on how we are challenged to learn and grow as research supervisors as we journey alongside the students, I can confidently share the power of a course like this to prepare students for real-world research. I am grateful for the work of all Science staff at Barker for their investment in the Science Extension students. In particularly I want to honour and thank Dr Matthew Hill and Mrs Kathy Haigh, who along with outstanding laboratory staff, and supervision in the first half of the course from Mrs Kirsten Prior, supported these students through the Science Extension research program.
Mrs Virginia Ellis Head of Science
Research Supervisors
Mrs Kathy Haigh Course Coordinator Science Extension
Dr Matthew Hill Director, Barker Institute Science Teacher
Mrs Virginia Ellis Head of Science
Mrs Kirsten Prior Science Teacher
In these 18 academic articles, students wrestle with the extant literature to accurately describe a gap in scientific understanding, before implementing valid methods to produce novel, first-hand results and findings to address this gap. It has been a pleasure to journey with them and wrestle with complex ideas and communication. Together we have endured through complications, celebrated breakthroughs, and explored implications. We are incredibly proud of them personally, and also the work that they have produced. We look forward to seeing the impact of this work on future research in Science.
Contents PART 1: BIOLOGY AND EARTH & ENVIRONMENTAL SCIENCE From Soil to Solution: Assessing Fungal Inoculation in Hydroponic Basil Production Josh Gates
11
Cave Drip Rates as a Model of Groundwater Recharge and Relationships with Weather Patterns Sophia Bock
19
Comparative Phytoremediation Efficacy of Six Native Australian Wetland Macrophyte Species in Removing Nitrate-Nitrogen and Phosphate-Phosphorus from simulated piggery effluent Huw Lawrence
29
The Effect of Increasing Phosphate Pollution on Artemia Nyos Hatching Numbers Liam Wyatt
37
Aloe Vera Efficacy Against Fungal Growth in Textiles Mia Nocom
45
Antimicrobial Activity of Oil Extracted from Melaleuca quinquenervia bark against Enterococcus faecium and Kluyveromyces marxianus Emmett Daly
53
How Does PET (polyethylene terephthalate) Microplastics and Motor Oil Effect OEM (Pseudomonas, Flavobacterium, Arthrobacter and Azotobacter genera) Growth Peppa Chen
59
Quantification of microplastic release from synthetic, natural and mixed fabrics during simulated washing Seraphina Herwono Antimicrobial protection in water-based cosmetic foundations: A comparative review of contemporary preservative systems Olivia Klockmann
65
71
Impact of Using OEM to Bioremediate Oil Spills Jimmy Li
79
Antimicrobial inhibition of Vinegar and Salt against Escherichia coli Baxter McLaughlin
87
PART 2: CHEMISTRY Effect of UV Light on Chalcone Synthase Enzyme Activity in Purple Heart Plants (Tradescantia Pallida) Chloe Woodhead
97
An investigation into the comparative differences between Chemical and Mineral sunscreen water resistance under simulated dynamic beach agitation Patrick Anderson
103
Mitigating Osmotic Stress Through a Comparison of Bulk and Stepwise Chaptalisation in cherry wine fermentation Gracie Anderson
113
Contents PART 3: PHYSICS AND APPLIED MATHEMATICS Mechanical Reinforcement of FDM PLA Through Multi-Layer Epoxy Coating Matthew Toft
125
Validating FD-z as a biomarker for Preclinical Alzheimer’s Disease Ahan Sharma
137
Investigation into recursive Equations and their Generalised Functional Counterparts Eric Hayman
149
Study and Optimization of Piezoelectric performance of Rochelle salt filled 3D printed Honeycomb Composites Tong Zhu
157
P
Physics Scientific Research in School Volume 8 Issue 1 2026
Biology and Earth & Environmental Science
Eleven students explored environmental sustainability, pollution, antimicrobial treatments and applied biology.
With a rapidly increasing hydroponic market, Josh Gates assessed fungal inoculation in hydroponic basil production. Whilst groundwater recharge is difficult to measure, Sophia Bock modelled this through cave drip rates and weather patterns. Both projects used observable biological or environmental systems to investigate sustainable resource management. Huw Lawrence compared native wetland plants for removing nutrients from simulated piggery effluent, building on previous knowledge and expanding the number of different Australian native plants that can be used as biofilters. Liam Wyatt examined how phosphate pollution affected Artemia hatching. This study extends the use of Artemia species to pollutant sensitivity studies to assess the biological impacts and potential management of aquatic pollution. With concern regarding resistance to antimicrobial agents, these projects considered varied approaches to identifying solutions to controlling microbial growth. Mia Nocom tested aloe vera against fungal growth in textiles, Emmett Daly examined the antimicrobial properties of oil extracted from Melaleuca quinquenervia bark, Olivia Klockmann tested the efficacy of preservative systems in cosmetic foundations, and Baxter McLaughlin tested combinations of vinegar and salt to improve effectiveness against Escherichia coli. In terms of exploring microplastics Seraphina Herwono quantified microplastic release from fabrics during simulated washing; and Peppa Chen examined if oil eating microbes could be used to act on microplastics. Jimmy Li investigated the use of oil eating microbes alongside immobilised algal balls to assess the effectiveness for oilspill bioremediation. Their work explored pollutant sources, biological effects and remediation strategies.
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From soil to solution: Assessing fungal inoculation in hydroponic basil production Josh Gates Barker College Australia’s hydroponic industry is rapidly expanding, with the market valued at approximately AUD $684 million in 2025 (Australia Hydroponics Market Size, Share & Report 2026-2035, 2025). This investigation assessed whether fungal inoculation using a commercially available arbuscular mycorrhizal fungi (AMF) product influences plant growth in hydroponically grown Ocimum basilicum (garden basil). Two identical hydroponic systems were operated across two 40-day trials, with one system inoculated with AMF and the other acting as a control. To ensure experimental rigour, the physical systems were exchanged between trials to control for potential equipment-based variation. Plant growth was measured using above-ground biomass (green mass) and estimated root biomass (wet mass). Across both trials and the combined dataset, control plants consistently produced higher green mass than AMF-treated plants, with statistically significant differences in all comparisons (p < 0.05). Root mass showed a similar overall trend, although results were less consistent between trials. These findings indicate that AMF inoculation did not improve plant growth and instead reduced biomass under hydroponic conditions. This suggests that the benefits of AMF observed in soil systems do not directly translate to soilless environments. Ultimately, this study highlights the need for critical evaluation of commercially marketed additives, as their use in hydroponics may be ineffective or even counterproductive.
Literature Review The integration of beneficial fungi into crop production systems has long been recognised as a strategy for enhancing plant growth, nutrient uptake, and resilience to environmental stressors (Mohammadi et al., 2011). In soil-based agriculture, arbuscular mycorrhizal fungi (AMF) play a key role in mediating plant–soil interactions and improving nutrient acquisition (Mohammadi et al., 2011). These fungi form a mutual relationship with plant roots, where the plant supplies carbohydrates and the fungi assist in accessing nutrients that would otherwise be difficult to obtain. This relationship is particularly important for nutrients such as phosphorus, which are often not readily available in soil. AMF extend filament-like hyphae beyond the root zone, increasing the surface area for nutrient absorption and improving plant growth and stress tolerance (Mohammadi et al., 2011). As a result, AMF are widely considered an important component of sustainable agriculture. However, the expansion of hydroponic systems (see Figure 1) raises questions about whether these soilderived symbioses function in soilless environments. Hydroponic agriculture is a rapidly growing industry, valued at approximately AUD $684 million in Australia in 2025 (Australia Hydroponics Market Size, Share & Report 2026-2035, 2025), which has
increased interest in additives that claim to improve crop yield. Hydroponics allows precise control of nutrients and environmental conditions, often resulting in faster growth and higher yields due to the direct delivery of dissolved nutrients to roots (Sublett et al., 2018; Verdoliva et al., 2021). Despite these advantages, hydroponic systems lack the complex microbial communities present in soil.
Figure 1: Diagram of a standard hydroponic system.
As a result, there is increasing interest in introducing beneficial microorganisms, including bacteria and fungi, into hydroponic systems (Lee & Lee, 2015; Stegelmeier et al., 2022). These organisms are hypothesised to improve plant performance in similar ways to soil systems. However, the extent to which these benefits translate to soilless environments remains unclear.
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Scientific Research in School Volume 8 Issue 1 2026 Hydroponics and soil cultivation create fundamentally different root environments, or rhizospheres (see Figure 2), In soil systems, roots interact with microorganisms, organic matter, and soil particles, influencing nutrient cycling and plant health. In contrast, hydroponic roots are exposed to nutrient solutions with minimal biological interaction, resulting in a simplified and highly controlled system.
Figure 2: Plants growing in soil and water with rhizospheres marked.
This difference has significant implications for AMF function. In soil systems, AMF form extensive hyphal networks that enhance nutrient uptake and support plant growth (Mohammadi et al., 2011). However, hydroponic systems lack the physical structure required to support these networks. While inert media provide some support, they do not replicate soil complexity, and plant–fungus interactions are often more variable and less predictable (Laevens et al., 2024). Nutrient availability is another key factor influencing AMF effectiveness. In soil, limited nutrients encourage plants to form symbiotic relationships to improve nutrient uptake. In hydroponics, nutrients are readily available and tightly controlled, which may reduce plant reliance on fungal partners. Studies show that environmental conditions and nutrient levels strongly influence plant growth responses in hydroponic systems (Kim, 2021; Saha et al., 2016). Mycorrhizal inoculation success is highly dependent on environmental conditions, microbial composition, and plant factors (Rog et al., 2025). These conditions are fundamentally altered in hydroponic environments, raising questions about whether commercially available AMF products are suitable for use in these systems. Research into microbial additions has produced inconsistent results, with some studies reporting no significant benefit to plant growth (Aini et al., 2025). Despite this uncertainty, commercial AMF inoculants are widely marketed to hydroponic growers. However,
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experimental evidence does not always support these claims. For example, McNew et al. (2025) found no significant improvement in growth or nutrient content in basil grown with AMF in soilless systems, suggesting that their effectiveness may be limited or highly context dependent. Although extensive research supports the benefits of AMF in soil-based systems, evidence for their effectiveness in hydroponics remains limited and inconsistent. The absence of soil structure, reduced microbial diversity, and high nutrient availability may limit AMF colonisation and function (Laevens et al., 2024; Rog et al., 2025). Many studies investigating microbial additions in hydroponics also rely on small sample sizes or broad growth measures, making it difficult to draw reliable conclusions. Furthermore, there is limited research examining commercially available AMF products under realistic hydroponic conditions, despite their increasing use in industry. Few studies focus specifically on hydroponic basil, and existing research often shows no significant treatment effects (McNew et al., 2025). This highlights a clear gap between commercial claims and scientific evidence. Therefore, further investigation is required to determine whether AMF inoculation has a measurable effect on plant growth, particularly biomass and root development, in hydroponic basil systems.
Scientific Research Question To what extent does inoculation with a commercially available arbuscular mycorrhizal fungi (AMF) product influence above-ground biomass and root biomass (fresh mass) in hydroponically grown Ocimum basilicum?
Scientific Hypothesis It is hypothesised that inoculation of a hydroponic system with AMF will not significantly increase plant biomass in Ocimum basilicum, and may reduce growth due to the high nutrient availability and lack of suitable conditions for effective fungal symbiosis.
Methodology A pilot study was conducted during the December holidays to validate the proposed methodology. Commercially available seeds of sweet basil (Ocimum basilicum) were purchased from Bunnings Warehouse (brand “Country Value”). Seed packets with the latest expiration dates were selected and combined to
Scientific Research in School Volume 8 Issue 1 2026 minimise batch variation. Basil was chosen due to its rapid growth and suitability for hydroponic systems (Ciriello et al., 2023). Two “Pro-leaf” three-tier hydroponic systems were assembled according to manufacturer instructions (see Figure 3). Hydroponics allows precise control of nutrient availability and environmental conditions, making it suitable for growth experiments (Das et al., 2020). Each system contained 78 pots (26 per tier × 3 tiers). A commercially available arbuscular mycorrhizal fungi (AMF) preparation was obtained from a specialist supplier; AMF form symbiotic relationships with plant roots and can influence biomass production (Othman et al., 2024).
Plants were harvested after 40 days by cutting stems at the sponge surface. Above-ground fresh mass was measured using a calibrated digital scale that was calibrated using calibration weights, which is a standard proxy for yield in hydroponic basil studies (McNew et al., 2025; Walters & Currey, 2015). Measurement of root biomass presented a methodological challenge due to the growth of fine root structures within the sponge matrix. Attempts to separate roots from the sponge were not viable, as roots were tightly interwoven and removal resulted in loss of material. Dry mass measurement was also not appropriate, as the fine, hair-like roots became brittle and difficult to recover accurately, increasing measurement error. Several alternative approaches were trialled to improve accuracy. These included estimating sponge and basket mass using non-germinated units, and standardising water content by saturating all samples prior to weighing. However, both methods introduced significant variability due to differences in sponge water retention across the system, and in some cases resulted in negative calculated root mass values, indicating unreliable measurement.
Figure 3: Hydroponic stand growing Ociumum basilicum (Left) empty pro leaf hydroponic stand (Right))
A machine-cut inert sponge was used as the growth medium to ensure consistent mass across samples. Inert substrates provide structural support while minimising variability (Rieley & Summerfield, 1972). Seeds were chosen randomly by combining 2 seed packets then 2 were planted per sponge. During the first five days, sponges were misted twice daily, and germination was monitored until the two-true-leaf stage (day 7). Both systems were treated identically during this phase. After germination, a commercial nutrient solution (“Manutec Hydroponic Nitro Plus”) was applied weekly at 10 mL per 2 L, with pH adjusted to 6.5. The treatment system was then inoculated with AMF at the recommended rate (two tablespoons per 25 L; total six tablespoons per system), while the control system received no inoculation. Plants were grown for 40 days.
As a result, root biomass was measured using a wet mass proxy. While this method does not provide precise absolute values, it allows for consistent comparison between treatment groups. Therefore, root mass data in this investigation was interpreted primarily in terms of relative differences rather than absolute measurements. Root systems were photographed under controlled conditions in a water tank to ensure consistent hydration and visibility, allowing for qualitative comparison of root size and structure. Data analysis compared mean root mass between AMF-treated and control groups across all replicates. Descriptive statistics (mean and standard deviation) were calculated, and an independent samples t-test was conducted at a 95% confidence level (α = 0.05).
To control for system-specific effects, the experiment was repeated with treatment allocation reversed between systems in the second trial. Data from both trials were combined, resulting in 156 replicates per treatment. Systems were monitored weekly, with pH adjustment, water refilling, and nutrient addition as required.
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Results Data collected from the two trials were compiled in Table 1. The experimental procedure was followed as outlined, resulting in data collected from two separate trials. Out of the 312 individual plants, 43 did not germinate. These data points were excluded to prevent skewing of results (see Table 1). Table 1: Amount of non-germinated data sets removed. Trial 1
74
AMF inoculated 66
4
12
Control
.
Germinated Did not germinate
Trial 2
63
AMF inoculated 66
15
12
Control
Measurements for both green mass (above-ground biomass) and estimated wet root mass were collected for all remaining plants and recorded in the full dataset (available from the author). Photographs of the root systems from the two central rows of each tray were taken during the second trial to support qualitative comparison (available from the author). Qualitative observation indicates visible variation in root density and size between samples, although patterns were less consistently distinguishable than for above-ground biomass. For clarity, System 1 is reported as the control group across both trials. Although the physical hydroponic systems were swapped between trials to control for equipment effects, they were re-labelled so that System 1 consistently represents the control condition and System 2 represents the AMF-treated condition. All data excluding the non-germinated plants were presented in 2 box plot graphs one for the green mass and one for the root mass (see Figure 4).
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Figure 4: 2 box plot graphs showing the full data sets. Root proxy mass (Right), green mass (left).
Statistical analysis was carried out using independent samples t-tests to compare the control and AMFtreated groups. Data were analysed separately for Trial 1, Trial 2, and as a combined dataset (see Table 2).
Scientific Research in School Volume 8 Issue 1 2026 Table 2: T-test results T-test Trial 1 Green mass
T-test Trial 2 Green mass
T-test Combined Trial Green mass
Group
system 1
System 2
Group
System 1
System 2
Group Mean (g)
System 1
System 2
Mean (g)
9.68
4.46
Mean (g)
7.48
5.62
SD (g)
4.88
2.54
SD (g)
5.62
4.7
8.68
5.04
5.32
3.81
0.57
SD (g) SEM (g)
SEM (g)
0.56
0.31
SEM (g)
0.71
N
74
66
N
62
0.45
0.33
66
N
136
132
P value < 0.0001
P value = 0.0443
P value < 0.0001
df =130
df=126
df=266
t=7.8020
t=2.0313
t=6.4132
Standard error of difference = 0.670 g
Standard error of difference = 0.915 g
Standard error of difference = 0.567 g
shows statistically significant
shows statistically significant
shows statistically significant
T-test Trial 1 Est. wet root mass
T-test Trial 2 Est. wet root mass
T-test Combined Trial Est. wet root mass
Group
system 1
System 2
Group
System 1
System 2
System 1
System 2
Mean (g)
17.9
15.23
Mean (g)
17.07
15.46
Group Mean (g)
17.52
15.35
SD (g)
3.39
3.3
SD (g)
5.23
4.62
4.34
4
SEM (g)
0.39
0.4
SEM (g)
0.65
0.56
SD (g) SEM (g)
0.37
0.34
N
74
66
N
63
66
N
137
132
P value < 0.0001
P value = 0.0666
P value < 0.0001
df = 138
df= 127
df= 267
t= 4.6991
t= 1.8506
t= 4.2582
Standard error of difference = 0.568 g
Standard error of difference = 0.869 g
Standard error of difference = 0.510 g
shows statistically significant
shows not statistically significant
shows statistically significant
Green Mass (Above-Ground Biomass) In Trial 1, the control system (System 1) had a mean green mass of 9.68 g, compared to 4.46 g in the AMFtreated system (System 2). This difference was statistically significant (t = 7.8020, df = 130, p < 0.0001). In Trial 2, the control system again showed a higher mean green mass of 7.48 g, compared to 5.62 g in the AMF-treated system. This difference was smaller than in Trial 1 but still statistically significant (t = 2.0313, df = 126, p = 0.0443).
In Trial 2, the control system again showed a higher mean value (17.07 g) compared to the AMF-treated system (15.46 g). However, this difference was not statistically significant (t = 1.8506, df = 127, p = 0.0666). When the data from both trials were combined, the control system had a mean root mass of 17.52 g, compared to 15.35 g in the AMF-treated system. This difference was statistically significant (t = 4.2582, df = 267, p < 0.0001).
When both trials were combined, the control system had a mean green mass of 8.68 g, compared to 5.04 g in the AMF-treated system. This difference was highly statistically significant (t = 6.4132, df = 266, p < 0.0001). Across all datasets, the control group consistently produced higher above-ground biomass than the AMF-treated group.
Overall Patterns Across both trials and the combined dataset, the control system consistently produced higher mean values for both green mass and root mass compared to the AMF-treated system. The difference in green mass was statistically significant in all cases, while the difference in root mass was less consistent, with one trial (Trial 2) not reaching statistical significance.
Estimated Root Biomass (Wet Mass) In Trial 1, the control system had a mean estimated root mass value of 17.90 g, compared to 15.23 g in the AMF-treated system. This difference was statistically significant (t = 4.6991, df = 138, p < 0.0001).
Standard deviations were relatively large in some datasets, indicating variability within treatment groups; however, the differences in mean values were sufficient to produce statistically significant results in most comparisons.
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Discussion The aim of this investigation was to determine whether inoculating a hydroponic system with arbuscular mycorrhizal fungi (AMF) would influence plant growth in Ocimum basilicum. The results clearly show that AMF treatment resulted in a negative effect on plant growth, with the control group consistently producing higher mean values for both green mass and root mass across both trials and the combined dataset. For above-ground biomass, the control system produced higher mean green mass in all cases, most notably in Trial 1 (9.69 g compared to 4.46 g) and in the combined dataset (8.68 g compared to 5.04 g). These differences were statistically significant, indicating a consistent reduction in shoot growth associated with AMF treatment. A similar pattern was observed for root mass. While the control system again showed higher mean values, the effect was less consistent, with Trial 2 not statistically significant (p = 0.0666). However, when combined, the difference became significant, reinforcing the overall trend. These findings indicate that AMF did not enhance plant growth in this hydroponic system and instead acted as a net cost to the plant. This contrasts with soilbased studies, where AMF typically improve plant growth and nutrient uptake (Mohammadi et al., 2011). However, these benefits are highly dependent on environmental conditions. In soil systems, AMF extend hyphal networks beyond the root zone, allowing access to nutrients such as phosphorus that are otherwise difficult to obtain. In return, plants supply carbohydrates, resulting in a beneficial exchange. In hydroponics, this balance is disrupted. Hydroponic systems represent an environment of extremely high nutrient availability, where plants have continuous access to dissolved nutrients without limitation (Rog et al., 2025). As a result, plants do not require fungal assistance for nutrient uptake. At the same time, the absence of a solid substrate limits the ability of AMF to form functional hyphal networks (Laevens et al., 2024). This reduces their benefit while potentially maintaining the carbon cost to the plant. Nutrient concentration, particularly phosphorus, likely played a key role. High phosphorus levels are known to suppress AMF colonisation and activity (Aini et al., 2025; Othman et al., 2024). In this investigation, nutrients were supplied at optimal levels, which may have reduced or prevented effective fungal colonisation. Under these conditions, the fungi may have offered little benefit, while still diverting
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energy from plant growth. This provides a clear mechanism for the reduced biomass observed. These results are consistent with recent literature showing that AMF are not universally beneficial. Othman et al. (2024) reported that approximately 34% of studies found no effect or a negative effect on biomass, while McNew et al. (2025) found no significant improvement in basil grown with commercial AMF products in soilless systems. This supports the conclusion that AMF effectiveness is highly context-dependent and often limited in hydroponic environments. A major strength of this investigation was the experimental design. By reversing treatment allocation between systems in the second trial, potential system-specific effects were controlled. This increases confidence that the observed differences were due to treatment rather than equipment variation. However, several limitations should be considered. Firstly, inconsistencies in system operation, such as variations in water pump performance, may have introduced small differences in growing conditions. Secondly, the use of a wet root mass (difficult to avoid in a hydroponic system where the matrix is a solid sponge) reflects the known limitations of the wet mass proxy method, particularly variation in sponge water retention due to variations in water retention, which may explain the less consistent root results. A key limitation of this investigation was the method used to estimate root biomass. Root mass was calculated using a wet mass proxy, which is sensitive to variation in water retention within both the sponge and the root tissue. This introduces non-biological variability, reducing the precision of the measurement. This limitation is consistent with the observed results, where root mass differences were less consistent and not statistically significant in Trial 2. The absence of statistical significance in Trial 2 likely reflects increased variability in the root biomass estimates, which reduced the statistical power of the analysis and limited the ability to detect differences between treatments. As a result, conclusions about root responses to AMF should be interpreted with caution, and the above-ground biomass data provides a more reliable indicator of treatment effects. Qualitative analysis of root images (Appendix C) supports the limitations of the quantitative root mass data. While some differences in root size and structure are visible, these variations are less consistent than those observed in above-ground biomass. This suggests that the variability observed in root mass
Scientific Research in School Volume 8 Issue 1 2026 measurements is likely influenced by both biological variation and methodological limitations, including water retention within the sponge and root tissue. Importantly, AMF colonisation was not directly measured. Research shows that colonisation in hydroponics can be low, particularly in early stages, and may require longer periods to establish (Othman et al., 2024). As a result, it is unclear whether the negative effects observed were due to an inefficient symbiosis or a failure of colonisation. This limitation could be addressed in future studies by using microscopic or staining techniques to confirm fungal presence. Additionally, hydroponic systems are sensitive to microbial interactions, and pathogens can spread rapidly through shared water systems (Laevens et al., 2024; Lee & Lee, 2015). While system hygiene was maintained, unintended microbial effects may have influenced plant growth. This study also focused on a single species over a short growth period. Basil was selected due to its fast growth and ease of measurement, but results may not be generalisable to other crops. Future research should therefore expand this work by investigating a broader range of hydroponic crops, such as strawberries or tomatoes, to determine whether the observed negative effects are species-specific. Further studies should also examine root morphology in more detail, including root density and branching patterns, to identify potential physiological changes not captured by total biomass measurements. In addition, testing AMF under reduced nutrient conditions, particularly lower phosphorus levels, would help determine whether the symbiotic relationship becomes beneficial when nutrient availability is limiting. Future work should also assess the reliability of commercial AMF products, as variation in inoculant quality and viability may influence outcomes (Rog et al., 2025). Overall, this investigation provides strong evidence that AMF inoculation can reduce plant growth in hydroponic basil under high-nutrient conditions. Rather than contradicting existing research, these findings highlight that the benefits of AMF are highly dependent on environmental context. While AMF are beneficial in soil systems, their role in hydroponics appears limited or negative when nutrient availability is high and structural conditions for fungal function are absent.
Conclusion This investigation demonstrates that the addition of arbuscular mycorrhizal fungi (AMF) to a hydroponic growth system did not improve plant performance in Ocimum basilicum. Instead, AMF-treated plants consistently produced reduced green mass compared to controls, with statistically significant differences across both trials and the combined dataset. Root mass showed a similar pattern, although results were less consistent. Overall the Hypothesis was supported as a not significant increase was found and a significant decrease was shown. These findings suggest that the cost–benefit balance of the plant–fungus relationship is altered in hydroponic systems. While AMF improve nutrient uptake in soilbased environments, their role appears limited or potentially detrimental when nutrients are already readily available. In this context, the carbon cost of maintaining the symbiosis may outweigh any benefits. These results have important implications for hydroponic agriculture, where AMF products are often marketed as universally beneficial. This study shows that their effectiveness cannot be assumed in soilless systems and highlights the need for further research before they can be reliably recommended.
Declaration of use of AI During the development of this project, I used an AI language model (ChatGPT) to assist with specific aspects of written communication, using prompts such as “Improve this method section for clarity and scientific tone suitable for a Year 12 Science Extension report,” “Rewrite this results section using correct scientific conventions and past tense,” and “Cross check this discussion with the annotated bibliography to ensure that all key information is included.” The AI-generated responses were used as drafting support only. All suggestions required careful revision to ensure scientific accuracy, appropriate terminology, alignment with my actual methodology, and consistency with syllabus requirements. I edited wording, adjusted claims to reflect my experimental design, and ensured that no AI-generated content misrepresented my data, procedures, or understanding. AI was not used to generate experimental results, fabricate data, conduct statistical testing, or develop the core experimental concept. The research design, data collection, analysis, and scientific reasoning are entirely my own work. I take full responsibility for the final submitted version of this project.
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Acknowledgments I would like to acknowledge the support I received during the development of this project. I am especially grateful to Mrs Kirsten Prior, my research supervisor, for the time and guidance she provided, particularly in assisting with the development of my methodology. Following Mrs Prior’s departure, I would like to thank Mrs Kathy Haigh for taking on the role of supervisor and for the support and feedback she provided in refining the scientific report. I would also like to acknowledge Mrs Lucy Pitkin for her assistance in sourcing and helping me understand the operation of the hydroponic systems used in this investigation. In addition, I thank Dr Matthew Hill and Mrs Virginia Ellis for their contributions and support throughout the project. Finally, I would like to acknowledge Dr Alison Gates for her careful reading of this report and the valuable feedback provided to improve the clarity and quality of the experimental design and the final submission.
References Aini, N., Yamika, W. S. D., & Ulum, B. (2025). Effect of Nutrient Concentration, PGPR and AMF on Plant Growth, Yield, and Nutrient Uptake of Hydroponic Lettuce. International Journal of Agriculture and Biology, 21(1). https://doi.org/10.17957/IJAB/15.0879 Australia Hydroponics Market Size, Share & Report 2026-2035. (2025). https://www.expertmarketresearch.com.au/aumasteradminpanelsec ure/reports/australia-hydroponics-market Ciriello, M., Cirillo, V., Formisano, L., El-Nakhel, C., Pannico, A., De Pascale, S., & Rouphael, Y. (2023). Productive, MorphoPhysiological, and Postharvest Performance of Six Basil Types Grown in a Floating Raft System: A Comparative Study. Plants, 12(3), 486. https://doi.org/10.3390/plants12030486 Das, D., Torabi, S., Chapman, P., & Gutjahr, C. (2020). A Flexible, Low-Cost Hydroponic Co-Cultivation System for Studying Arbuscular Mycorrhiza Symbiosis. Frontiers in Plant Science, 11. https://doi.org/10.3389/fpls.2020.00063 Kim, J.-S. (2021). Effects of nutrient solution and artificial light on the growth and physicochemical properties of hydroponically cultivated barley. Journal of Plant Biotechnology, 48(2), 77–85. https://doi.org/10.5010/JPB.2021.48.2.77
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Laevens, G. C. S., Dolson, W. C., Drapeau, M. M., Telhig, S., Ruffell, S. E., Rose, D. M., Glick, B. R., & Stegelmeier, A. A. (2024). The Good, the Bad, and the Fungus: Insights into the Relationship Between Plants, Fungi, and Oomycetes in Hydroponics. Biology, 13(12), 1014. https://doi.org/10.3390/biology13121014 Lee, S., & Lee, J. (2015). Beneficial bacteria and fungi in hydroponic systems: Types and characteristics of hydroponic food production methods. Scientia Horticulturae, 195, 206–215. https://doi.org/10.1016/j.scienta.2015.09.011 McNew, J., Kathi, S., Paudel, P., & Fontanier, C. (2025). Effect of Commercial Mycorrhizae Products on Soilless Media–grown Basil Yield and Nutrient Content in Controlled Environment Conditions. HortTechnology, 35(5), 745–747. https://doi.org/10.21273/HORTTECH05703-25 Mohammadi, K., Khalesro, S., Sohrabi, Y., & Heidari, G. (2011). A Review: Beneficial Effects of the Mycorrhizal Fungi for Plant Growth. Othman, Y. A., Alananbeh, K. M., & Tahat, M. M. (2024). Can Arbuscular Mycorrhizal Fungi Enhance Crop Productivity and Quality in Hydroponics? A Meta-Analysis. Sustainability, 16(9), 3662. https://doi.org/10.3390/su16093662 Rieley, J. O., & Summerfield, R. J. (1972). The use of inert polymers in hydroponic studies. Plant and Soil, 37(1), 183–185. https://doi.org/10.1007/BF01578492 Rog, I., van der Heijden, M. G. A., Bender, F., Boussageon, R., Lambach, A., Schlaeppi, K., Bodenhausen, N., & Lutz, S. (2025). Mycorrhizal inoculation success depends on soil health and crop productivity. FEMS Microbiology Letters, 372. https://doi.org/10.1093/femsle/fnaf031 Saha, S., Monroe, A., & Day, M. R. (2016). Growth, yield, plant quality and nutrition of basil (Ocimum basilicum L.) under soilless agricultural systems. Annals of Agricultural Sciences, 61(2), 181– 186. https://doi.org/10.1016/j.aoas.2016.10.001 Stegelmeier, A. A., Rose, D. M., Joris, B. R., & Glick, B. R. (2022). The Use of PGPB to Promote Plant Hydroponic Growth. Plants, 11(20), 2783. https://doi.org/10.3390/plants11202783 Sublett, W. L., Barickman, T. C., & Sams, C. E. (2018). The Effect of Environment and Nutrients on Hydroponic Lettuce Yield, Quality, and Phytonutrients. Horticulturae, 4(4), 48. https://doi.org/10.3390/horticulturae4040048 Verdoliva, S. G., Gwyn-Jones, D., Detheridge, A., & Robson, P. (2021). Controlled comparisons between soil and hydroponic systems reveal increased water use efficiency and higher lycopene and β-carotene contents in hydroponically grown tomatoes. Scientia Horticulturae, 279, 109896. https://doi.org/10.1016/j.scienta.2021.109896 Walters, K. J., & Currey, C. J. (2015). Hydroponic Greenhouse Basil Production: Comparing Systems and Cultivars. https://doi.org/10.21273/HORTTECH.25.5.645
Scientific Research in School Volume 8 Issue 1 2026
Cave Drop Rates as a Model of groundwater recharge and relationships with weather patterns Sophia Bock Barker College Groundwater recharge is difficult to measure directly, particularly in karst environments where water moves rapidly through underground fractures and conduits. This investigation examined how rainfall, antecedent wetness and weather systems influence recharge within the Mole Creek karst system, Tasmania, using cave drip logger data from Australia’s National Groundwater Recharge Observing System (NGROS). Fifteen drip loggers recorded hourly drip rates between September 2023 and August 2025, with four representative loggers selected for detailed analysis. Recharge events were compared with rainfall totals, soil moisture and synoptic weather patterns using Spearman’s rank correlations, chi-squared tests and seasonal analyses. Twenty-four recharge events were identified, occurring predominantly during winter and spring. Recharge magnitude showed strong positive relationships with 168-hour rainfall (ρ = 0.682, p = 0.0002) and soil moisture (ρ = 0.572, p = 0.0028), but not with 48-hour rainfall (ρ = 0.127, p = 0.547). These findings indicate that recharge is primarily controlled by antecedent wetness and threshold behaviour rather than shortterm rainfall alone. The results suggest that remotely sensed soil moisture and rainfall data may enable recharge prediction in ungauged karst catchments.
Literature Review Groundwater recharge is difficult to measure directly, particularly in karst environments where water moves rapidly through underground fractures and conduits. Groundwater recharge is one of the most uncertain components of the water balance, yet it is critical for sustainable water management across Australia (Boas & Mallants, 2022; Priestley et al., 2025). Groundwater supports ecosystems, agriculture and regional communities, particularly during dry periods, making accurate understanding of recharge processes increasingly important, and can reveal both past and future climactic shifts (Benz et al., 2024; GouldWhaley et al., 2025). However, groundwater recharge is difficult to directly measure because much of the process occurs underground and can vary greatly between environments, and even between nearby locations. Traditional methods to estimate recharge commonly rely on measurements of groundwater levels, tracers or hydrological modelling (Ali & Mubarak, 2017; Scanlon et al., 2002). While these approaches can estimate long-term average recharge, they often lack the detailed timing information required to identify individual recharge events or determine the exact climatic conditions that trigger recharge (Rau et al., 2015). This makes it difficult to understand how recharge responds to short-term weather variability, particularly in complex environments such as karst systems.
Karst landscapes form when soluble rocks, such as limestone, dissolve, producing caves, sinkholes and underground drainage networks (Dreybrodt, 1989). Unlike many other groundwater systems, water movement in karst systems can occur rapidly through fractures and conduits rather than slowly filtering through soil and rock. Such recharge behaviour can be highly variable and strongly dependent on rainfall intensity, soil moisture and underground flow pathways (Berglund et al., 2019; Somaratne, 2014). Recent work through Australia’s National Groundwater Recharge Observing System (NGROS), led by researchers at UNSW Sydney and partner institutions, has significantly improved understanding of recharge processes. Drip loggers installed underground between the soil surface and the water table can directly record event-scale recharge (Baker et al., 2020, 2021, 2024). This technology measures water that has moved beyond the root zone and may contribute to groundwater replenishment. Recharge across southeastern Australia is episodic rather than continuous and strongly controlled by rainfall magnitude and season (Boas & Mallants, 2022; Cuthbert et al., 2014). Median rainfall thresholds of approximately 10–20 mm over 48 hours are commonly required to activate recharge, although thresholds vary seasonally (Baker et al., 2024; Priestley et al., 2025). Most of Tasmania’s large rainfall events are associated with cold fronts and low-
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Scientific Research in School Volume 8 Issue 1 2026 pressure systems embedded within the Southern Ocean westerly circulation (Mandal et al., 2026; Pook et al., 2006). These synoptic systems frequently generate prolonged and widespread rainfall, particularly during winter, increasing soil moisture and promoting groundwater recharge. Consequently, recharge events are expected to be more common during periods dominated by frontal systems, highlighting the importance of considering not only rainfall magnitude but also the broader atmospheric conditions that generate rainfall. During summer and dry periods, higher evapotranspiration and vegetation water uptake can reduce infiltration even during larger rainfall events (Zhang et al., 2023). In wetter periods, recharge can occur with smaller rainfall amounts because soil and epikarst storage are close to saturation (Baker et al., 2021). This behaviour is often described as a threshold response system, where little change occurs until a critical level is exceeded, after which the system responds rapidly. Within karst systems, recharge may remain minimal during moderate rainfall events until sufficient saturation connects underground flow pathways (Berthelin et al., 2023).
Scientific Research Question How do rainfall, weather systems, antecedent wetness and soil moisture influence recharge events within a karst cave system?
Scientific Hypothesis Recharge events within the karst cave system will be more strongly influenced by antecedent rainfall and soil moisture than by short-term rainfall alone. Recharge will occur most frequently during winter and early spring when soil moisture levels are highest and soil above the cave system is closer to saturation, and most often after cold fronts.
Methodology This investigation used cave drip logger data collected from the Mole Creek karst system in northern Tasmania, Australia, as part of Australia’s National Groundwater Recharge Observing System (NGROS). Mole Creek lies within a temperate forest biome (41°35'S, 146°17'E; Figure 1) (Google, 2026).
Previous research has also shown that different drip sites within the same cave can respond differently to rainfall events due to varying underground flow pathways and storage processes (White, 2006). Some drip sites respond rapidly through fracture flow, while others display delayed responses linked to slower storage flow. These variations can reflect differences in cave depth, rock permeability and underground routing and are a vital component in understanding groundwater recharge and response across broader zones. Hence, multiple monitoring sites are used within cave systems to capture the spatial variability of recharge processes and provide a more representative understanding of subsurface hydrology. This approach reduces the influence of localised anomalies and improves the reliability of recharge estimates across larger catchments. Although previous studies have identified rainfall thresholds across southeastern Australia, temperate environments such as Mole Creek, Tasmania remain less represented in existing literature. Additionally, limited research has directly linked recharge activation to synoptic-scale weather systems such as cold fronts, low pressure systems and troughs. This investigation therefore aims to improve understanding of how rainfall, soil moisture and broader weather patterns influence recharge behaviour within the Mole Creek karst system.
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Figure 1: Map of Mole Creek Site, with insert showing location of King Solomons Cave and Marakoopa Cave (After: Google, 2026)
Mean annual precipitation is approximately 1100 mm and mean annual temperature is 10°C, based on Somer Hill (Station 91150), the closest weather station to the study area. Data were accessed through the National
Scientific Research in School Volume 8 Issue 1 2026 Groundwater Recharge Observing System (NGROS) database (National Groundwater Recharge Observing System, 2026). Fifteen drip sensors installed within King Solomons Cave and Marakoopa Cave recorded hourly drip counts between 12 September 2023 and 13 August 2025 (the most recent data download date). The loggers were positioned at varying depths and locations to capture differences in subsurface water movement and recharge behaviour (Figure 2). Figure 3: Comparison of drip rate records for retained and discarded drip sensors, shown against root zone soil moisture for comparison. Grey and white shading indicates alternating seasons (spring, summer, etc.)
Figure 2: Site schematic. Soil moisture data from weather stations and satellites were used to calculate the water balance using AWRA-L (Source: Frost et al., 2026)
The first stage of the investigation involved collating and cleaning the logger dataset. Time-series data from all sensors were examined to assess data continuity, reliability and responsiveness to rainfall events. Using these criteria, four loggers were selected for detailed analysis, two from each cave. Logger selection was based on record continuity, clarity of response to rainfall events and consistency with recharge behaviour observed in previous NGROS studies (Figure 3). Loggers displaying limited responses, substantial delays between rainfall and drip-rate increases, equipment malfunctions or highly inconsistent behaviour, were deemed unsuitable for identifying recharge events. The selected loggers provided the most reliable and representative records across a range of hydrological conditions. Meteorological data, including rainfall totals, root zone soil moisture and synoptic weather systems, were obtained from Bureau of Meteorology records and related government datasets (Bureau of Meteorology, 2026a, 2026b). Rainfall was calculated over two periods: (1) 48-hour totals, representing short-term rainfall inputs, and (2) 168-hour totals, representing antecedent rainfall conditions. Synoptic weather systems associated with recharge events were classified as cold fronts, low-pressure systems, troughs, warm fronts or occluded fronts.
Recharge events were identified through time-series analysis of drip logger responses. To enable comparison between loggers with different baseline drip rates, recharge magnitudes were normalised and combined to produce a normalised average recharge value. Recharge events were subsequently grouped into Summer (December–February), Autumn (March–May), Winter (June–August) and Spring (September–November). Descriptive statistics, including minimum, maximum, mean and median rainfall totals associated with recharge events, were calculated to estimate recharge thresholds. Inferential statistical analyses were performed to quantify relationships between groundwater recharge and potential predictive variables, including rainfall, antecedent wetness and season. Spearman’s rank correlation coefficient was used to assess relationships between normalised recharge magnitude and 48-hour rainfall, 168-hour rainfall and soil moisture. Spearman’s correlation was selected because recharge data were non-normally distributed and the relationships were not expected to be strictly linear. Seasonal differences in recharge were assessed using a Kruskal–Wallis test applied to the complete monitoring record. This non-parametric test was selected because recharge data were not normally distributed and sample sizes differed between seasons. A chi-squared test of independence was used to determine whether recharge occurrence was associated with rainfall exceeding a selected 48-hour threshold. This test compares observed frequencies with those expected if recharge and rainfall threshold exceedance were independent. A significant result would indicate that recharge occurs disproportionately during high-rainfall periods, whereas a non-significant result would suggest that rainfall threshold
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Scientific Research in School Volume 8 Issue 1 2026 exceedance alone is insufficient to explain recharge occurrence.
Results A total of 24 recharge events were identified during the monitoring period. Recharge events were not evenly distributed throughout the year, with sixteen occurring during winter, six during spring, two during summer and none during autumn (Figure 4).
Figure 4: Count of recharge events per month and
Recharge events were most associated with cold fronts, troughs and low-pressure systems. Only one recharge event was associated with a warm front and one with an occluded front, both during high soil moisture periods (Figure 5).
Figure 5: Relationship between normalised average drips per 48-hour period and root zone soil moisture (%), coloured to show type of weather event associated with rainfall.
Rainfall associated with recharge events varied substantially. The average 48-hour rainfall total associated with recharge was 31.4 mm, with a median of 30 mm, minimum of 11 mm and maximum of 64.4 mm. Root zone soil moisture during recharge events ranged from 58% to 94%, with an average of 82%, and 71% of events occurring with soil moisture ≥80%. Recharge generally occurred during periods of elevated antecedent wetness. For example, a recharge event recorded on 24 August 2024 occurred when soil moisture was 92%, although only 15.5 mm of rainfall fell over the preceding 48 hours, following several previous recharge events (Figure 6). Spearman's rank correlation identified a very strong positive relationship between recharge magnitude and 168-hour rainfall totals (ρ = 0.682, p = 0.0002) and a strong relationship between recharge magnitude and soil moisture (ρ = 0.572, p = 0.0028) (Figure 7). In contrast, recharge magnitude displayed no significant relationship with 48-hour rainfall totals (ρ = 0.127, p = 0.547). These results indicate that recharge was more strongly associated with antecedent wetness conditions than short-term rainfall events (Figure 7). The soil moisture data suggested a minimum observed value of approximately 58% for recharge to occur; however, most recharge events occurred when soil moisture exceeded 80%, indicating that recharge probability increases substantially as soil moisture approaches saturation (Figure 6). Analysis of the complete monitoring record identified significant differences in recharge distributions between seasons (Kruskal-Wallis H = 367.08, p < 0.001). Winter exhibited the highest recharge values, followed by spring, while autumn showed no activity.
Figure 6: Root zone soil moisture (%) and normalised average drip rates per 48-hour period for the four drip sensors, by date. Grey and white shading indicates alternating seasons (spring, summer, etc)
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Figure 6: Relationship between normalised average drip rates per 48-hour period and a) root zone soil moisture; b) precipitation during the previous 48 hours and c) precipitation during the previous 168 hours. In panel (a) the vertical lines represent the thresholds for minimum soil moisture for a recharge event to occur (58%) and the threshold above which recharge become much more likely (80%). In panel (c) the significant linear relationship between normalised drip rates and precipitation over the previous 168 hours is shown. The grey background dots represent the other 48hr drip rates, not identified as recharge events.
The chi-squared analysis performed on recharge events did not identify a significant association between recharge occurrence and rainfall exceeding 30 mm over 48 hours (χ² = 0.017, p = 0.895). The high p-value indicates that recharge occurrence was statistically independent of whether rainfall exceeded the 30 mm threshold, the median rainfall that triggered recharge, suggesting that short-term rainfall alone was not a reliable predictor of recharge. Additionally, grouping events into wet (Winter and Spring) and dry (Summer and Autumn) seasons further supports the importance of seasonality. In the wet season, 13 of 21 periods with more than 30 mm of rainfall over 48 hours produced recharge, whereas in the dry season only 3 of 6 comparable periods produced recharge.
Discussion Controls on recharge at Mole Creek The results indicate that recharge within the Mole Creek karst system is controlled primarily by antecedent wetness conditions rather than short-term rainfall alone. Rainfall accumulated over previous days, and existing soil moisture exerted a much stronger influence on recharge activation than individual rainfall events. The strongest relationship identified in this study was between recharge magnitude and 168-hour rainfall totals. In comparison, the relationship between recharge and 48-hour rainfall was not statistically significant. These results suggest that recharge within the Mole Creek system is not simply a direct response to individual storms; rainfall over the previous week
was a much better predictor of recharge than rainfall immediately before an event (Figure 7). The chisquared analysis provided further evidence that recharge cannot be explained by a simple rainfall threshold alone. The test examined whether recharge occurred more frequently than expected during periods with greater than 30 mm of rainfall over 48 hours. Because no significant association was detected, recharge occurrence appeared largely independent of this rainfall threshold. This suggests that recharge thresholds are dynamic rather than fixed, varying according to the hydrological state of the catchment. The importance of antecedent wetness is further supported by the significant relationship observed between recharge and soil moisture. As soil moisture increased, recharge became more likely and recharge magnitudes generally increased (Figure 7). This suggests that soil moisture acts as a control on infiltration efficiency, with recharge occurring more readily once soil and epikarst storage zones approach saturation. The concentration of recharge events above approximately 80% soil moisture indicates that recharge probability increases substantially as catchment wetness increases. Collectively, the correlation analyses and observed recharge patterns suggest that the Mole Creek system exhibits threshold behaviour (Figure 7). In thresholdcontrolled systems, hydrological responses remain limited until a critical level of storage is exceeded, after which recharge increases rapidly.
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Scientific Research in School Volume 8 Issue 1 2026 Recharge was also strongly influenced by seasonal conditions. Analysis of the complete monitoring record identified highly significant differences in recharge distributions between seasons. Winter exhibited the highest recharge values, followed by spring, whereas autumn displayed very little recharge activity (Figure 4). This pattern is consistent with the expectation that lower evapotranspiration rates and progressively increasing soil moisture during winter enhance the likelihood of recharge. During summer and autumn, a larger proportion of rainfall is lost through evapotranspiration and soil moisture replenishment before reaching deeper subsurface pathways and reaching the necessary threshold for recharge is unlikely. Further evidence for seasonal controls was observed when rainfall events exceeding 30 mm over 48 hours were grouped into wet and dry seasons. In the wet season, 13 of 21 high-rainfall periods produced recharge, compared with only 3 of 6 in the dry season. Weather-system analysis provided additional context for understanding recharge behaviour. Most recharge events were associated with cold fronts, troughs and low-pressure systems (Figure 5). The only recharge events associated with warm-front and occluded-front systems occurred following prolonged wet periods and unusually high soil moisture. This suggests that weather systems influence recharge indirectly by shaping catchment wetness rather than through weather-system type alone. Variation between individual drip loggers provides evidence that the Mole Creek cave system contains multiple flow pathways and storage mechanisms (Figure 6). Some drip sites responded rapidly to rainfall, whereas others showed little relationship with short-term rainfall inputs. Similar variability has been reported in previous karst hydrology studies and reflects differences in fracture connectivity, storage capacity and water residence times within the subsurface (Baker et al., 2024). Comparison with NGROS and broader research These findings are highly consistent with previous research undertaken through Australia's National Groundwater Recharge Observing System (NGROS). Baker et al. (2024) and Kumar et al. (2026) found that recharge across southeastern Australia is often episodic and controlled by antecedent moisture conditions rather than rainfall magnitude alone. Similarly, Cuthbert et al. (2014b) demonstrated that recharge thresholds vary depending on previous rainfall and soil moisture conditions. The hydrological processes operating at Mole Creek are comparable to those identified at other recharge-monitoring sites
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across Australia, as shown by the strong effect of 168hour rainfall on recharge. As rainfall and soil moisture can be monitored remotely using satellite observations, these findings suggest the potential for predicting recharge occurrence across broader regions where direct groundwater measurements are unavailable. The strong winter-spring recharge signal observed at Mole Creek is also consistent with previous Australian studies of seasonal recharge (Baker et al., 2024; Boas & Mallants, 2022). Comparison with previous NGROS studies indicates that the Mole Creek system shares many characteristics with recharge sites elsewhere in southeastern Australia. The significant winter-spring recharge pattern observed at Mole Creek is also consistent with studies from mainland Australia where recharge is concentrated during periods of lower evapotranspiration and higher soil moisture. However, Mole Creek differs from many mainland monitoring sites with its cooler and wetter climate. Higher annual rainfall and lower evaporative demand may allow recharge to occur more frequently and under lower rainfall thresholds than in drier environments. This may explain why some recharge events occurred following relatively modest rainfall totals when antecedent wetness conditions were already high. These findings suggest that while the recharge-threshold framework identified by NGROS studies is broadly applicable, local climate and hydrological conditions influence the magnitude and timing of recharge responses (Kumar et al., 2026). Broader applications and prediction An important implication of these findings is the potential to predict groundwater recharge in ungauged karst catchments using remotely sensed environmental data. Root-zone soil moisture products derived from satellites and gridded rainfall datasets are increasingly available at regional and continental scales, allowing catchment wetness to be monitored without direct groundwater measurements. Because recharge at Mole Creek was most strongly associated with antecedent rainfall and soil moisture, these variables may provide a practical basis for predicting recharge occurrence in locations where cave monitoring is unavailable. These variables jointly suggest a simple conceptual model for recharge prediction: the probability of recharge increases when soil moisture exceeds a critical threshold and sufficient rainfall has accumulated over the preceding week. In the Mole Creek system, this threshold appears to occur at around 80% soil moisture, while
Scientific Research in School Volume 8 Issue 1 2026 the specific rainfall threshold remains less clearly defined. While this study did not develop a predictive model, a two-variable threshold or logistic model incorporating soil moisture and antecedent rainfall provides a testable hypothesis for future research. The broader applicability of such a model depends on whether similar threshold dynamics operate in other karst systems. Previous NGROS studies suggest that antecedent wetness controls recharge across a range of Australian environments (Baker et al., 2024), indicating that the overall framework may be transferable. However, the specific thresholds identified in this study, including the approximate 80% soil moisture threshold and the importance of 168-hour rainfall accumulation, are likely to be sitespecific and influenced by factors such as soil properties, epikarst storage capacity, fracture connectivity and local climate. Thus, while the conceptual framework may generalise across karst systems, the numerical parameters would likely require local calibration. These findings have practical implications for groundwater management. Karst aquifers provide important water resources for ecosystems, agriculture and human consumption, yet recharge is notoriously difficult to monitor directly due to the complexity of subsurface flow pathways. A remote-sensing-based recharge indicator could help water managers anticipate recharge periods, assess vulnerability to drought and improve groundwater management in regions where long-term monitoring infrastructure is unavailable. Limitations and future work Several limitations should be considered when interpreting these findings. The study covered less than two years of observations, limiting the number of recharge events available for analysis and reducing the ability to assess long-term climatic variability. Recharge responses were derived from a small selection of loggers, and soil moisture measurements represented conditions at a single depth. Several environmental variables that may influence recharge, including evapotranspiration, fracture connectivity, subsurface storage characteristics and recharge lag times, were not included in this investigation. Future research could address these limitations by extending monitoring over longer periods, assessing drip-water chemistry, water stable isotopes analyses and incorporating additional environmental variables such as evapotranspiration. Investigation of lag times between rainfall and recharge could provide insight into underground flow pathways and storage.
Conclusion The results support the scientific hypothesis and demonstrate that recharge in temperate karst environments is governed by interactions between rainfall history, soil moisture and subsurface storage processes. Groundwater recharge within the Mole Creek karst system was found to be controlled primarily by antecedent wetness conditions. Recharge magnitude showed significant positive relationships with 168-hour rainfall totals and soil moisture, whereas no relationship was observed with 48-hour rainfall. These findings indicate that recharge exhibits threshold behaviour, with soil and epikarst storage requiring replenishment before substantial recharge can occur. Recharge occurred predominantly during winter and spring, when soil moisture was highest and evapotranspiration was low. Most recharge events were associated with cold fronts, troughs and lowpressure systems, although weather systems appeared to influence recharge indirectly through their effects on rainfall accumulation and catchment wetness. The observed concentration of recharge events above approximately 80% soil moisture suggests that remotely sensed soil moisture and rainfall data may provide a practical basis for predicting recharge in ungauged karst systems. Improved understanding of recharge processes is increasingly important for sustainable groundwater management under changing climatic conditions.
Declaration of use of AI Throughout this report I aimed to use AI, primarily ChatGPT, as a tool for feedback, suggesting analysis and finding resources. I began by writing the sections myself and then asked AI for word suggestions, ways to make it flow better and where I could cut words. I also asked it to suggest both scientific papers (which I then read to confirm their relevance) and statistical tests which were related to my research. I also utilised Elicit AI’s find paper tool to assist in finding papers related to the topic. As the various figures were coded in R, I utilised Claude as an assistant. The following are some of the prompts: 20/02/26: Can you please find me a few scientific papers that have been written in the past 5 years, related to this project. [files attached] What word could I put into the blank space to improve cohesion: Graphing these will help identify when the largest drip rates were across sites, and these dates can then be compared with public weather data to [___] correlations.
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Scientific Research in School Volume 8 Issue 1 2026 22/02/26: Can you please look through this research proposal and highlight any grammatical mistakes or places where it doesn't "flow", providing suggestions for alternatives. [my draft at that point was attached] 3/05/26: What are some statistical tests I could use to solidify my results/connections for this project? [files attached] 7/06/26: Could you please suggest some places, specifically in the method and results where I could cut words? [files attached]
Acknowledgements I would like to extend my sincere gratitude to Professor Andy Baker from UNSW Sydney for his guidance throughout this project, including assistance in developing the research focus and providing access to the NGROS database. I would also like to thank Professor Wendy Timms for downloading and supplying the logger data, and the National Parks and Wildlife Service for supporting hydrological monitoring at the study site. Additionally, I am grateful to Mrs Kathy Haigh, Dr Matthew Hill and Mrs Virginia Ellis for their ongoing guidance and encouragement throughout this investigation; their advice and assistance have been invaluable.
References Ali, M., & Mubarak, S. (2017). Approaches and methods of quantifying natural groundwater recharge – a review. Asian Journal of Environment & Ecology, 5(1), 1–27. https://doi.org/10.9734/AJEE/2017/36987 Baker, A., Berthelin, R., Cuthbert, M. O., Treble, P. C., Hartmann, A., & Kss Cave Studies Team, T. (2020). Rainfall recharge thresholds in a subtropical climate determined using a regional cave drip water monitoring network. Journal of Hydrology, 587, 125001. https://doi.org/10.1016/j.jhydrol.2020.125001 Baker, A., Scheller, M., Oriani, F., Mariethoz, G., Hartmann, A., Wang, Z., & Cuthbert, M. O. (2021). Quantifying temporal variability and spatial heterogeneity in rainfall recharge thresholds in a montane karst environment. Journal of Hydrology, 594, 125965. https://doi.org/10.1016/j.jhydrol.2021.125965 Baker, A., Shanafield, M., Timms, W., Andersen, M. S., Priestley, S., & Melo Zurita, M. (2024). An underground drip water monitoring network to characterize rainfall recharge of groundwater at different geologies, environments, and climates across Australia. Geoscientific Instrumentation, Methods and Data Systems, 13(1), 117– 129. https://doi.org/10.5194/gi-13-117-2024 Benz, S. A., Irvine, D. J., Rau, G. C., Bayer, P., Menberg, K., Blum, P., Jamieson, R. C., Griebler, C., & Kurylyk, B. L. (2024). Global groundwater warming due to climate change. Nature Geoscience, 17(6), 545–551. https://doi.org/10.1038/s41561-024-01453-x
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Berglund, J. L., Toran, L., & Herman, E. K. (2019). Deducing flow path mixing by storm-induced bulk chemistry and REE variations in two karst springs: With trends like these who needs anomalies? Journal of Hydrology, 571, 349–364. https://doi.org/10.1016/j.jhydrol.2019.01.050 Berthelin, R., Olarinoye, T., Rinderer, M., Mudarra, M., Demand, D., Scheller, M., & Hartmann, A. (2023). Estimating karst groundwater recharge from soil moisture observations – a new method tested at the Swabian Alb, southwest Germany. Hydrology and Earth System Sciences, 27(2), 385–400. https://doi.org/10.5194/hess-27-385-2023 Boas, T., & Mallants, D. (2022). Episodic extreme rainfall events drive groundwater recharge in arid zone environments of central Australia. Journal of Hydrology: Regional Studies, 40, 101005. https://doi.org/10.1016/j.ejrh.2022.101005 Bureau of Meteorology. (2026a). AWO Soil Moisture [Dataset]. https://awo.bom.gov.au/products/historical/soilMoisturerootZone/8,-41.558,147.222/pt,-41.558,146.389/a/d/202304-08 Bureau of Meteorology. (2026b). Climate Data Online [Dataset]. https://www.bom.gov.au/climate/data/index.shtml Cuthbert, M. O., Rau, G. C., Andersen, M. S., Roshan, H., Rutlidge, H., Marjo, C. E., Markowska, M., Jex, C. N., Graham, P. W., Mariethoz, G., Acworth, R. I., & Baker, A. (2014). Evaporative cooling of speleothem drip water. Scientific Reports, 4(1), 5162. https://doi.org/10.1038/srep05162 Dreybrodt, W. (1989). Processes in Karst Systems: Physics, Chemistry, and Geology (Vol. 26). https://doi.org/10.5860/CHOICE.26-4499 Frost, A., Ramchurn, A., & Smith, A. (2026). The Bureau’s Operational AWRA Landscape (AWRA-L) Model. https://awo.bom.gov.au/assets/notes/publications/Frost__M odel_Description_Report.pdf Google. (2026). Mole Creek, https://earth.google.com/web/
Tasmania
[Map].
Gould-Whaley, C. N., Drysdale, R. N., Treble, P. C., May, J.-H., Priestley, S. C., Hellstrom, J. C., Vardanega, C. R., & Buswell, C. C. (2025). Subaqueous speleothems as archives of groundwater recharge on Australia’s southern arid margin. Climate of the Past, 21(5), 857–876. https://doi.org/10.5194/cp-21-857-2025 Kumar, A., Baker, A., Andersen, M. S., Timms, W., Fisher, A. G., & Sun, D. (2026). Investigating correlative & causal drivers of precipitation-driven groundwater recharge occurrences in southeastern Australia. Journal of Hydrology: Regional Studies, 66, 103547. https://doi.org/10.1016/j.ejrh.2026.103547 Mandal, M., Pepler, A., Spence, P., & Holbrook, N. J. (2026). Contributions from different weather systems to rainfall in Tasmania, Australia. Journal of Southern Hemisphere Earth Systems Science, 76(1), ES25026. https://doi.org/10.1071/ES25026 National Groundwater Recharge Observing System. (2026). NGROS database [Dataset]. https://groundwater.unsw.edu.au/
Scientific Research in School Volume 8 Issue 1 2026 Pook, M. J., McIntosh, P. C., & Meyers, G. A. (2006). The Synoptic Decomposition of Cool-Season Rainfall in the Southeastern Australian Cropping Region. https://doi.org/10.1175/JAM2394.1 Priestley, S. C., Baker, A., Shanafield, M., Timms, W., Andersen, M. S., & de Lourdes Melo Zurita, M. (2025). Groundwater recharge of fractured rock aquifers in SE Australia is episodic and controlled by season and rainfall amount. Geophysical Research Letters, 52(5), e2024GL113503. https://doi.org/10.1029/2024GL113503 Rau, G. C., Cuthbert, M. O., Andersen, M. S., Baker, A., Rutlidge, H., Markowska, M., Roshan, H., Marjo, C. E., Graham, P. W., & Acworth, R. I. (2015). Controls on cave drip water temperature and implications for speleothembased paleoclimate reconstructions. Quaternary Science Reviews, Novel Approaches to and New Insights from Speleothem-Based Climate Reconstructions, 127, 19–36. https://doi.org/10.1016/j.quascirev.2015.03.026
Scanlon, Healy, & Cook. (2002). Choosing appropriate techniques for quantifying groundwater recharge. Hydrogeology Journal, (10), 18–39. Somaratne, N. (2014). Characteristics of point recharge in karst aquifers. Water, 6(9), 2782–2807. https://doi.org/10.3390/w6092782 White, W. B. (2006). Fifty years of karst hydrology and hydrogeology: 1953–2003. In R. S. Harmon & C. M. Wicks (Eds.), Perspectives on Karst Geomorphology, Hydrology, and Geochemistry—A Tribute Volume to Derek C. Ford and William B. White (p. 0). Geological Society of America. https://doi.org/10.1130/2006.2404(12) Zhang, H., Zhou, Z., Dong, H., Yan, L., Ding, S., Huang, J., Gong, X., & Su, D. (2023). Seasonal variations of cave dripwater hydrogeochemical parameters and δ13CDIC in the subtropical monsoon region and links to regional hydroclimate. Science of The Total Environment, 881, 163509. https://doi.org/10.1016/j.scitotenv.2023.163509
Appendix Collection of factors influencing drip rates and subsequent recharge events Date
Loggers Drip Rate & Recharge 6 8 13
15
Avg
24/10/23 16/12/23 18/1/24 29/6/24 12/7/24 22/7/24 27/7/24 17/8/24 21/8/24 24/8/24 29/8/24 31/8/24 2/9/24 6/9/24 6/10/24 19/10/24 30/11/24 3/12/24 17/6/25 24/6/25 10/7/25 14/7/25 22/7/25 26/7/25
451 75 469 4 5 5 71 200 509 453 570 10324 37185 17688 1616 488 7310 7116 1 5 0 1 128 92
244 243 10279 12 13 155 474 437 4209 8080 74383 59 9790 85 5 0 11347 4 0 0 0 0 0 1
97 0 56 36864 35772 27154 39956 32448 37045 50009 66173 53015 48119 62673 6289 874 84594 40260 5019 22642 54880 19471 110321 103653
1934 4594 8199 12970 5575 2475 4113 2925 2882 5840 19299 10961 17759 12969 3073 5796 40593 10834 430 590 7865 2407 80372 27654
5/8/25
2
0
25392
2885
Summer (DJF)
Autumn (MAM)
682 1228 4748 12463 10341 7447 11154 9003 11161 16096 40106 18589 28213 23354 2746 1789 35961 14454 1363 5809 15686 5470 47705 32850
Norm. Avg 0.009 0.016 0.063 0.124 0.098 0.070 0.105 0.085 0.110 0.161 0.463 0.210 0.396 0.277 0.032 0.023 0.395 0.163 0.013 0.053 0.149 0.030 0.501 0.321
48hr Rainfall Total 38.8 53.4 57.4 47.2 14.4 12.4 36.8 43.4 17.4 15.5 25.8 33.6 33 47.2 27.4 22.8 64.4 29.8 26.4 15.2 42.2 13.6 39.2 11
168hr Rainfall Total 39.1 53.4 61.4 80.6 34.6 37.6 55.4 56.6 63.6 35.8 58.4 85.6 103.4 98.4 35 22.8 66.7 100.5 27 19.3 43.4 56.8 69.2 59.8
7070
0.067
16.2
16.8
Winter (JJA)
Weather Event LP, CF T, CF LP, T LP, CF T, CF CF T, CF LP, T T, CF T, WF CF CF CF CF CF, LP CF LP, T LP, T LP, CF CF, T CF, T CF, T CF, LP LP, CF, O CF, T
Soil Moisture. % 72 58 63 81 83 88 89 89 90 92 91 92 94 92 88 88 75 84 62 63 76 81 82 90 88
Spring (SON)
CF: Cold Front, WF: Warm Front, LP: Low Pressure, HP: High Pressure, T: Trough, O: Occluded
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Comparative Phytoremediation Efficacy of Six Native Australian Wetland Macrophyte Species in Removing Nitrate-Nitrogen and Phosphate-Phosphorus from Simulated Piggery Effluent Huw Lawrence Barker College This investigation compared six native Australian wetland macrophyte species (Carex appressa, Juncus usitatus, Baumea juncea, Ficinia nodosa, Gahnia sp., and Lomandra longifolia) for their ability to remove nitrate-nitrogen and phosphate-phosphorus from synthetic piggery effluent (2175 mg/L nitrate-N, 850 mg/L phosphate-P) over 14 days, using three replicates per species plus three sand-only controls. By Day 14, all six species removed over 95% of nitrate-N and over 85% of phosphate-P. C. appressa removed the most of both nutrients (98.3% nitrate-N, 90.8% phosphateP), recorded the highest health score (4.5/5), and grew the most (+2.3 cm). Gahnia sp. removed the least of both nutrients (95.5% nitrate-N, 85.8% phosphate-P), scored lowest for health (1.0/5), and lost 4.4 cm in height. One-way ANOVA found a significant difference between species for both nitrate-N removal (p < 0.001) and phosphate-P removal (p = 0.028).
Literature Review Agricultural runoff such as rainwater carrying nitrogen and phosphorus from animal waste, fertilisers, and feed residues off farmland is a major driver of waterway eutrophication in Australia (Smith et al., 1999). Excess nutrients entering freshwater systems trigger algal and cyanobacterial blooms, which deplete dissolved oxygen as they decompose, killing fish and invertebrates and, in severe cases, producing toxins that contaminate drinking water (Paerl & Otten, 2013). Intensive piggeries are a particularly significant source: piggery wastewater can carry nitrogen and phosphorus concentrations orders of magnitude higher than typical urban stormwater, and conventional chemical or mechanical treatment of this effluent is often too costly for smaller operations (Tucker, 2015). This has driven interest in phytoremediation which is using living plants to remove or transform pollutants from water as a low cost, nature based alternative. Phytoremediation of nutrient rich water operates through several linked mechanisms. Macrophytes take up dissolved nitrogen and phosphorus through their roots for growth, directly converting the pollutant into plant biomass (Vymazal, 2014). Roots also host biofilms of nitrifying and denitrifying bacteria that convert nitrate into gaseous nitrogen (Vymazal, 2007). Many wetland macrophytes possess internal air channels that allow radial oxygen loss from roots into the substrate (aerenchyma) creating aerobic
microzones around the root that support nitrification, alongside anaerobic zones further away that support denitrification (Colmer, 2003). The balance of root uptake, microbial transformation, and substrate filtration determines a species overall removal efficiency, and varies with root architecture, growth rate, and stress tolerance which is why species selection is central to constructed wetland design guidelines (FAWB, 2009; AGSBS, n.d.; Kadlec & Wallace, 2009). Constructed wetlands that exploit these mechanisms have been widely adopted internationally for treating agricultural, industrial, and municipal wastewater with species that combine extensive root systems, rapid biomass accumulation, and tolerance of high nutrient and salinity stress generally favoured in system design (Kadlec & Wallace, 2009). The six species selected for this investigation C. appressa, J. usitatus, B. juncea, F. nodosa, Gahnia sp., and L. longifolia represent a deliberately varied set of growth forms and root architectures within this design framework. Clump forming sedges with dense fibrous roots (C. appressa, B. juncea, F. nodosa), rush like species with cylindrical, photosynthetic stems (J. usitatus), tussock forming sedges with coarser root systems (Gahnia sp.), and strap leaved species with rhizomatous root mats (L. longifolia). All six are commonly recommended in Australian biofiltration and constructed wetland guidelines (FAWB, 2009; AGSBS, n.d.), but their performance under high
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strength piggery effluent has not previously been compared. Minkus (2025) tested C. appressa a robust, clump forming sedge native to south eastern Australian wetlands across a range of synthetic piggery effluent concentrations, and found consistently high nitratenitrogen and phosphate-phosphorus removal efficiencies even at undiluted effluent strength, attributing this to the species dense fibrous root mass and nutrient load tolerance. However, Minkus (2025) tested only one species, leaving open how C. appressa compares to other macrophytes recommended for biofiltration. This investigation extends Minkus (2025) by holding effluent concentration constant at undiluted piggery strength (2175 mg/L nitrate-nitrogen, 850 mg/L phosphate-phosphorus) while testing six native species in parallel C. appressa, J. usitatus, B. juncea, F. nodosa, Gahnia sp., and L. longifolia. Comparing removal efficiency, growth, and visual health across these species under identical high-concentration conditions aims to identify which are best suited to remediate high strength agricultural runoff, informing constructed wetland design for piggery effluent treatment. As the species selected differ considerably in root architecture, growth form, and taxonomic family, this comparison should indicate not only which individual species perform best, but also whether particular root or growth traits are associated with stronger nutrient removal under high strength effluent conditions, providing a more general basis for species selection than a single species study can offer.
Scientific Research Question To what extent does species selection (C. appressa, J. usitatus, B. juncea, F. nodosa, Gahnia sp., and L. longifolia) affect the removal efficiency of nitratenitrogen and phosphate-phosphorus from synthetic agricultural runoff at a concentration representative of undiluted piggery effluent (2175 mg/L nitrate-N, 850 mg/L phosphate-P) over a 14 day period?
Scientific Hypothesis It is hypothesised that species with denser, more fibrous root systems and greater tolerance of high nutrient loading will achieve significantly higher nitrate-nitrogen and phosphate-phosphorus removal efficiencies and will maintain higher visual health scores and greater height gains, than species with sparser root systems or lower tolerance of nutrient stress, over the 14 day exposure period.
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Methodology Eighteen experimental units were established, comprising three replicates each of six native Australian wetland macrophyte species: C. appressa, J. usitatus, B. juncea, F. nodosa, Gahnia sp., and L. longifolia. An additional three units containing only the substrate, with no plant present, served as sandonly controls. All 21 units were filled with a substrate consisting of commercially pre-washed coarse sand. Each unit received synthetic agricultural runoff prepared to a concentration representative of undiluted piggery effluent, containing 2175 mg/L nitratenitrogen and 850 mg/L phosphate-phosphorus. The solution was prepared by adding Black Marvel liquid fertiliser (14.33% nitrogen) and Liquid Potash Plus fertiliser (2.0% phosphorus) to deionised water; for each 2.5 L batch, approximately 38 mL of Black Marvel and 106 mL of Liquid Potash Plus were added to deionised water made up to 2.5 L total volume. Each experimental unit received 100 mL of this solution on Days 1, 3, 5, 7, 9, 11 and 13. Nutrient concentrations in the outflow of each unit were sampled on Days 1, 7 and 13 using PASCO EZ Snap colorimeter kits. Samples for nitrate analysis were diluted at a ratio of 1:100, and samples for phosphate analysis were diluted at a ratio of 1:100, prior to colorimetric measurement, with results converted back to original concentration using the appropriate dilution factor. Plant height (cm), measured from the substrate surface to the tip of the tallest leaf or stem, was recorded for each replicate on Days 2, 4, 6, 8, 10, 12, and 14. Visual health was assessed on Days 1, 7, and 14 using a standardised five point scoring rubric, where a score of 5 indicated excellent health with vigorous growth and fully green tissue and no visible stress, and a score of 0 indicated a dead plant, with intermediate scores corresponding to increasing severity of chlorosis, browning, and biomass loss. Plants were housed within a greenhouse to minimise environmental variation and ensure relatively stable temperature and light conditions. Pots were rotated regularly throughout the experiment to further standardise light exposure among treatments. All measurements and observations were conducted on the same schedule to reduce the influence of confounding variables. Data were analysed using descriptive statistics, one way ANOVA with Tukey HSD post hoc testing, Pearson correlation, linear regression, the Kruskal Wallis H test as a non parametric alternative to ANOVA given the small sample size (n = 3 per group), and independent
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samples t tests comparing the best and worst performing species against the sand-only control. Nutrient removal efficiency (RE%) for each sample was calculated using the formula:
Table 3: Independent samples t-test, C. appressa vs sandonly control, Day 13 nitrate-N RE% (df = 4). Group C. appressa Control
Mean RE (%) 98.3 93.9
t 3.06 —
p 0.038 —
RE% = ((Cin − Cout) / Cin) × 100 where Cin is the initial concentration of the synthetic runoff (2175 mg/L for nitrate-N or 850 mg/L for phosphate-P) and Cout is the measured outflow concentration on the corresponding sampling day
Results Removal efficiency (RE%) for nitrate-nitrogen and phosphate-phosphorus was calculated relative to initial synthetic runoff concentrations of 2175 mg/L and 850 mg/L respectively, using RE% = ((Cin − Cout) / Cin) × 100. Tables 1–12 present mean values across the three replicates per species (n = 3), followed by the corresponding statistical tests. Nitrate-Nitrogen Removal Table 1: Mean nitrate-nitrogen (NO₃-N) outflow concentration (mg/L) and removal efficiency (RE%), Day 1 and Day 13, by species (n = 3). Species C. appressa J. usitatus B. juncea F. nodosa Gahnia sp. L. longifolia Control (sand only)
Day 1 Outflow (mg/L) 28.3 36.3 42.0 41.0 47.0 47.3
Day 13 Day 1 RE Day 13 Outflow (%) RE (%) (mg/L) 36.0 98.7 98.3 46.7 98.3 97.9 61.3 98.1 97.2 50.3 98.1 97.7 97.3 97.8 95.5 58.3 97.8 97.3
120.0
133.3
94.5
93.9
Table 2: One-way ANOVA and Tukey HSD post-hoc test for Day 13 nitrate-N removal efficiency between species A–F (df = 5, 12). Source Between groups Within groups
SS
df
MS
F
p
14.13
5
2.83
39.79
<0.001
0.85
12
0.07
—
—
Tukey HSD (q > 4.75 = significant) C. appressa vs Gahnia sp. J. usitatus vs Gahnia sp. C. appressa vs B. juncea F. nodosa vs Gahnia sp. C. appressa vs L. longifolia Gahnia sp. vs L. longifolia B. juncea vs Gahnia sp. All other pairs
q 18.32 15.14 7.57 14.04 6.67 11.65 10.76 < 4.75
Figure 1: Mean nitratenitrogen outflow concentration (mg/L) by species, Days 1, 7, and 13 (n = 3 per species).
Phosphate-Phosphorous Removal Table 4: Mean phosphate-phosphorus (PO₄-P) outflow concentration (mg/L) and removal efficiency (RE%), Day 1 and Day 13, by species (n = 3). Species C. appressa J. usitatus B. juncea F. nodosa Gahnia sp. L. longifolia Control (sand only)
Day 1 Outflow (mg/L) 57.3 59.7 60.7 83.3 82.3 64.0
Day 13 Day 1 RE Day 13 Outflow (%) RE (%) (mg/L) 78.3 93.3 90.8 83.0 93.0 90.2 88.0 92.9 89.6 94.0 90.2 88.9 120.7 90.3 85.8 91.3 92.5 89.3
166.7
156.7
80.4
81.6
Table 5: One-way ANOVA and Tukey HSD post-hoc test for Day 13 phosphate-P removal efficiency between species A–F (df = 5, 12). Source Between groups Within groups
SS
df
MS
F
p
46.01
5
9.20
3.74
0.028
29.51
12
2.46
—
—
Tukey HSD (q > 4.75 = significant) C. appressa vs Gahnia sp. J. usitatus vs Gahnia sp. All other pairs
q 5.50 4.89 < 4.75
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Figure 2: Mean phosphatephosphorus outflow concentration (mg/L) by species, Days 1, 7, and 13 (n = 3 per species).
Figure 3: Mean plant height (cm) by species, Days 2, 4, 6, 8, 10, 12, and 14 (n = 3 per species). Table 9: One-way ANOVA and Tukey HSD for Day 14 height between species A–F (df = 5, 12).
Table 6: Independent samples t-test, C. appressa vs sandonly control, Day 13 phosphate-P RE% (df = 4). Group C. appressa Control
Mean RE (%) 90.8 81.6
t 3.93 —
p 0.017 —
Source Between groups Within groups
p 0.003
F
p
89.69
5.74
0.006
187.61
12
15.63
—
— q 6.47 5.61 < 4.75
Table 10: Mean visual health score (0–5), Days 1, 7, and 14, and total decline, by species (n = 3).
Table 8: Mean plant height (cm), Day 1 and Day 14, and net change, by species (n = 3). Day 1 Height (cm) 28.3 14.5 24.1 21.6 24.9 16.7
Species C. appressa J. usitatus B. juncea F. nodosa Gahnia sp. L. longifolia
Day 14 Height (cm) 30.6 15.9 26.1 21.2 20.5 17.8
Net Change (cm) +2.3 +1.4 +2.0 −0.4 −4.4 +1.1
Table 9: Mean plant height (cm), Day 1 and Day 14, and net change, by species (n = 3) C. appressa J. usitatus B. juncea F. nodosa Gahnia sp. L. longifolia
MS
5
Visual Health Score
Plant Height
Species
df
Tukey HSD (q > 4.75 = significant) C. appressa vs J. usitatus C. appressa vs L. longifolia All other pairs
Table 7: Pearson correlation between species-mean nitrate-N RE% and phosphate-P RE%, Day 13 (n = 6 species). r 0.955
SS 448.45
Day 1 Day 14 Net Change Height (cm) Height (cm) (cm) 28.3 30.6 +2.3 14.5 15.9 +1.4 24.1 26.1 +2.0 21.6 21.2 −0.4 24.9 20.5 −4.4 16.7 17.8 +1.1
Species C. appressa J. usitatus B. juncea F. nodosa Gahnia sp. L. longifolia
Day 1 (/5) 5.0 5.0 5.0 5.0 5.0 5.0
Day 7 (/5) 4.8 4.3 3.8 3.5 3.0 4.5
Day 14 (/5) 4.5 3.5 2.5 1.5 1.0 4.0
Table 10a: Standardised visual health scoring rubric (0–5) used to assess plant condition on Days 1, 7, and 14. Score (/5)
Rating
5
Excellent
4
Good
3
Moderate
2
Poor
1
Very poor
0
Dead
Description Vigorous growth, fully green tissue, no visible stress. Minor chlorosis affecting less than 25% of tissue. Visible chlorosis affecting 25–50% of tissue. Extensive yellowing and browning affecting 50–75% of tissue. Severe chlorosis, necrosis, and major biomass loss. Plant is dead.
Table 11: Pearson correlation between total health decline (Day 1–14) and net height change, by species (n = 6). r −0.811
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p 0.050
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Overall Species Ranking Table 12: Species ranked by mean removal efficiency (average of Day 13 nitrate-N and phosphate-P RE%), with Day 14 health score and net height change (n = 3). Rank Species 1 2 3 4 5 6
C. appressa J. usitatus B. juncea F. nodosa L. longifolia Gahnia sp.
Mean RE% Day 14 Net (N+P, Day Health Height 13) (/5) (cm) 94.6 4.5 +2.3 94.0 3.5 +1.4 93.4 2.5 +2.0 93.3 1.5 −0.4 93.3 4.0 +1.1 90.7 1.0 −4.4
Discussion The results support the hypothesis. Species made a significant difference to nitrate-N removal (Table 2, p < 0.001), and C. appressa the species with the densest, most fibrous root system among those tested — removed the most of both nutrients (Table 1, Table 4: 98.3% nitrate-N, 90.8% phosphate-P) while also recording the highest health score and the greatest height gain (Table 8, Table 10). This agrees with Minkus (2025), who found C. appressa removed around 90% of nitrate-N and phosphate-P even at undiluted piggery concentrations and is consistent with the idea that dense fibrous roots support more of the uptake and microbial activity that drives nutrient removal (Vymazal, 2007, 2014). Gahnia sp. was the worst performing species. The Tukey HSD comparisons in Table 2 show it removed significantly less nitrate-N than every other species, and Figure 1 shows its removal rate decreasing over the 14 days (slope = 3.86 mg/L/day) while every other species stayed consistent. This tracks with what happened to the plants themselves: all three Gahnia sp. replicates dropped from a health score of 5.0 to an average of 1.0 by Day 14 (Table 10), and Figure 3 shows one replicate's height peaking around Day 7 before significantly decreasing 17 cm to Day 13 (Table 8). With n = 3, that one collapsing replicate has a large effect on the species average, but the fact that all three Gahnia sp. plants declined in health points to the species genuinely struggling under this concentration. The correlation between nitrate-N and phosphate-P removal across species was very strong (Table 7, r = 0.955, p = 0.003), and this is now reflected in the ANOVA results for both nutrients: species made a significant difference to phosphate-P removal (Table 5, p = 0.028) as well as nitrate-N removal (Table 2, p < 0.001). This points to both nutrients being removed by broadly the same mechanism most likely root uptake and the aerenchyma driven oxygen zones (Colmer, 2003), which support both nitrification denitrification
(nitrate-N) and phosphorus binding in the root zone (phosphate-P). As with nitrate-N, the phosphate-P result was driven mainly by Gahnia sp. The Tukey HSD comparisons in Table 5 show Gahnia sp. removed significantly less phosphate-P than C. appressa and J. usitatus, and Figure 2 shows its phosphate-P outflow rising sharply between Day 7 and Day 13 — the same period over which its nitrate-N outflow and height were also worsening (Figures 1 and 3). The remaining five species removed between 88.9% and 90.8% of phosphate-P, a narrower spread than for nitrate-N, but Gahnia sp. fell to 85.8%. The sand-only control still removed less phosphate-P (81.6%) than any planted species including Gahnia sp. (Table 4), and the t-tests in Tables 3 and 6 confirm C. appressa significantly outperformed the control for both nutrients. The negative correlation between health decline and height change (Table 11, r = −0.811, p = 0.050) suggests that species which lost more health also tended to grow less or shrink, though this result was not found to be significantly different. Table 12 shows that C. appressa and Gahnia sp. were consistent best and worst performers across all three measures (removal efficiency, health, and height), but the species in between were less consistent — L. longifolia had the second-best health score but only ranked fifth for combined removal, while B. juncea and F. nodosa ranked above it for removal despite worse health scores. This suggests that, outside of the clear best and worst performers, nutrient removal and visual health/growth do not always track each other closely. The overall ranking of species also aligns with the differences in root architecture outlined in the literature review. C. appressa, which possesses a dense fibrous root system, achieved the highest nutrient removal and maintained the best overall plant health throughout the trial. In contrast, Gahnia sp., characterised by a coarser tussock forming root system, recorded the lowest nutrient removal efficiencies and experienced the greatest decline in health and height. The intermediate performance of J. usitatus, B. juncea, F. nodosa, and L. longifolia further suggests that root structure may play an important role in determining phytoremediation performance. While root biomass was not measured directly in this investigation, the results are consistent with previous research indicating that extensive root systems provide greater surface area for nutrient uptake and support larger microbial communities involved in nitrogen and phosphorus cycling (Vymazal, 2007, 2014).
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Limitations The main limitation was the small sample size (n = 3 per species), which meant that one collapsing Gahnia sp. replicate had a large effect on that species' results. The PASCO colorimeter kits, while practical for a school setting, are also less precise than laboratorygrade equipment, so the removal efficiency figures carry some measurement uncertainty. The 14 day trial period, while matching Minkus (2025), is short — Gahnia sp.'s decline was still accelerating at Day 13 (Figure 1), so it is not clear whether it would have stabilised, recovered, or continued to worsen with more time. Finally, the synthetic runoff used here, while matched to published piggery effluent concentrations, does not include the suspended solids, pathogens, or variable nutrient ratios of real piggery effluent. Implications and Future Research For anyone designing a constructed wetland to treat piggery effluent, these results point to C. appressa as the strongest single choice among the six species tested, consistent with both this investigation and Minkus (2025). J. usitatus and B. juncea also performed well and could be reasonable alternatives or companion species. Gahnia sp. does not look suitable for sustained exposure to undiluted piggery effluent at this concentration and would need testing at lower concentrations before it could be recommended. A longer trial would help confirm whether Gahnia sp.'s decline keeps getting worse or eventually stabilises, and whether the other species' steady performance holds up beyond 14 days. A larger sample size would also reduce how much a single struggling replicate can skew a species' result. Testing mixed species plantings, and eventually real piggery effluent rather than synthetic runoff, would both bring this closer to how a real constructed wetland would work.
Conclusion
that species with dense, fibrous roots and high tolerance of nutrient loading perform best under high strength effluent. Gahnia sp. was the worst performer on every measure, with both its nitrate-N and phosphate-P removal still declining at Day 13, indicating it is poorly suited to this concentration. The very strong link between nitrate-N and phosphateP removal across species (r = 0.955, p = 0.003) suggests a shared underlying mechanism. Overall, C. appressa is recommended for biofiltration of high strength piggery effluent, in line with and extending Minkus (2025), while Gahnia sp. is not recommended without further testing at lower concentrations.
Declaration of use of AI AI was used to assist in drafting this report. Specifically, the AI generated a structured draft of the methodology, results, discussion, and conclusion largely helping with punctuation, grammar and flow of the writing. The AI assisted in performing statistical calculations (descriptive statistics, ANOVA, Tukey HSD, Pearson correlation, linear regression, KruskalWallis H, and independent t-tests). I reviewed all calculations for scientific accuracy.
Acknowledgements Thank you to Mrs Kathy Haigh and Mrs Virginia Ellis for help designing and completing the experimental component of the investigation.
References AGSBS. (n.d.). Vegetation selection for stormwater biofilters [Fact sheet]. Facility for Advancing Water Biofiltration, Monash University. https://watersensitivecities.org.au/wpcontent/uploads/2016/06/AGSBS-A5-Vegetation-selectionfor-stormwater-biofilters.pdf Colmer, T. D. (2003). Long-distance transport of gases in plants: A perspective on internal aeration and radial oxygen loss from roots. Plant, Cell & Environment, 26(1), 17–36. https://doi.org/10.1046/j.1365-3040.2003.00846.x
This investigation compared six native Australian wetland macrophyte species for their ability to remove nitrate-nitrogen and phosphate-phosphorus from synthetic piggery-strength effluent over 14 days. Species made a significant difference to both nitrateN removal (p < 0.001) and phosphate-P removal (p = 0.028).
FAWB. (2009). Adoption guidelines for stormwater biofiltration systems. Facility for Advancing Water Biofiltration, Monash University.
C. appressa was the best performer on every measure: highest nitrate-N removal (98.3%), highest phosphateP removal (90.8%), best health score (4.5/5), and the most growth (+2.3 cm). This supports the hypothesis
Paerl, H. W., & Otten, T. G. (2013). Harmful cyanobacterial blooms: Causes, consequences, and controls. Microbial Ecology, 65(4), 995–1010. https://doi.org/10.1007/s00248012-0159-y
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Kadlec, R. H., & Wallace, S. D. (2009). Treatment wetlands (2nd ed.). CRC Press. Minkus, B. (2025). Biofiltration of agricultural runoff using Carex appressa. Scientific Research in School, 7(1), 123– 128
Scientific Research in School Volume 8 Issue 1 2026 PASCO Scientific. (n.d.). ezSample snap vial – Nitrate (EZ-2333B) [Product manual]. https://cdn.pasco.com/product_document/ezSample-SnapVial-Nitrate-EZ-2333B-Manual.pdf PASCO Scientific. (n.d.). ezSample snap vial – Phosphate (EZ-2337) [Product manual]. https://cdn.pasco.com/product_document/ezSample-SnapVial-Phosphate-Manual-EZ-2337.pdf Smith, V. H., Tilman, G. D., & Nekola, J. C. (1999). Eutrophication: Impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems.
Environmental Pollution, 100(1–3), 179–196. https://doi.org/10.1016/S0269-7491(99)00091-3 Tucker, R. (2015). Piggery manure and effluent management and reuse guidelines. Australian Pork Limited. Vymazal, J. (2007). Removal of nutrients in various types of constructed wetlands. Science of the Total Environment, 380(1–3), 48–65. https://doi.org/10.1016/j.scitotenv.2006.09.014 Vymazal, J. (2014). Constructed wetlands for treatment of industrial wastewaters: A review. Ecological Engineering, 73, 724–751. https://doi.org/10.1016/j.ecoleng.2014.09.034
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The Effect of Increasing Phosphate Pollution on Artemia Nyos Hatching Numbers Liam Wyatt Barker College This investigation examined how phosphate pollution, simulated through varying concentrations of ammonium dihydrogen phosphate (ADP), affects the hatching success of Artemia nyos, also known as Brine Shrimp or Sea Monkeys. Six phosphate concentrations (0, 0.08, 0.4, 0.8, 1.7, and 4.1 mg/L) were tested using 250 mL conical flasks containing 150 mL of prepared solution and 1.5 g of A. nyos egg mixture. Three replicates were conducted for each concentration, and the number of hatched Artemia was counted after two days. Results showed a clear decline in hatching success as phosphate concentration increased, with the control treatment (0 mg/L) producing the highest mean number of 17.3 individuals, compared with only 2 individuals at 4.1 mg/L. A one-way ANOVA revealed a statistically significant effect of phosphate concentration on hatching success (F = 10.55, p = 0.000458), supporting the alternative hypothesis. These findings suggest that elevated phosphate levels negatively affect A. nyos hatching, likely through eutrophication-related reductions in dissolved oxygen. Although the investigation was limited by a small sample size, the assumption of equal egg numbers between samples, and laboratory conditions that may not fully reflect natural aquatic environments, the results still support the potential use of A. nyos as a bioindicator of pollution and highlight the ecological risks associated with increasing nutrient runoff into waterways.
Literature Review Phosphate and the Effect Upon Oceans and Waterways Phosphate in the ocean acts as a limiting nutrient, meaning it controls the growth of algae and phytoplankton by supplying essential nutrients. When phosphate levels are too high, eutrophication can occur. This leads to rapid algal growth, followed by large-scale die-off (Brownlie et al. 2022) (UNEP 2024). As algae decomposes, bacteria consume significant amounts of oxygen, creating hypoxic “dead zones” that can suffocate marine organisms. Conversely, when phosphate levels are too low, there are not enough nutrients to support the growth of algae and phytoplankton, which are the primary producers that form the base of marine food webs (US EPA 2013). Why and where is this a Problem? The widespread adoption of phosphate-based fertilisers such as Diammonium phosphate (DAP) and Monoammonium phosphate (MAP), from the 1950’s, has resulted in increasingly harmful levels of phosphate within waterways. Areas most affected are those that receive excess nutrients from upstream sources, such as river mouths, estuaries, and bays. Bodies of water located near agricultural land are particularly vulnerable, as they are more likely to receive nutrient-rich runoff during rainfall, harming the biodiversity of the waterway (Water Research Center, 2020).
Phosphate Cycle and Agricultural Runoff Pathways In the agricultural sector, phosphate fertilisers are commonly applied to crops and pastures as granular products such as superphosphate, Mono or Di ammonium phosphate. Once applied, phosphate does not move through soil as easily as nitrate; instead, it tends to bind to soil minerals and organic matter near the soil surface. Problems arise when fertiliser is applied in excess of crop demand, spread immediately before heavy rain, left on bare or recently cultivated ground, or used on sloping land with poor ground cover (Hart, Quin & Nguyen 2004). During rainfall or irrigation, water can either dissolve a small portion of the phosphate or detach and transport fine soil particles that have phosphate attached to them. Because much of the phosphorus is particle-bound, soil erosion is often the main pathway by which agricultural phosphate reaches waterways (Hansen, Daniel, Sharpley & Lemunyon 2002). Runoff from fields enters farm drains, roadside ditches, drainage channels, creeks, and rivers, while phosphorus attached to sediment can also settle temporarily in dams or wetlands and later be remobilised during storms as diagrammed in figure 1. Additional agricultural sources include livestock manure, poultry litter, and effluent from dairies, feedlots, and piggeries, all of which contain high phosphorus concentrations and can be washed into drainage systems if not properly stored and managed (King, K. W., Williams, M. R., Macrae, M. L., Fausey, N. R.,
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Scientific Research in School Volume 8 Issue 1 2026 Frankenberger, J., Smith, D. R., Kleinman, P. J., & Brown, L. C. 2015).
Figure 1: Schematic diagram of agricultural runoff generation and control (Source: Xia, Y., Zhang, M., Tsang, D.C.W., 2020)
Levels Typically Found in Various Waterways Phosphate levels in waterways vary depending on location, surrounding land use, and human impact. • • •
Oceans - 0.005–0.05 mg/L Coastal waters - 0.01–0.1 mg/L Agricultural or urban impacted rivers - 0.1–0.5 mg/L
The Potential Effects upon Hatching Numbers The hatching of A.nyos cysts requires oxygen for aerobic respiration to produce ATP, which fuels embryo growth and development. (Yu & Lu 2018). Low or fluctuating oxygen levels limit this metabolic activity, resulting in potential negative affects upon lifecycle processes as illustrated in Figure 2, including the following: • Reduced hatching success: insufficient oxygen may prevent cysts from completing development (Torrens & Steeby 2008). • Delayed hatching: embryos switch to less efficient anaerobic pathways, slowing development (Torrens & Steeby 2008). • Asynchronous hatching: Fluctuating oxygen levels cause uneven metabolic activation, leading to irregular hatching times (Torrens & Steeby 2008). • Increased larval mortality: Newly hatched brine have high oxygen demands and are particularly vulnerable to hypoxia
Figure 2: Brine shrimp lifecycle (Source: University of Utah, n.d.)
Artemia nyos as Bioindicators Living organisms, including plants, animals, and microbes can be used to screen, monitor, and assess the health of ecosystems and environmental quality. (Fihlo et al., n.d). A. nyos are saltwater invertebrate microcrustaceans found in salt lakes and estuaries connected to the open sea. Starting off as durable cysts that can hatch when in contact with water, growing up to 2.5cm and reaching maturity within 4 weeks, the organisms have been widely used for testing toxicity of chemicals and screening of insecticidal activity. Being a primary consumer within the food web, highly sensitive to environmental fluctuations, the growth and reproduction of A. nyos make for useful bioindicators, particularly in the detection of fluctuating oxygen levels, due to their need for oxygen to facilitate rapid cellular respiration. (Yu & Lu 2018). Current Bioindicator Limitations In terms of the analysis of phosphate pollution, current research has used other bioindicators such as Glass Shrimp, and various macroinvertebrates such as Limnephilidae, worms, and molluscs. However, results show that they respond to the effects of phosphate enrichment (particularly hypoxia and food quality changes) rather than to phosphate as a direct stressor (Nebeker, A. V., Onjukka, S. T., Stevens, D. G., & Chapman, G. A. 1996). Their additional limitation is that they are too hardy and too taxonomically inconsistent to serve as sensitive, reproducible bioindicators, and existing research has focused almost entirely on metals, pesticides, and PAHs rather than nutrient loading (Webb 2011) & (Key et al. 2006). Utah Case Study Native to The Great Salt Lake in northern Utah, the brine shrimp are one of the only species of animal that can thrive in such high salinity environments, making
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Scientific Research in School Volume 8 Issue 1 2026 them a keystone species to the food web of the local ecosystem. The brine shrimp provide significant caloric fuel for millions migratory birds, thus providing A. nyos with a significant ecological role (Great Salt Lake Artemia: Ecosystem Functions and Services with a Global Reach | Springer Nature Link, n.d.). Within Utah, A. nyos have become a widely used bioindicator to measure the function of The Great Salt Lake and the effect pollutants have upon the aquatic ecosystem. In recent years, (Wayne A. Wurtsbaugh, Amy M. Marcarelli et Al., n.d.) have found a relationship between A. nyos population numbers and eutrophic/polluted waterways such as Farmington Bay as a result agricultural and sewage related runoff. Decided Measurements of single independent variable (Phosphate concentration) Based upon the volatility of A. nyos and the small parameters of ideal conditions for optimised hatching numbers. The range of concentrations selected were, 0.0mg/L of Ammonium Dihydrogen phosphate concentration, followed by 0.1 mg/L (very low), 0.5 mg/L (low–moderate), 1 mg/L (moderate), 2mg/L (high), 5 mg/L (very high).These levels of concentration were also assisted from (biological reviews 2021) that investigated rising levels of phosphate and nitrogen levels in relation to aquatic invertebrate declines, as well as (Ayoub et al., 2023) who evaluated the Effects of Three Chemical Fertilizers on Artemia salina (a particular species of brine shrimp). Research Gap/Contribution to Literature Timeline Study of the effects of phosphate pollution have previously been measured against various biotic factors such as algal growth and abiotic factors such as nutrient imbalance (Qiu & McComb 1994). This project aims to expand on this research by using A. nyos to measure the impacts of changing environmental factors. There appears to be limited information regarding the potential use of A. nyos as bioindicators of agricultural pollution. Despite their sensitivity to environmental changes and fast hatching and growth rates, other invertebrate species such as Glass shrimp are used more often (Webb 2011). This project aims to contribute to the literature timeline by highlighting the potential use of A. nyos to measure changing environmental conditions in an age of rising pollution levels.
Scientific Research Question How does phosphate pollution, simulated by different phosphate concentration levels effect the number of hatched Artemia nyos?
Scientific Hypothesis That as the phosphate concentration level increases from the minimal 0mg/L the number of hatched Artemia nyos will decrease.
Methodology Preparation of Ammonium Dihydrogen Phosphate Solutions Five one-litre beakers were filled with deionised water, each supplemented with 2.5 scoops of commercial sea monkey growth food to stimulate algal growth and provide a nutrient baseline. Ammonium dihydrogen phosphate (NH₄H₂PO₄) was then added to each beaker in quantities calculated to achieve the required concentration levels across the experimental groups. Each solution was subsequently divided equally across six 250 mL conical flasks, with the remaining volume retained for use in future trials. Preparation of Experimental Samples Using 100 mL and 150 mL measuring cylinders in conjunction with a pipette, 150 mL of each phosphate solution at its respective concentration was dispensed into three 250 mL conical flasks. Each flask was labelled to indicate its corresponding trial number and concentration. To each flask, 1.5 g of a combined commercial sea monkey egg and water purifier mixture was added. Each flask was swirled for one minute to ensure complete dissolution of all non-egg contents, then sealed with Parafilm. The flasks were placed in a welllit water bath maintained at 26°C, with positioning arranged to ensure uniform light exposure across all six flasks. This entire procedure was repeated on two further occasions using the same ammonium dihydrogen phosphate solutions, yielding a total of three experimental trials per concentration. Counting of Hatched Artemia Nyos After two days, the contents of each conical flask were individually poured into a petri dish that was then placed onto a colony counter. The visible A. nyos were counted and results recorded. Data analysis After data was recorded and placed into a results table, a statistical one-way ANOVA test was used to determine if the results were significantly different at each of the different phosphate concentrations.
Results After results were collected, data was placed into result tables to be analysed. Table 1 shows averages
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Scientific Research in School Volume 8 Issue 1 2026 and standard deviations of the data collected. Figure 3 is then a graphed representation of the average number of hatched A. nyos, as well as their respective standard deviation. Table 2 reflects the results from the oneway ANOVA test and Table 3 displays a Post-Hoc Tukey's HSD Test to determine if there was a significant difference in hatching numbers of A. nyos in different phosphate concentration solutions. Table 1: Average number of hatched A. nyos in Phosphate concentrations (mg/L), 0, 0.08, 0.4, 0.8, 1.7 and 4.1 Phosphate Conc. (mg/L) 0 0.08 0.4 0.8 1.7 4.1
No. of A. nyos hatched per 1.5g of eggs
Mean
Std Dev
Std Error
17.3 12.7 9 7.3 6 2
3.51 5.03 2 0.58 2 1.73
2.03 2.9 1.15 0.33 1.15 1
Table 2: Results of a one-way Anova test Group (ADP mg/L) 0 0.08 0.4 0.8 1.7 4.1 ANOVA Table Source of Variation Between Groups (Treatment) Within Groups (Error) Total
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n 3 3 3 3 3 3
Sum 52 38 27 22 18 6
Mean 17.3333 12.6667 9 7.3333 6 2
Variance 12.3333 25.3333 4 0.3333 4 3
Std Dev 3.5119 5.0332 2 0.5774 2 1.7321
SS
df
MS
F
P-value
430.9444
5
86.1889
10.5537
0.000458
98
12
8.1667
528.9444
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Table 3: Post Hoc tests for one-way ANOVA testing different phosphate concentrations of hatching A. nyos. Key – results highlighted in grey: individuals significantly different [Mean diff>7.16HSD], Group: Phosphate conc.Group Group A (P mg/L) 0 mg/L 0 mg/L 0 mg/L 0 mg/L 0 mg/L 0.08 mg/L 0.08 mg/L 0.08 mg/L 0.08 mg/L 0.4 mg/L 0.4 mg/L 0.4 mg/L 0.8 mg/L 0.8 mg/L 1.7 mg/L
Group B (P mg/L) 0.08 mg/L 0.4 mg/L 0.8 mg/L 1.7 mg/L 4.1 mg/L 0.08 mg/L 0.4 mg/L 1.7 mg/L 4.1 mg/L 0.08 mg/L 0.4 mg/L 1.7 mg/L 1.7 mg/L 4.1 mg/L 4.1 mg/L
Mean A
Mean B
Mean Diff
HSD
Significant?
17.33 17.33 17.33 17.33 17.33 12.67 12.67 12.67 12.67 9 9 9 7.33 7.33 6
12.67 9 7.33 6 2 7.33 9 6 2 7.33 2 6 6 2 2
4.67 8.33 10 11.33 15.33 5.33 3.67 6.67 10.67 1.67 7 3 1.33 5.33 4
7.16 7.16 7.16 7.16 7.16 7.16 7.16 7.16 7.16 7.16 7.16 7.16 7.16 7.16 7.16
No Yes Yes Yes Yes No No No Yes No No No No No No
Figure 3: Graphed data of average A. nyos hatched in Ammonium concentration (mg/L) 0, 0.08, 0.4, 0.8, 1.7, 4.1
Discussion Trends showed that in the controlled solution, (0mg/L) the number of hatched A. nyos was significantly higher (average of 17.3 A. nyos hatched) than that of all other solutions with concentration of (0.08mg/L averaged 12.7 A. nyos hatched), (0.4mg/L averaged 12.7 A. nyos hatched), (0.8mg/L averaged 7.3 A. nyos hatched), (1.7mg/L averaged 6 A. nyos hatched) and (4.1mg/L averaged 2 A. nyos hatched). Based off the scientific hypothesis, that as the phosphate concentration level increases from the minimal 0mg/L the number of hatched Artemia nyos will decrease, the hatching number of A. nyos decreased as phosphate concentration increased as expected. With the optimal concentration being 0mg/L for hatching success, thus
supporting the initial hypothesis. This can be furthers supported with the results of the one-way ANOVA test producing a measured P-value less then 0.05 (at 0.000458). This means the null hypothesis of there being no significant difference between increasing phosphate concentration and A. nyos hatching numbers, can be rejected and the alternative hypothesis that there is a significant difference between increasing phosphate concentration and A. nyos hatching numbers accepted. Similarly, the graph in figure 3 demonstrates the trends in the data, which can be explained by the fact that species are unable to survive in solutions that are too eutrophic as a result of phosphate concentration increases, supported by significant hatching success Science Extension Journal • 41
Scientific Research in School Volume 8 Issue 1 2026 decrease between (0mg/L and 0.5mg/L) with a mean difference of (8.33 which is greater than the HSD of 7.16). This increasing phosphate concentration is representative of current phosphate levels and potential increases if pollution persists (Brownlie, W.J. et al. 2022) (Lucas, E., Kennedy, B., Roswall, T. et al, n.d.). This significant decrease in hatching success can be attributed to the increasing eutrophication that when combined with the ‘sea monkey food’ caused a decomposition of excess nutrients that consumed oxygen within the conical flask solution (McDaniel et al 2005). This depletion of oxygen then disrupts the metabolic functions such including cellular respiration that slows development, causing structural deformities, and increases the rate of embryonic mortality that results in no hatching success (Nebeker, A. V., Onjukka, S. T., Stevens, D. G., & Chapman, G. A. 1996). Hatching numbers were highest at 0mg/L with a mean of 17.3 hatched A. nyos which would be their ideal phosphate concentration as it is closest to the 0.03mg/L average found in the Great Salt Lake to which they are native to. There were no unexpected results in this experiment, perhaps due to the inability to recognise any outliers due to a limited sample size. However, all averages had a standard deviation differing in no more than 1.52 from each other, which suggests all results were consistent. Within this experiment there were several issues experienced. One of these issues occurred due to a first trial that used brine shrimp eggs sourced from EduSupplies that yielded no hatching numbers. This lack of results could be attributed to the possibility that the eggs had been kept in refrigerated conditions and had been exposed to moisture content causing the process of cryptobiosis to be disrupted before being used for the scientific investigation, resulting in blank cysts. This theory can be further supported by qualitative data of a rotting smell produced after the brine shrimp were in contact in the solutions for two days, suggesting the cysts were deceased and beginning to decompose in the solution. This meant for future trials brine shrimp eggs were used from enclosed undisturbed commercial packets of ‘sea monkey eggs’. Another issue within this experiment was the initially proposed phosphate concentration, ranging from 50mg/L to 400mg/L being too high to produce any results. These concentrations were decided based off concerns of the final phosphate concentrations being too small to see any effect. However, the higher concentrations simulated phosphate pollution at unrealistic levels, resulting in no hatching numbers
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across all trials of all concentrations. To achieve more realistic results simulative of current phosphate pollution levels and potential future levels the concentrations were then lowered between 0.1mg/L to 5mg/L. Given the time constraint of the investigation a limited sample size was produced with only 3 trials for each concentration potentially reducing the overall reliability of the investigation. The assumption was made that the same 1.5g mass of eggs weighed into the conical flasks had the same amount of eggs. This was made due to a physical count of eggs would not have been possible with the available equipment. This meant any trends in the data may not be completely reliable due to the small sample size being unable to remove any outliers such as dormant cysts or varying numbers of cysts in each sample. The variation in the number of eggs between samples could have influenced hatching rates independently of phosphate concentration, reducing the validity of the investigation by introducing an uncontrolled variable. Additionally, the low number of trials increases the impact of random error and anomalous results, decreasing the reliability and reproducibility of the findings. Further research should consist of testing the same research question with a larger sample size, as well as testing smaller increments between 0mg/L and 1 mg/L that are more commonly found in A. nyos natural habitats to find out the minimum level needed to disrupt the organism as well as to gain a more accurate depiction of real phosphate changes and the potential for A. nyos as a bioindicator when in a more familiar environment. This was not possible within this experiment due to limited access to accurate information regarding the phosphate concentration found in such areas. Another suggestion for future research includes the exploration of investigating the effects of increasing phosphate concentrations upon other A. nyos measures such as growth rate, size or hatching speed.
Conclusion This study investigated, and recorded data regarding the effect of increasing phosphate concentration levels on the number of hatched A. nyos, and has found a significant difference in the data that rejects the null hypothesis and supports the initial hypothesis, that as the phosphate concentration level increases from the minimal 0mg/L the number of hatched Artemia nyos will decrease. Five different Ammonium Dihydrogen phosphate solutions were tested, at concentrations of
Scientific Research in School Volume 8 Issue 1 2026 0.1mg/L, 0.5mg/L, 1mg/L, 2mg/L, 5mg/L as well as one controlled solution of 0mg/L. It was found that the highest average hatching numbers of 17.3 were found in the controlled solutions of 0mg/L, followed by an average of 12.7 in the 0.1mg/L solutions. Hatching numbers then continued to decrease as the concentration of the solution increased with 2mg/L producing an average of 6 hatched and 5mg/L producing and average of 2 hatched. These results strengthen the understanding of the negative impacts of increasing phosphate pollution in aquatic environments and the organisms within it. The investigation also acts as a reflection toward the usefulness and effectiveness of using A. nyos to demonstrate these impacts and their potential use as bioindicator in other fields of research. Whilst further research is needed to solidify the conclusion drawn, that increasing phosphate concentrations in aquatic environments will have a harmful effect on the number of hatchings of A. nyos, the findings align with other studies, and shall hopefully contribute to stimulating future research toward minimizing anthropogenic contributions, specifically agricultural runoff, to increasing the phosphate concentrations of aquatic environments.
Acknowledgements I would like to thank my supervisor Mrs Kathy Haigh for helping with the formation of this investigation, and in providing detailed feedback that led to the development of my final investigation. I would also like to thanks Mrs Virginia Ellis in the sourcing of the necessary equipment and the forward guidance provided for my practical component. Additionally, this project and its details wouldn’t be possible without Mia Vesey’s pre-existing work and method on the effect of acidification levels on Sea Monkey hatching rates (2022).
Declaration of use of AI Throughout the project, the use of ai, predominantly using the software of ‘Chat GPT’ was kept in one project folder. From this, it recorded all the conversations and prompts that were used in reference to the investigation. Asking the ai to recall all the conversations, it was then tasked with creating a declaration of its use in the investigation. Artificial Intelligence (AI) was used throughout the planning, research, and writing stages of this investigation as a support tool. AI was not used to generate experimental data, perform practical work, or determine the final conclusions of the investigation. All experimental procedures, observations, hatching
counts, statistical analyses, and interpretations of results were completed by the researcher. AI assistance was primarily used to support background research and scientific communication. During the literature review stage, AI was used to explain the role of phosphate in aquatic ecosystems, including eutrophication, algal blooms, oxygen depletion, and the movement of phosphate from agricultural fertilisers into waterways through runoff and soil erosion. AI was also used to identify relevant scientific papers and reports relating to phosphate pollution, aquatic ecosystems, and Artemia species, which were then reviewed and incorporated into the report where appropriate. Throughout the development of the investigation, AI was used to clarify scientific concepts relating to Artemia hatching requirements, including the role of oxygen in aerobic respiration and embryo development. AI also assisted in explaining how changing phosphate concentrations may indirectly affect hatching success through eutrophication and hypoxia. Information regarding phosphate concentrations in waterways, the Great Salt Lake ecosystem, and the use of Artemia as bioindicators was also obtained with AI assistance and subsequently verified using scientific literature. In addition, AI was used as a writing support tool. This included assistance with improving the clarity and structure of sections of the literature review, discussion, conclusion, and abstract. AI was also used to help generate APA-style in-text citations from author lists provided by the researcher and to suggest limitations, improvements, and future research directions based on the experimental design. All decisions regarding the research question, hypothesis, methodology, data analysis, interpretation of results, and final written content remained the responsibility of the researcher. AI was used as an aid to understanding, literature exploration, and scientific writing rather than as a substitute for independent research or analysis.
References Brownlie, W., Spears, B. M., M.A. Sutton, Heal, K. V., & Reay, D. S. (2022). Our Phosphorus Future -Summary for Policymakers. https://doi.org/10.13140/RG.2.2.11029.40162 Climate Change Effects on Phosphorus Loss from Agricultural Land to Water: A Review | Current Pollution Reports | Springer Nature Link. (n.d.). Retrieved June 7, 2026, from https://link.springer.com/article/10.1007/s40726-02300282-7
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“Comparative Analysis of Pollution in Farmington Bay and the Great Salt” by Wayne A. Wurtsbaugh, Amy M. Marcarelli et al. (n.d.). Retrieved May 27, 2026, from https://digitalcommons.usu.edu/wats_facpub/540/
Phosphate Water Testing Water Quality Monitoring Growth Lake Eutrophication. (n.d.). Retrieved February 14, 2026, from https://www.knowyourh2o.com/outdoor4/phosphates-in-the-environment
Dissolved oxygen and dietary phosphorus modulate utilization and effluent partitioning of phosphorus in rainbow trout (Oncorhynchus mykiss) aquaculture— ScienceDirect. (n.d.). Retrieved June 8, 2026, from https://www.sciencedirect.com/science/article/abs/pii/S026 9749105002137?utm_source=chatgpt.com
Phosphorus runoff from agricultural land and direct fertilizer effects: A review—PubMed. (n.d.). Retrieved June 5, 2026, from https://pubmed.ncbi.nlm.nih.gov/15537918/
Effect of low dissolved oxygen on aquatic life stages of the caddisfly Clistoronia magnifica (Limnephilidae)—PubMed. (n.d.). Retrieved June 8, 2026, from https://pubmed.ncbi.nlm.nih.gov/8975816/ Filho, S. T., Jorge, E. N. de L. F., Oliveira, A. S. de, Marques, M. R. da C., & Franco, H. A. (n.d.). Study on the use of Artemia salina as bioindicator in the ecotoxicological evaluation of landfill leachate. Revista Eletrônica Em Gestão, Educação e Tecnologia Ambiental, 21, 24. https://doi.org/10.5902/2236117029718 Great Salt Lake Artemia: Ecosystem Functions and Services with a Global Reach | Springer Nature Link. (n.d.). Retrieved May 27, 2026, from https://link.springer.com/chapter/10.1007/978-3-03040352-2_7 Key, P. B., Wirth, E. F., & Fulton, M. H. (2006). A Review of Grass Shrimp, Palaemonetes spp., as a Bioindicator of Anthropogenic Impacts. Environmental Bioindicators, 1(2), 115–128. https://doi.org/10.1080/15555270600685115 Nessel, M. P., Konnovitch, T., Romero, G. Q., & González, A. L. (2021). Nitrogen and phosphorus enrichment cause declines in invertebrate populations: A global meta‐analysis. Biological Reviews, 96(6), 2617–2637. https://doi.org/10.1111/brv.12771 (PDF) Evaluation of the Effects of Three Chemical Fertilizers on Artemia salina. (n.d.). ResearchGate. Retrieved April 24, 2026, from https://www.researchgate.net/publication/370433362_Eval uation_of_the_Effects_of_Three_Chemical_Fertilizers_on _Artemia_salina (PDF) The Fate and Transport of Phosphorus in Agricultural Systems. (n.d.). Retrieved June 5, 2026, from https://www.researchgate.net/publication/43277646_The_F ate_and_Transport_of_Phosphorus_in_Agricultural_Syste ms
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Phosphorus transport in agricultural subsurface drainage: A review—PubMed. (n.d.). Retrieved June 5, 2026, from https://pubmed.ncbi.nlm.nih.gov/26023966/ Qiu, S., & McComb, A. (1994). Effects of oxygen concentration on phosphorus release from reflooded airdried wetland sediments. Australian Journal of Marine and Freshwater Research, 45(7), 1319–1328. https://doi.org/10.1071/MF9941319 Tanaka, Y., & Omura, S. (1993). AGROACTIVE COMPOUNDS OF MICROBIAL ORIGIN. Annual Review of Microbiology, 47(Volume 47, 1993), 57–87. https://doi.org/10.1146/annurev.mi.47.100193.000421 Torrans, L., & Steeby, J. (2008). Effects of Dissolved Oxygen Concentration on Oxygen Consumption and Development of Channel Catfish Eggs and Fry: Implications for Hatchery Management. North American Journal of Aquaculture, 70(3), 286–295. https://doi.org/10.1577/A07046.1 Understanding phosphorus: Global challenges and solutions. (n.d.). Retrieved November 3, 2025, from https://www.unep.org/news-and-stories/story/whatphosphorus-and-why-are-concerns-mounting-about-itsenvironmental-impact US EPA, O. (2013, March 12). The Effects: Dead Zones and Harmful Algal Blooms [Overviews and Factsheets]. https://www.epa.gov/nutrientpollution/effects-dead-zonesand-harmful-algal-blooms Webb, D. (2011). Freshwater shrimp (Palaemonetes australis) as a potential bioindicator of crustacean health. Environmental Monitoring and Assessment, 178(1), 537– 544. https://doi.org/10.1007/s10661-010-1711-1 Yu, J., & Lu, Y. (2018). Artemia spp. Model—A WellEstablished Method for Rapidly Assessing the Toxicity on an Environmental Perspective. Medical Research Archives, 6(2). https://doi.org/10.18103/mra.v6i2.1700
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Aloe Vera Efficacy Against Fungal Growth in Textiles Mia Nocom Barker College Fungal contamination of skin-contact textiles poses documented risks to fibre integrity and human health. This study investigated the antifungal efficacy of aloe vera (Aloe barbadensis miller) extract at five concentrations (0%, 25%, 50%, 75%, and 100%) applied to cotton and polyester substrates, using disc diffusion assays against Kluyveromyces marxianus and Saccharomyces boulardii — yeast species representative of what can be found in warm, humid textile microenvironments. Treated fabric samples were prepared via a pad-dry-cure method with citric acid crosslinking, then placed on inoculated agar plates and incubated at 30°C for 24 hours. Zones of inhibition (ZOI) were measured in millimetres. Results showed measurable antifungal activity across multiple concentrations, with polyester substrates consistently producing larger ZOI than cotton. The doseresponse relationship was non-monotonic — for S. boulardii on polyester, ZOI peaked at 50% (2.67 mm) before declining sharply at 75% (0.03 mm) — attributed to the increased viscosity of higherconcentration extracts limiting agar diffusion. These findings partially support the hypothesis that aloe vera possesses concentration-dependent antifungal activity, providing foundational concentration-response data for evidence-based, sustainable antimicrobial textile finishing.
Literature Review Textiles worn in close contact with the body create warm, humid microenvironments that promote fungal proliferation, resulting in fibre degradation, odour formation, discolouration and potential disease transmission (Owen & Laird, 2020). Micro-fungi including Aspergillus, Penicillium, and Fusarium enzymatically degrade cellulose and reduce fibre tensile strength (Balachandran 2023) (Akhoundi et al. 2022). S. Wazed Ali, Roli Purwar, M. Joshi & S. Rajendran (2009) documented that Aspergillus flavus and Alternaria solani caused visible decolourisation and dye degradation in textile substrates, demonstrating the aesthetic and structural threat posed by fungal contamination. Beyond material damage, textiles function as fomites: Owen and Laird (2020) established that microorganisms survive on fabric surfaces for extended periods and can transfer to skin, making indirect fungal transmission biologically plausible. Dermatophytoses—superficial fungal infections—such as tinea pedis, caused primarily by Trichophyton rubrum, are transmitted almost exclusively through contact with contaminated textiles and environmental surfaces (Nigam & Saleh, 2023), illustrating the direct public health relevance of fungal contamination in clothing.
2021) (Bibi et al. 2024) (refer to Figure 2). This prolonged moisture retention, combined with organic residues such as sebum and skin cells, provides the aqueous nutrient substrate necessary for fungal spore germination and hyphal extension. Cornell University researchers identified cotton garments as the leading carrier of fungal spores in hospital environments, attributing this to the fibrous structure enabling high spore adhesion and retention (Cornell University News, 2001). Polyester, although hydrophobic and quick-drying, is oleophilic and adsorbs sebum strongly onto its surface, sustaining microbial activity in the absence of free moisture (refer to Figure 3) (Callewaert et al., 2021). The interaction between fibre hydrophobicity and microbial colonisation is influenced by surface chemistry, with hydrophobic fibres capable of supporting microbial adhesion through interactions with organic residues (Møllebjerg et al., 2021) These contrasting but each problematic properties motivate comparative evaluation of antimicrobial treatments across both fibre types.
Fibre Chemistry and Fungal Susceptibility The susceptibility of a textile to fungal colonisation is determined substantially by fibre chemistry. Cotton is a hydrophilic cellulosic fibre with a moisture retention approximately ten times greater than that of polyester under equivalent drying conditions (Callewaert et al.,
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friendly than synthetic counterparts while maintaining broad-spectrum activity against pathogenic microorganisms.
Figure 1: Experimental set up of lawn plating and inoculation of treated fabric squares in the fungal species S.boulardii in a laboratory
Figure 2: Molecular structure of Cotton Fiber (Source: Khaifa & Ladhari, 2019)
Figure 3: Molecular structure of Polyester Fiber (Source: Decon, 2022)
Conventional and Plant-Derived Antimicrobial Treatments Conventional antimicrobial textile finishes rely on synthetic agents including triclosan, quaternary ammonium compounds (QACs), and metallic nanoparticles. The development of antimicrobial textiles requires balancing microbial inhibition with environmental safety, as many synthetic finishes present concerns regarding toxicity and persistence (Morais et al., 2016). While effective, these compounds raise environmental concerns: triclosan is associated with endocrine disruption and aquatic toxicity, and QACs accumulate in wastewater systems (Mercier et al., 2024). As a result, plant-derived alternatives have attracted increasing research attention (Fatma Filiz Yıldırım et al. 2020). Vigneshwaran et al. (2025) identified plant extracts as advanced sustainable antimicrobial innovations for skin-contact textiles, noting that their complex phytochemical profiles enable multi-target mechanisms of action that reduce the likelihood of resistance developing. Khan et al. (2024) further concluded in a comparative review that naturally treated antimicrobial textiles are safer and more eco-
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Aloe Vera: Phytochemistry and Antifungal Activity Aloe vera (Aloe barbadensis Miller) contains over 75 bioactive compounds, including anthraquinones (aloin, aloe-emodin), saponins, flavonoids, tannins, and polysaccharides such as acemannan (Editor BCSRJ. 2020). Its antifungal activity is primarily attributed to anthraquinones, which disrupt fungal cell membrane integrity and inhibit metabolic processes, and saponins, which act as biological surfactants causing membrane permeabilisation and cell lysis (Radha & Laxmipriya, 2015). In disc diffusion assays, ethanolic aloe extracts produced inhibition zones of up to 19 mm against Fusarium oxysporum and 18 mm against Aspergillus niger (Naseem et al., 2020). Jarrar et al. (2025) reported that whole-leaf aloe extract exhibited superior antifungal efficacy against Candida albicans (MIC₅₀ ≈ 75.42 mg/mL) compared to gel extract alone (MIC₅₀ ≈ 184.93 mg/mL), indicating that the anthraquinone-rich latex fraction contributes substantially to antifungal potency. Application of Aloe Vera to Textiles Application of aloe vera to textiles has been most rigorously studied via the pad-dry-cure method. Ali et al. (2014) demonstrated via FTIR spectroscopy that aloe vera active ingredients bond covalently to the hydroxyl groups of cotton cellulose when 1,2,3,4butanetetracarboxylic acid (BTCA) is used as a crosslinking agent; cotton fabric finished at concentrations ≥3% (w/v) inhibited both Staphylococcus aureus and Escherichia coli in Antibacterial Activity of Textile Materials: Parallel Streak Method (AATCC-147) disc diffusion assays. ATCC-147 is a qualitative test used to evaluate the bacteriostatic (growth-inhibiting) properties of treated fabrics. It primarily determines if an antibacterial agent diffuses out of a textile to prevent bacterial growth. Jothi (2008) reported that antimicrobial activity increased with concentration from 1–5 g/L, with maximum efficacy at 5 g/L and wash durability maintained over 50 cycles. Rajendran et al. (2018) confirmed that combining aloe vera with bioactives from plants with medicinal properties produced effective antifungal and antibacterial cotton finishes with good laundering durability. Despite these findings, systematic evaluation of aloe vera’s antifungal efficacy across multiple concentrations against yeast species specifically relevant to human skin — such as K. marxianus and S. boulardii — remains limited. Both species exhibit documented susceptibility to antifungal agents in standard disc
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diffusion assays and grow at temperatures approximating skin-contact textile conditions (35°C), making them appropriate model organisms (Barone et al., 2025; Lim et al., 2019). The present study addresses this gap by assessing zone of inhibition data across five aloe vera concentrations.
Scientific Research Question To what extent is aloe vera in inhibiting fungal growth (Kluyveromyces marxianus; Saccharomyces boulardii) when applied to textiles (cotton; polyester) across five concentrations (0%, 25%, 50%, 75%, and 100%)?
Scientific Hypothesis Aloe vera extract applied to cotton and polyester fabric squares will produce concentration-dependent zones of inhibition against Kluyveromyces marxianus and Saccharomyces boulardii in disc diffusion assays, with larger zones at higher concentrations, demonstrating that aloe vera can confer antifungal properties to textile substrates.
prior to treatment to remove manufacturing contaminants. Each fabric square was immersed in its respective aloe vera solution for 5 minutes to ensure full saturation, then transferred to the 8% citric acid solution and soaked for 5–10 minutes to allow crosslinking. Excess liquid was removed by applying manual pressure using rollers. Samples were dried at approximately 80–100°C and subsequently cured at 140–160°C for 2–5 minutes to promote esterification and bind aloe vera compounds to the fabric matrix. A schematic of the pad-dry-cure process is illustrated in Figure 4.
Discussion The results from the experiment provide partial evidence supporting the first hypothesis. The PLA and HIPS ultimate tensile strength did not change, thus, the first hypothesis is partially supported. The gradients of the graph in Figure 12
Methodology Preparation of Aloe Vera Extract and Solutions Fresh aloe vera leaves were obtained and cut open using a scalpel to expose the inner gel. The gel was scraped out using a paring knife and transferred into labelled beakers. A series of aloe vera solutions were prepared at concentrations of 0%, 25%, 50%, 75%, and 100% using distilled water. For the 0% control, 100 mL of distilled water was measured. For the 25% solution, 6.25 g of aloe vera extract was combined with 18.75 mL of distilled water and blended until uniform. The 50%, 75%, and 100% solutions were prepared following the same mass-to-volume ratio, with the 100% solution consisting of undiluted aloe vera extract. Each solution was sealed with clingwrap to prevent evaporation and contamination. Preparation of Citric Acid Crosslinking Solution An 8% (w/v) citric acid solution was prepared and was used as a crosslinking agent in place of BTCA to facilitate the covalent binding of aloe vera bioactives to the cellulosic hydroxyl groups of the cotton fabric (Ali et al., 2014), with the expectation that similar esterification chemistry would also promote surface adhesion on the polyester. Fabric Preparation and Pad-Dry-Cure Treatment Cotton and polyester fabrics were cut into equal-sized squares (10 mm × 10 mm), producing 30 samples of each fabric type (six concentration groups × five replicates each). All samples were washed and dried
Figure 4: Diagram of the process of the pad-dry-cure method to create treated fabrics (Source: ResearchGate, 2023)
Agar Plate Preparation and Antifungal Testing Sterile agar plates were prepared and separately inoculated with Kluyveromyces marxianus and Saccharomyces boulardii, both of which were selected for their documented susceptibility to antifungal compounds and capacity to grow at temperatures approximating skin-contact textile conditions (approximately 35°C) (Barone et al., 2025; Lim et al., 2019). Treated fabric samples from each concentration group were placed centrally on the surface of inoculated agar plates and incubated at 25– 30°C for 24 hours. Following incubation, zones of inhibition (ZOI) surrounding each fabric sample were measured in millimetres using vernier calipers from the top-right corner of each square. Reliability Each condition was repeated three times to ensure the reliability and consistency of results. Mean ZOI values were calculated for each concentration and substrate combination.
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Results Zones of inhibition were recorded across all concentration groups for both species and both substrates, with the exception of several near-zero values in cotton groups. Polyester substrates consistently produced larger inhibition zones than cotton across all concentrations for both species. Results are summarised in Tables 1 and 2. Table 1: Mean zone of inhibition (mm) for Kluyveromyces marxianus across aloe vera concentrations and fabric substrates. Aloe vera concentration (%) 0 25 50 75 100
Average Cotton ZOI (mm)
Average Polyester ZOI (mm)
0.00 0.08 0.10 0.13 0.70
0.67 0.75 1.42 1.46 0.83
Table 2: Mean zone of inhibition (mm) for Saccharomyces boulardii across aloe vera concentrations and fabric substrates. Aloe vera concentration (%) 0 25 50 75 100
Average Cotton ZOI (mm)
Average Polyester ZOI (mm)
0.25 0.29 0.00 0.06 0.83
1.00 1.67 2.67 0.03 1.50
For K. marxianus on polyester, ZOI increased from 0.67 mm at 0% to a peak of 1.46 mm at 75%, before declining to 0.83 mm at 100% (Figure 5). For S. boulardii on polyester, ZOI increased from 1.00 mm at 0% to a maximum of 2.67 mm at 50%, then fell sharply to 0.03 mm at 75% before partially recovering to 1.50 mm at 100% (Figure 6). Cotton substrates produced consistently smaller ZOI values, ranging from 0.00 mm to 0.83 mm across all groups, with the S. boulardii cotton group recording 0.00 mm at 50% — lower than both the 0% control (0.25 mm) and 25% concentration (0.29 mm). An F-test was performed, and it was found there was no significant statistical difference as the p-values of all the samples were > 0.05. The p-value corresponding to the F-statistic of one-way ANOVA is higher than 0.05, suggesting that the treatments are not significantly different for that level of significance (refer to Table 3) Table 3: One-way ANOVA test values (F-value and pvalue) Fabric Cotton Polyester Cotton Polyester
Fungi Species K. marxianus K. marxianus S. boulardii S. boulardii
F-value 1.0129 0.8571 0.9336 1.7169
p-value 0.3922 0.4488 0.4199 0.2209
Discussion
Figure 5: Aloe Vera efficiency against K.marxianus at increasing concentrations in cotton and polyester
Figure 6: Aloe Vera efficiency against S.boulardii at increasing concentration in cotton and polyester
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Concentration-Response Patterns The results of this investigation partially support the hypothesis that aloe vera extract exhibits concentration-dependent antifungal activity against yeast species relevant to skin-contact textile environments. Measurable zones of inhibition were recorded at multiple concentrations, with polyester substrates consistently producing larger inhibition zones than cotton. However, the dose-response relationship was not uniformly monotonic across all conditions, and several anomalous results and limitations in the methodology complicate a straightforward interpretation. The non-monotonic patterns observed — most notably the sharp decline in ZOI for S. boulardii on polyester from 2.67 mm at 50% to 0.03 mm at 75% — suggest that factors beyond simple active compound concentration influenced the experimental outcomes. At higher concentrations, the increased viscosity of the aloe vera gel may have reduced the rate of diffusion through the agar medium, limiting the effective spread of bioactive compounds and producing smaller inhibition zones despite a greater absolute quantity of antifungal agents being present on the disc (Naseem et al., 2020). This is a recognised limitation of disc diffusion methodology (Andrews,
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2001): the assay measures the capacity of a compound to diffuse through agar, not its bioavailability on a fabric surface, meaning that highly viscous extracts may underperform relative to their true inhibitory potential under real application conditions (Biemer, 1973). Phytochemical Basis of Antifungal Activity The antifungal activity observed can be explained by the phytochemical composition of Aloe barbadensis miller. Aloe vera contains over 75 bioactive compounds, including anthraquinones (aloin and aloeemodin), saponins, flavonoids, and polysaccharides such as acemannan (Radha & Laxmipriya, 2015). A comprehensive analysis of aloe vera phytochemistry identifies multiple bioactive groups including phenolic compounds, flavonoids and polysaccharides responsible for its multifunctional biological activity (Kar & Bera, 2018). Anthraquinones are the primary agents responsible for antifungal activity: they intercalate with fungal cell membranes, disrupting lipid bilayer integrity and inhibiting key metabolic enzymes required for hyphal growth and spore germination. Saponins act synergistically as biosurfactants, reducing membrane surface tension and causing irreversible cell lysis. Jarrar et al. (2025) demonstrated that whole-leaf aloe extract — which retains the anthraquinone-rich latex fraction — produced a significantly lower minimum inhibitory concentration against Candida albicans (approximately 75.42 mg/mL) compared to gel-only extract (approximately 184.93 mg/mL), confirming that anthraquinone concentration is a primary determinant of antifungal potency. This supports the general trend of increasing ZOI with concentration observed on polyester substrates. Substrate-Dependent Differences Cotton substrates produced consistently smaller ZOI values than polyester across both species. This result is likely attributable to the hydrophilic and absorptive nature of the cotton substrate: cotton fibres can absorb and retain the aqueous extract within the fabric matrix, reducing the quantity of bioactive compounds that diffuse outward into the agar zone where inhibitory activity is measured (Callewaert et al., 2021). However, this finding should not be interpreted as meaning polyester is the superior substrate for a functional finish in real textile applications. Ali et al. (2014) confirmed via Fourier Transform Infrared (FTIR) spectroscopy that aloe vera bioactives bond covalently to hydroxyl groups in cotton cellulose when facilitated by crosslinking agents, potentially producing more durable and bioavailable finishes on cotton under repeated laundering conditions than the disc diffusion assay is capable of detecting. Similarly,
Mondal and Saha (2019) demonstrated that aloe vera modified cotton fabrics exhibited enhanced antimicrobial properties, supporting the feasibility of aloe-based textile finishing. The small ZOI values recorded in the 0% control conditions for both species and both substrates likely reflect natural variability in yeast coloantiny growth rates and measurement error rather than genuine antifungal activity, highlighting the importance of larger replicate numbers in controlling for biological variation. Ecological Relevance of Model Organisms The selection of K. marxianus and S. boulardii as model organisms was methodologically justified by their documented susceptibility to antifungal compounds in standard disc diffusion assays and their capacity for growth at temperatures approximating skin-contact textile conditions (approximately 35°C), making them ecologically relevant proxies for fungi likely to colonise warm, humid textile microenvironments (Barone et al., 2025; Lim et al., 2019). Owen and Laird (2020) established that bodyworn textiles create precisely these conditions — warm, humid, and enriched with organic substrates such as sebum and desquamated skin cells — that favour fungal proliferation and associated fibre degradation, odour formation, and potential disease transmission. The inhibition of these yeast species by aloe vera therefore has direct applied relevance to antimicrobial textile development. Limitations Several limitations must be acknowledged. First, disc diffusion assays measure diffusion of a soluble extract through agar, not the behaviour of a bound fabric finish; wash durability of aloe vera finishes has been demonstrated at up to 50 cycles at 5 g/L (Jothi, 2008) but was not evaluated here. Second, the low replicate number per condition reduced statistical power and was insufficient to determine whether anomalous data points — such as the absence of inhibition for S. boulardii on cotton at 50% — represent genuine biological effects or experimental error. Third, the concentration series did not extend far enough at intermediate intervals to precisely define the relationship between anthraquinone delivery and inhibition, making it difficult to establish a true minimum inhibitory concentration for either species. Fourth, testing was limited to two yeast species; inclusion of clinically significant dermatophytes such as Trichophyton rubrum — the primary causative agent of tinea pedis, transmitted almost exclusively through contaminated textiles (Leoty-Okombi et al.,
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2022) — would have strengthened the translational relevance of the findings.
Conclusion This investigation examined the antifungal activity of aloe vera extract at five concentrations (0%, 25%, 50%, 75%, and 100%) against K. marxianus and S. boulardii on cotton and polyester substrates using disc diffusion assays, to determine whether a concentration-dependent relationship exists that could inform sustainable antimicrobial textile finishing. The results demonstrated measurable antifungal activity across multiple concentrations, with polyester consistently producing larger zones of inhibition than cotton. However, the dose-response relationship was non-monotonic in several conditions — particularly for S. boulardii on polyester, where inhibition peaked at 50% (2.67 mm) before declining sharply at 75% (0.03 mm) and partially recovering at 100% (1.50 mm). These findings partially support the hypothesis that aloe vera possesses concentration-dependent antifungal activity, though the relationship is more complex than a simple linear trend, and the influence of extract viscosity on agar diffusion at higher concentrations must be considered when interpreting the data, though other factors such as contamination need to account for the disparity of results between 75% and 100% concentrations. The antifungal efficacy of aloe vera is primarily attributable to its anthraquinone and saponin content, which disrupts fungal cell membrane integrity and induces cell lysis at sufficient concentrations (Radha & Laxmipriya, 2015; Jarrar et al., 2025). This phytochemical basis distinguishes aloe vera from conventional synthetic antimicrobial textile agents such as triclosan and quaternary ammonium compounds, which carry environmental concerns including endocrine disruption, aquatic toxicity, and wastewater accumulation (Mercier et al., 2024). The present findings contribute to a growing body of evidence supporting plant-derived antimicrobial finishes as more ecologically sustainable alternatives capable of maintaining antifungal efficacy (Khan et al., 2024; Vigneshwaran et al., 2025). While significant limitations relating to wash durability testing, the non-monotonic concentration response, the absence of dermatophyte testing, and the translational gap between agar diffusion and fabric application must be addressed in future work, this study provides foundational concentration-response data to guide further development of aloe vera as a functional and sustainable antimicrobial finish for skin-contact textiles.
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Declaration of use of AI Artificial intelligence (AI) was used in specific and limited ways during the preparation of this scientific report. The AI tool Claude was used to assist in drafting and refining the discussion and conclusion sections, improving the clarity of the components to align with the scientific question and hypothesis. Claude was further used in organising the literature review. The experimental data tables, all cited sources, and the scientific argument structure were provided by the student. ChatGPT was used to ensure the discussion addressed all required elements — interpretation and explanation of results, identification of limitations, and future research directions — and to improve alignment with Year 12 Science Extension conventions. All factual claims, citations, and interpretations of experimental results reflect the student’s own analysis. Claude did not generate the research question, design the experiment, collect or analyse data, or produce any references. The AI tool ChatGPT was used to assist with drafting the methodology subheadings and calculating the aloe vera mass-to-distilled-water ratios for each concentration. All content was reviewed and edited by the student after AI-assisted drafting to ensure accuracy and alignment with actual experimental results. No AI tools were used to fabricate or select references or data. AI was also utilised in this declaration to provide an objective summary of AI usage for this report.
Acknowledgements I would like to acknowledge Mrs. Kirsten Prior, Mrs. Virginia Ellis, Mrs. Kathy Haigh and Dr. Matthew Hill for their support in the practical and literary components of the methodology, as well as their aid in structuring and refining this scientific report.
References Akhoundi, M., Nasrallah, J., Marteau, A., Chebbah, D., Izri, A., & Brun, S. (2022). Effect of Household Laundering, Heat Drying, and Freezing on the Survival of Dermatophyte Conidia. Journal of Fungi, 8(5), 546. https://doi.org/10.3390/jof8050546 Ali, S. W., Purwar, R., Joshi, M., & Rajendran, S. (2014). Antibacterial properties of Aloe vera gel-finished cotton fabric. Cellulose, 21(3), 2063–2072. https://doi.org/10.1007/s10570-014-0175-9 Balachandran, S. (2023, September 1). Mold and Mildew on Textiles. Mci.si.edu. https://mci.si.edu/mold-and-mildewtextiles Belhaj Khalifa, I., & Ladhari, N. (2019). Hydrophobic behavior of cotton fabric activated with air atmosphericpressure plasma. The Journal of the Textile Institute, 111(8), 1191–1197. https://doi.org/10.1080/00405000.2019.1688907
Scientific Research in School Volume 8 Issue 1 2026 Bibi, A., Gul Afza, Afzal, Z., Farid, M., Sajjad Hussain Sumrra, Muhammad Asif Hanif, Kama, K., & Zubair, M. (2024). Synthetic vs. natural antimicrobial agents for safer textiles: a comparative review. RSC Advances, 14(42), 30688–30706. https://doi.org/10.1039/d4ra04519j
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Mondal, Md. I. H., & Saha, J. (2019). Antimicrobial, UV Resistant and Thermal Comfort Properties of Chitosan- and Aloe vera-Modified Cotton Woven Fabric. Journal of Polymers and the Environment, 27(2), 405–420. https://doi.org/10.1007/s10924-018-1354-9
Editor BCSRJ. (2020, August 5). ANTIFUNGAL AND ANTIBACTERIAL ACTIVITY OF ALOE VERA PLANT EXTRACT. Academia.edu. https://www.academia.edu/43785840/ANTIFUNGAL_AN D_ANTIBACTERIAL_ACTIVITY_OF_ALOE_VERA_P LANT_EXTRACT Fatma Filiz Yıldırım, Ozan Avinc, Arzu Yavas, & Gökcin Sevgisunar. (2020). Sustainable Antifungal and Antibacterial Textiles Using Natural Resources. Sustainable Textiles, 111–179. https://doi.org/10.1007/978-3-03038541-5_5 Jothi, D. (2009). Experimental study on antimicrobial activity of cotton fabric treated with aloe gel extract from Aloe vera plant for controlling the Staphylococcus aureus (bacterium). African Journal of Microbiology Research, 3(5), 228–232. https://academicjournals.org/article/article1380185091_Jot hi.pdf Kar, S. K., & Bera, T. K. (2018, April 1). Phytochemical constituents of aloe vera and their multifunctional properties: A comprehensive review . International Journal of Pharmaceutical Sciences and Research. https://ijpsr.com/bft-article/phytochemical-constituents-ofaloe-vera-and-their-multifunctional-properties-acomprehensive-review/?view=fulltext Khalifa, I. B. (2019). Molecular structure of cotton fibre. In Hydrophobic behavior of cotton fabric activated with air atmospheric-pressure plasma. https://www.researchgate.net/figure/Molecular-structureof-cotton-fiber_fig2_337289304 Lang, S. S. (2001, February 5). Cotton clothes found to be leading carrier of fungal spores, a scourge to some hospital patients with damaged immune systems. Cornell Chronicle. https://news.cornell.edu/stories/2001/02/cotton-clothescarry-fungal-spores-hospitals Lim, H., Kim, D.-H., Jeong, D., Kang, I.-B., Kim, H., & Kim, H. (2019). Biochemical characteristics, virulence traits and antifungal resistance of two major yeast species isolated from kefir:Kluyveromyces marxianusandSaccharomyces unisporus. 72(2), 275–281. https://doi.org/10.1111/1471-0307.12582
Morais, D., Guedes, R., & Lopes, M. (2016). Antimicrobial Approaches for Textiles: From Research to Market. Materials, 9(6), 498. https://doi.org/10.3390/ma9060498 Oliveira, C. S., Rocha, A., Antunes, J., Silva, A., Silva, C., & Tavaria, F. K. (2025). Boosting textiles with plant extracts: an advanced sustainable antimicrobial innovation for direct skin applications. 3 Biotech, 15(9), 300. https://doi.org/10.1007/s13205-025-04475-3 Owen, L., & Laird, K. (2020). The role of textiles as fomites in the healthcare environment: a review of the infection control risk. PeerJ, 8, e9790. https://doi.org/10.7717/peerj.9790 Sawalha, H., Yousef, H., Shalabi, R., Hamamreh, R., Kmail, A., & Qoraan, I. (2025). Phytochemical Analysis and Antifungal Activity of Aloe vera Extract Against Candida albicans. Scientifica, 2025(1). https://doi.org/10.1155/sci5/4496097 Taha, A. S., Ibrahim, Wael A. A. Abo-Elgat, AbdelMegeed, A., Mohamed, & Mamoun S. M. Abd El-Kareem. (2023). GC–MS, quantum mechanics calculation and the antifungal activity of river red gum essential oil when applied to four natural textiles. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-45480-x Verma, A., Ahuja, S., & Arora, S. (2023a). Advancements in Enhancing Antibacterial Properties of Cotton Fabric through Chitosan and Nanoparticles. ChemistrySelect, 8(44). https://doi.org/10.1002/slct.202303215 Verma, A., Ahuja, S., & Arora, S. (2023b). Illustration of Pad Dry Technique. In Advancements in Enhancing Antibacterial Properties of Cotton Fabric through Chitosan and Nanoparticles. https://www.researchgate.net/figure/Illustration-of-the-PadDry-Cure-Technique_fig5_375896742 Nigam, P. K., & Saleh, D. (2023). Tinea Pedis. Nih.gov; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK470421/
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Antimicrobial activity of oil extracted from Melaleuca quinquenervia bark against Enterococcus faecium and Kluyveromyces marxianus Emmett Daly Barker College The increasing prevalence of antimicrobial resistance has generated interest in plant-derived compounds as potential alternative antimicrobial agents. This investigation examined the antibacterial and antifungal effects of essential oil extracted from the bark of Melaleuca quinquenervia (paperbark) against Enterococcus faecium and Kluyveromyces marxianus. Essential oil was extracted from powdered bark using steam distillation and its antimicrobial activity was assessed using a disc diffusion assay. Following incubation, zones of inhibition surrounding oiltreated discs were measured and compared between the two microorganisms. The essential oil inhibited the growth of both species, producing mean zones of inhibition of 10.0 mm for E. faecium and 10.4 mm for K. marxianus. A two-sample t-test indicated no statistically significant difference between the antimicrobial effects observed for the bacterial and fungal species (t = −0.492, p = 0.6357). These findings support the hypothesis that M. quinquenervia bark contains compounds with antimicrobial properties. However, limitations associated with extraction consistency, the presence of residual water in the oil sample, and insufficient control replicates reduced the reliability of the results. Further research incorporating improved extraction methods, increased replication, and chemical analysis of the oil is recommended to confirm the antimicrobial potential of M. quinquenervia bark essential oil.
Literature Review For at least 60,000 years, Indigenous Australians have developed extensive knowledge of the Australian environment and its medicinal resources. Among the plants of cultural and medicinal significance is M quinquenervia (paperbark tree), which has been used traditionally for shelter construction, canoe manufacture, food and water containers, and wound treatment. The bark can be readily removed in large sheets, making it particularly useful as a natural bandaging material. Traditional medicinal applications are attributed to the presence of bioactive essential oils within the bark and leaves, which exhibit antimicrobial and anti-inflammatory properties and have historically been used to treat wounds, infections, and respiratory conditions (Guerrero, 2024). Increasing concern regarding antibiotic resistance has stimulated interest in natural antimicrobial agents, particularly plant-derived essential oils. Essential oils have demonstrated a wide range of biological activities, including antimicrobial, antifungal, antioxidant, and anti-inflammatory effects (PezantesOrellana et al., 2024). Despite extensive scientific investigation of Australian essential oils such as those derived from M. alternifolia (tea tree) and Eucalyptus species, comparatively little research has focused on
M. quinquenervia. The traditional use of paperbark as a wound dressing suggests the presence of biologically active compounds capable of inhibiting microbial growth, warranting further investigation of its antimicrobial properties. M. quinquenervia is a small to medium-sized tree, typically reaching heights of 4–12 m, and is native to eastern Australia, occurring from Towra Point in New South Wales to Cape York Peninsula in Queensland, with extensions into Papua New Guinea (Ireland et al., 2002). Previous studies have identified two distinct chemotypes of M. quinquenervia. Chemotype 1 is characterised by high concentrations of E-nerolidol (74–95%) and linalool (14–30%) and is primarily distributed between Sydney and Selection Flat in New South Wales. Chemotype 2 contains varying proportions of 1,8-cineole (10–75%), viridiflorol (13– 66%), α-terpineol (0.5–14%), and β-caryophyllene (0.5–28%) and occurs throughout a broader geographical range extending from Sydney to Papua New Guinea and New Caledonia (Ireland et al., 2002). Chemotype 2 exhibits a more uniform distribution than Chemotype 1 and is commonly referred to as niaouli. Essential oils have been used medicinally by numerous cultures for thousands of years. Historical records indicate that aromatic oils were used by
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Scientific Research in School Volume 8 Issue 1 2026 ancient Egyptian civilisations as early as 4500 BC in cosmetics, ointments, and religious practices. Traditional Chinese and Indian medicinal systems documented the therapeutic use of hundreds of plantderived substances, including cinnamon, ginger, myrrh, and sandalwood, between 3000 and 2000 BC. During the eighteenth and nineteenth centuries, advances in chemistry enabled the identification and isolation of biologically active compounds from medicinal plants, including caffeine and morphine. Today, essential oils such as lavender and peppermint continue to be incorporated into pharmaceutical and therapeutic products. Within plants, essential oils are synthesised and stored in specialised secretory structures known as oil cells, where the oils accumulate within the cytoplasm. A major constituent of niaouli oil is 1,8-cineole (eucalyptol), a bicyclic monoterpenoid commonly found in the essential oils of numerous plant species, particularly eucalyptus. Eucalyptol typically comprises 30–90% of eucalyptus essential oils and 31–70% of niaouli oil (Mączka et al., 2021). Plantderived compounds such as eucalyptol are considered important sources for pharmaceutical discovery; between 1949 and 2009, approximately 80 new drugs were developed directly or indirectly from medicinal plants (Cai et al., 2021). Eucalyptol has been used in traditional Chinese, Indian, and Indigenous Australian medicine for the treatment of respiratory disorders, digestive conditions, fever, and pain (Hoch et al., 2023). Its antimicrobial activity is believed to result from increased microbial membrane permeability, leading to leakage of intracellular proteins and disruption of cellular function (Merghni et al., 2023a). The antibacterial activity of M. quinquenervia essential oil will be evaluated against E. faecium, a Gram-positive bacterium that forms part of the normal human intestinal microbiota but is also an important opportunistic pathogen. E. faecium is responsible for a range of healthcare-associated infections, including urinary tract infections, wound infections, and intraabdominal infections, with many strains exhibiting significant antibiotic resistance (Wei et al., 2024). Reports indicate that approximately 60% of enterococcal infections are acquired in healthcare settings, highlighting the need for alternative antimicrobial treatments. The antifungal activity of M. quinquenervia essential oil will be assessed against K. marxianus the sexual stage of Atelosaccharomyces pseudotropicalis. Although generally harmless in healthy individuals, K. marxianus is an opportunistic yeast capable of causing superficial fungal infections under certain conditions (Nwofor, 2025).
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Essential oil extraction will be conducted using steam distillation, the most widely used conventional method for isolating volatile plant compounds. Steam distillation is estimated to recover approximately 93% of available essential oils, with the remaining fraction requiring alternative extraction techniques (Masango, 2005). This method is advantageous because it produces relatively pure essential oils with reduced contamination and generally achieves higher extraction yields compared with other methods (Yani et al., 2014). However, steam distillation is timeintensive and often requires extended extraction periods. Furthermore, recovery efficiency may be reduced unless the condensed water is re-distilled to maximise essential oil yield.
Scientific Research Question To what extent does essential oil derived from Melaleuca quinquenervia bark exhibit antibacterial activity against Enterococcus faecium and antifungal activity against Kluyveromyces marxianus?
Scientific Hypothesis Essential oil extracted from Melaleuca quinquenervia bark will inhibit bacterial and fungal growth, as quantified by the zone of inhibition.
Methodology Essential Oil Extraction by Steam Distillation Bark samples were collected from the inner layer at the base of the M. quinquenervia. The inner bark was selected to minimise prior exposure to air and reduce the loss of volatile compounds through evaporation. The bark was cut into small pieces using scissors to increase the surface area available for extraction. The samples were then ground to a fine powder using a coffee grinder. A mass of 9.75 g of bark powder was weighed and transferred to a large round-bottom flask. Distilled water (123 mL) was added to the flask, which was subsequently connected to a steam distillation apparatus. The condenser was assembled so that cooling water flowed counter-current to the direction of vapour movement, maximising heat exchange efficiency. Steam distilled for 6 hours at boiling. Upon completion of the extraction, the distilled essential oil was collected and stored in a sterile container until further analysis. Oil amount was small so no further purification was undertaken. Antimicrobial Activity Assay Aseptic techniques were used prior to handling Agar plates. Agar plates were arranged on the work surface
Scientific Research in School Volume 8 Issue 1 2026 and kept closed to minimise airborne contamination. Each plate was labelled with the date, sample identification, microbial species, treatment, and control designation. Control plates remained sealed throughout the experiment. Cultures of E. faecium were aseptically sampled using a sterile pipette. One to two drops of microbial suspension were placed in the centre of each agar plate assigned to that species. The inoculum was distributed evenly across the agar surface using a sterile spreader to create a uniform microbial lawn. The spreader was sterilised between each plate to prevent crosscontamination. The inoculation procedure was repeated for cultures of K. marxianus. Following inoculation, the extracted essential oil was transferred to a sterile beaker. Sterile filter-paper discs were handled with sterile forceps and immersed in the oil until fully saturated. Oilsaturated disc were placed onto the surface of each inoculated agar plate. The discs were positioned to ensure complete contact with the agar surface. All plates were sealed with laboratory tape and incubated for 24 h at 37 degrees Celsius. Plates were incubated in an inverted position to prevent condensation from dripping onto the agar surface. Following incubation, the plates were removed and stored under refrigeration until measurements were conducted.
Table 1: Mean zones of inhibition produced by M. quinquenervia bark extract against E. faecium and K. marxianus. Species Control Enteroccocous faecium
Kluyveromyces marxianus
Zone of Inhibition (mm) 6 10 11 10 10 9 10 9 9 11 13
Mean (mm)
Standard Deviation
6
0
10
0.707107
10.4
1.67332
As shown in Table 1, the average zone of inhibition for K. marxianus was slightly greater than that observed for E. faecium. However, there was overlap in the ranges of inhibition measured for the two species. A two-sample t-test was conducted to compare the mean zones of inhibition. The analysis produced a test statistic of t = −0.492 and a p-value of 0.6357. At a significance level of α = 0.05, the null hypothesis was not rejected.
Measurement of Antimicrobial Activity Antimicrobial activity was assessed by measuring the zones of inhibition surrounding each filter-paper disc. The diameter of each inhibition zone was then measured and averaged The resulting data were then tabulated.
Results Table 1 summarises the zones of inhibition produced by the M. quinquenervia bark extract against E. faecium and K. marxianus. The extracted product consisted of a small volume of oil and possessed a characteristic aromatic odour. Zones of inhibition were observed for both microorganisms tested as seen in figure 1. The mean zone of inhibition for E. faecium was 10.0 mm, with recorded values ranging from 9 mm to 11 mm. The mean zone of inhibition for K. marxianus was 10.4 mm, with recorded values ranging from 9 mm to 13 mm.
Figure 1: Antimicrobial activity against E. faecium demonstrated by the formation of a zone of inhibition.
Discussion This investigation examined the antimicrobial activity of essential oil extracted from the bark of Melaleuca quinquenervia (paperbark) using steam distillation followed by a zone of inhibition assay. The results demonstrated that the extracted oil inhibited the growth of both E. faecium and Kluyveromyces marxianus, with mean zones of inhibition of 10.0 mm and 10.4 mm, respectively. These findings support the hypothesis that essential oil extracted from M. quinquenervia possesses antimicrobial properties.
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Scientific Research in School Volume 8 Issue 1 2026 The observed antimicrobial activity is likely attributable to the presence of 1,8-cineole (eucalyptol), a major constituent of niaouli oil. Niaouli oil typically contains between 10% and 75% 1,8cineole, a compound widely recognised for its antimicrobial effects (Moo et al., 2021). The inhibition of E. faecium, a species known to exhibit resistance to multiple antibiotics (Wei et al., 2024), suggests that the oil may be effective against certain antibioticresistant microorganisms. Although K. marxianus is not typically associated with antibiotic resistance, a comparable inhibitory effect was observed, indicating broad-spectrum antimicrobial activity. Statistical analysis revealed no significant difference between the inhibitory effects of the oil on the bacterial and fungal species tested. A two-sample ttest produced a p-value of 0.6357, indicating that the null hypothesis could not be rejected. The zones of inhibition ranged from 9–11 mm for E. faecium and 9–13 mm for K. marxianus, demonstrating similar susceptibility of both microorganisms to the extracted oil. The antimicrobial effects observed are consistent with the known biological activity of 1,8-cineole. As a monoterpene, 1,8-cineole exhibits strong antibacterial activity against a wide range of drug-resistant bacteria (Merghni et al., 2023). Its mechanism of action involves disruption of membrane permeability and increased membrane fluidity, resulting in altered membrane protein function and inhibition of cellular respiration. In fungi, 1,8-cineole has been shown to impair ergosterol biosynthesis, weaken cellular membranes, inhibit germ tube development, and compromise cell wall integrity (Singh et al., 2024). The antimicrobial activity observed in this investigation is therefore consistent with previously published literature describing the effects of 1,8cineole on microbial growth. Essential oil extraction was performed using steam distillation, a standard and widely accepted method for isolating volatile essential oils (Wong, 2014). Steam distillation selectively targets volatile compounds by vaporising them at temperatures below their normal boiling points. The vapours are subsequently condensed, producing a mixture of oil and water that can be separated for analysis (Distillation – An Overview | ScienceDirect Topics, n.d.). The use of this established extraction technique contributes to the validity of the investigation. However, the overall reliability of the investigation was limited by difficulties in reproducing the extraction procedure. Although the zone of inhibition
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assay produced relatively consistent results across the replicate plates, the steam distillation process did not consistently yield the same quantity of essential oil. Consequently, reliability across multiple independent oil samples could not be established. Several limitations were identified during the investigation. The inconsistent yield of essential oil may have resulted from incomplete disruption of oilcontaining cells within the bark tissue, reducing extraction efficiency. An alternative extraction technique, such as microwave-assisted hydrodistillation, may improve extraction yield and consistency, as previous studies have reported greater essential oil recovery using this method (Moradi et al., 2018). In addition, the study included insufficient control replicates, preventing robust statistical comparison between the treatment and control groups and limiting the ability to determine the significance of the observed antimicrobial effects. Recommendations and Future Research Future investigations should incorporate a negative control consisting of sterile water applied to the paper discs to determine whether any observed inhibition is attributable solely to the essential oil. Increasing the number of control and treatment replicates would improve statistical power and allow more robust evaluation of significance. To improve the purity of the extracted oil, double distillation should be considered, as the extracted samples appeared to contain residual water. The presence of water in the oil samples may have influenced the measured zones of inhibition and introduced variability into the results. Additionally, a portion of the extracted oil should be retained for chemical analysis using gas chromatography to quantify the concentration of active compounds, particularly 1,8-cineole. This would enable direct correlation between chemical composition and antimicrobial activity. Further refinement of the extraction protocol is also recommended to improve consistency and reproducibility. Greater emphasis should be placed on optimising extraction conditions before conducting biological assays. Repeating the entire investigation using multiple independently extracted oil samples and an increased number of control replicates would substantially improve the reliability and validity of the findings.
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Conclusion The results of this investigation indicate that essential oil extracted from the bark of Melaleuca quinquenervia exhibits antimicrobial activity against both Enterococcus faecium and Kluyveromyces marxianus. Mean zones of inhibition of 10.0 mm and 10.4 mm, respectively, were observed, supporting the hypothesis that paperbark bark oil possesses antimicrobial properties. No statistically significant difference was found between the effects on bacterial and fungal growth. While the findings are consistent with the known antimicrobial activity of 1,8-cineole, limitations associated with extraction consistency and insufficient controls reduce the strength of the conclusions. Further investigation employing improved extraction methods, increased replication, and chemical analysis of the oil is recommended to confirm and expand upon these findings.
Declaration of use of AI AI was used in this paper to assist my research. It was used to find scientific papers that supported or were relevant to my research. I then read through the papers and decided whether it was relevant to add in or not. It was also used to rewrite my sections of the paper. No information was acquired by AI and all information in the paper was found and put in. It was used to make sections sound more formal and less colloquial. It was used to enhance the clarity of Figure 1.
Acknowledgements The author gratefully acknowledges Mrs Zoe Tonge for her assistance with steam distillation techniques.
The author also thanks Mrs Kathy Haigh for her valuable contributions to the editing and review of the manuscript, and Mrs Kirsten Prior for her assistance with microbial culturing, plating procedures, and testing the antimicrobial activity of the extracted oil.
References Distillation—An overview | ScienceDirect Topics. (n.d.). Retrieved June 13, 2026, from https://www.sciencedirect.com/topics/agricultural-andbiological-sciences/distillation Merghni, A., Belmamoun, A. R., Urcan, A. C., Bobiş, O., & Lassoued, M. A. (2023). 1,8-Cineol (Eucalyptol) Disrupts Membrane Integrity and Induces Oxidative Stress in Methicillin-Resistant Staphylococcus aureus. Antioxidants, 12(7), 1388. https://doi.org/10.3390/antiox12071388 Moo, C.-L., Osman, M. A., Yang, S.-K., Yap, W.-S., Ismail, S., Lim, S.-H.-E., Chong, C.-M., & Lai, K.-S. (2021). Antimicrobial activity and mode of action of 1,8cineol against carbapenemase-producing Klebsiella pneumoniae. Scientific Reports, 11(1), 20824. https://doi.org/10.1038/s41598-021-00249-y Moradi, S., Fazlali, A., & Hamedi, H. (2018). MicrowaveAssisted Hydro-Distillation of Essential Oil from Rosemary: Comparison with Traditional Distillation. Avicenna Journal of Medical Biotechnology, 10(1), 22–28. Singh, K., Deepa, N., Chauhan, S., Tandon, S., Verma, R. S., & Singh, A. (2024). Antifungal action of 1,8 cineole, a major component of Eucalyptus globulus essential oil against Alternaria tenuissima via overproduction of reactive oxygen species and downregulation of virulence and ergosterol biosynthetic genes. Industrial Crops and Products, 214, 118580. https://doi.org/10.1016/j.indcrop.2024.118580 xpert, chem. (2014, March 28). Extraction of Essential Oil from Cinnamon (Cinnamomum Zeylanicum). Oriental Journal of Chemistry. https://www.orientjchem.org/vol30no1/extraction-ofessential-oil-from-cinnamon-cinnamomum-zeylanicum/
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How PET (polyethylene terephthalate) Microplastics & Motor Oil Effects OEM (Pseudomonas, Flavobacterium, Arthrobacter and Azotobacter genera) Growth Peppa Chen Barker College This investigation examined how polyethylene terephthalate, PET, glitter and motor oil affected the growth and metabolic activity of Oil Eating Microbes, OEM. The organisms used were specifically Pseudomonas, Flavobacterium, Arthrobacter and Azotobacter genera. OEM bacterial cultures were exposed to three treatment conditions: water control, PET glitter and motor oil for 24 hours. Bacterial growth was measured using colony counts, while metabolic activity was measured using tetrazolium absorbance at 500 nm. PET glitter produced the highest average colony count at 392.17, followed by motor oil at 382.67 and the water control at 313.50. However, motor oil produced the highest average absorbance at 0.167, followed by the water control at 0.163 and PET glitter at 0.145. These results partially supported the hypothesis, suggesting that PET glitter and motor oil affected OEM bacteria differently. PET glitter was associated with greater viable bacterial growth, while motor oil was associated with higher metabolic activity.
Literature Review Oil-eating microorganisms are important in environmental science because some bacteria can survive in polluted environments and respond to petroleum-based contaminants. Certain bacteria can break down hydrocarbon compounds found in crude oil, diesel and motor oil through biodegradation, a process used in bioremediation to reduce environmental contamination. Head, Jones, and Röling (2006) explain that microbial communities play an important role in oil degradation, as some bacteria can metabolise oil-related compounds. However, bacterial responses to oil depend on factors such as oxygen availability, nutrients, temperature and the bacterial species present. The oil-eating microbial culture used in this investigation was a mixed OEM culture. According to the EDVO-Kit #956 Bioremediation by Oil-Eating Bacteria manual, the freeze-dried OEM mixture contains bacteria from the Pseudomonas, Flavobacterium, Arthrobacter and Azotobacter genera (EDVOTEK, 2015). These genera are relevant because mixed microbial cultures may respond differently to environmental contaminants compared with a single bacterial species. Petroleum-based substances such as motor oil are chemically complex and may affect bacteria in more than one way. Khalid et al. (2021) explain that petroleum biodegradation often involves different microorganisms contributing to different stages of oil breakdown.
In this investigation, motor oil was included as a petroleum-based treatment because it may influence OEM bacterial growth and metabolic activity. A positive bacterial response to motor oil could result in increased colony counts or greater tetrazolium absorbance. However, motor oil may also create stressful conditions by limiting oxygen availability, altering the physical environment of the culture, or exposing bacteria to toxic compounds. Therefore, its effect on OEM bacteria cannot be assumed and must be tested experimentally. Polyethylene terephthalate, or PET, is a petroleumderived plastic polymer used in products such as bottles, packaging and synthetic materials. Unlike motor oil, PET exists as a stable solid polymer. Andrady (2011) and Webb et al. (2013) explain that many plastics are resistant to rapid microbial breakdown because of their chemical structure. Therefore, PET glitter is not expected to act as an immediate nutrient source for OEM bacteria during a short 24-hour incubation period. Instead, PET glitter may influence bacteria physically by providing surfaces for attachment. Zettler, Mincer, and Amaral-Zettler (2013) describe microbial communities that form on plastic debris as the “Plastisphere”, showing that plastic particles can support bacterial attachment and community formation. Similarly, Oberbeckmann, Löder, and Labrenz (2015) explain that microbial colonisation of plastic surfaces can be influenced by plastic type, surface properties, exposure time and environmental
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Scientific Research in School Volume 8 Issue 1 2026 conditions. This is relevant because PET glitter may have increased the surface area available for bacterial attachment, even if it was not degraded as a food source. Microplastics may also influence bacterial environments by interacting with surrounding chemicals. Rochman, Hoh, Hentschel, and Kaye (2013) found that plastic particles can sorb organic contaminants from water, meaning they may change the chemical conditions around microorganisms. Wright, Langille, and Walker (2021) also found that plastic-associated microbial communities can be diverse and may include bacteria linked with hydrocarbon degradation. These studies are relevant because they show that plastic particles can influence microbial communities, even when the plastic itself is not used as a direct food source. This investigation measured two dependent variables: colony count and tetrazolium absorbance. Colony count estimates the number of viable bacteria able to grow into visible colonies on agar plates, while tetrazolium absorbance estimates metabolic activity. The EDVOTEK manual explains that tetrazolium indicator is converted into a coloured product by metabolically active bacteria (EDVOTEK, 2015). In this investigation, absorbance was measured at 500 nm, with higher absorbance generally indicating greater metabolic activity. Overall, the literature suggests that motor oil and PET glitter may affect OEM bacteria in different ways. Motor oil may influence OEM bacteria because of its petroleum-based hydrocarbon composition, while PET glitter is more likely to affect bacteria by providing surfaces for attachment or short-term survival. Therefore, this investigation compares water, PET glitter and motor oil to determine how each treatment affects OEM bacterial growth and metabolic activity after 24 hours of incubation.
Scientific Research Question To what extent does exposure to different treatment substances, including deionised water, PET glitter, and motor oil, affect the growth and metabolic activity of OEM bacterial cultures after 24 hours of incubation, as measured through colony count and tetrazolium absorbance at 500 nm?
Scientific Hypothesis It was hypothesised that if OEM bacterial cultures were exposed to different crude-oil-derived treatments, then cultures treated with motor oil would show greater bacterial growth and metabolic activity
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than cultures treated with PET glitter or distilled water. Variables The independent variable was the treatment substance added to the OEM culture: water control, PET glitter or motor oil. The dependent variables were bacterial colony count and tetrazolium absorbance at 500 nm. Controlled variables included incubation duration, OEM culture volume, tetrazolium volume, incubation temperature, dilution method, plating volume and agar incubation time.
Methodology PET glitter and motor oil were used as treatment substances to compare the response of OEM bacteria to a solid petroleum-derived plastic and a liquid petroleum-derived product. A sterile measuring cylinder was used to measure 250 mL of distilled water into a sterile capped bottle. Then, 0.5 g of OEM culture powder was added to the bottle. A sterile magnetic stirrer was placed into the bottle, and the cap was left slightly loose to allow oxygen to enter. The bottle was labelled with the date, culture type and time, then placed on a rocker at 200 rpm for 16–20 hours to activate the OEM culture. After activation, nine sterile conical flasks were labelled into three treatment groups: OEM with water control, OEM with PET glitter, and OEM with motor oil. Each treatment group contained three replicate flasks. While the OEM culture was being stirred, 25 mL of the prepared culture was dispensed into each flask using sterile technique. Then, 0.5 mL of tetrazolium indicator was added to each flask using an adjustable micropipette. The water control group received 1 mL of distilled water. The motor oil group received 1 mL of motor oil. The PET glitter group received a 0.79g of PET glitter. All nine flasks were sealed, labelled and incubated at room temperature for 24 hours. After incubation, the appearance of each culture group was observed and recorded. A dilution was then prepared for each sample. While stirring, 100 µL of sample liquid was transferred into 100 mL of distilled water. This process was repeated for each flask. A 10 µL sample of the diluted liquid was then transferred onto agar plates and lawn plated using sterile technique. The plates were left to sit for 5 minutes, inverted, and incubated at 37°C for up to 24 hours.
Scientific Research in School Volume 8 Issue 1 2026 After incubation, visible colonies were counted and recorded. Tetrazolium absorbance was also measured at 500 nm using a colorimeter or spectrophotometer. The colony count and absorbance data were then used to calculate treatment averages for water control, PET glitter and motor oil.
& Amaral-Zettler, 2013). Oberbeckmann, Löder, and Labrenz (2015) also explain that microbial colonisation of plastic surfaces can be influenced by plastic type, surface properties and exposure time. Therefore, PET glitter most likely acted as a physical surface rather than a nutrient source.
Results
This explanation also helps explain why PET glitter produced the highest colony count but the lowest tetrazolium absorbance. The glitter may have helped bacteria attach or survive, but it did not provide an accessible carbon source that increased metabolic activity. Since PET is resistant to rapid degradation, a 24-hour incubation period was not long enough to determine whether OEM bacteria could degrade the plastic.
Table 1: Colony counts of OEM bacteria after 24 hours of incubation under different treatments Treatment Flask Water control PET glitter Motor oil
1 2 3 1 2 3 1 2 3
Replicate 1 297 347 570 215 330 487 889 87 295
Colony Count Replicate 2 220 310 137 201 377 743 621 124 280
Average 313.50 392.17 382.67
Figure 1: Mean colony count of OEM bacteria after 24 hours of incubation under water control, PET glitter and motor oil treatments. Error bars show standard deviation
Discussion The results partially supported the hypothesis. Motor oil produced the highest average tetrazolium absorbance, suggesting slightly greater metabolic activity. However, it did not produce the highest colony count. Instead, PET glitter produced the highest average colony count. This suggests that PET glitter and motor oil affected OEM bacteria in different ways. PET glitter produced the highest average colony count, which suggests that it may have supported the survival or growth of viable OEM bacteria during the 24-hour incubation period. However, this does not prove that OEM bacteria degraded PET plastic. PET is a chemically stable polymer and is unlikely to be broken down within such a short time frame. A more likely explanation is that PET glitter provided extra surfaces for bacterial attachment. This is supported by the concept of the “Plastisphere”, where microbial communities form on plastic debris (Zettler, Mincer,
Motor oil produced the highest average tetrazolium absorbance, which suggests that it was associated with slightly higher metabolic activity. This may be because the OEM culture contained bacterial genera associated with oil degradation, including Pseudomonas, Flavobacterium, Arthrobact er and Azotobacter (EDVOTEK, 2015). These bacteria may have responded to hydrocarbon compounds in the motor oil. However, the absorbance difference between motor oil and the water control was very small, only 0.004 absorbance units. This means that motor oil did not strongly increase metabolic activity under the conditions of this experiment. Motor oil also showed the largest spread in colony count data, which reduces the reliability of the average. This variation may have occurred because motor oil is hydrophobic and does not mix evenly in water. As a result, some samples may have contained more oil droplets than others, affecting bacterial exposure, dilution and plating accuracy. Motor oil may also have created stressful conditions by reducing oxygen availability or exposing bacteria to toxic compounds. Therefore, motor oil may have increased metabolic activity in some bacteria while limiting growth or survival in others. A key limitation of this investigation was the large variation between replicate flasks. The motor oil colony counts ranged from 87 to 889, while the PET glitter counts ranged from 201 to 743. This suggests that the treatments may not have been evenly distributed during sampling. Uneven glitter distribution, inconsistent bacterial attachment to glitter particles, and uneven motor oil mixing may have affected the results.
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Scientific Research in School Volume 8 Issue 1 2026 Another limitation was that the investigation only measured bacterial response after 24 hours. Bacterial populations change over time through lag, exponential, stationary and death phases, so one time point cannot show the full growth pattern. The OEM culture also contained multiple bacterial genera, meaning the experiment could not identify which bacteria responded most strongly to each treatment. In addition, commercial PET glitter may contain coatings or additives, meaning it may not have represented pure PET plastic.
activity, although the difference from the control was small.
Future research should measure bacterial growth and metabolic activity over a longer time period, such as 24, 48, 72 and 96 hours. More replicate flasks should also be used to improve reliability. Stronger mixing methods could reduce uneven motor oil distribution, and PET particles with consistent size and composition could improve control of the microplastic treatment. Testing PET glitter and motor oil together would also be useful because plastics and oil-based pollutants can occur together in contaminated environments.
Declaration of use of AI
Overall, the results suggest that PET glitter and motor oil caused different short-term responses in OEM bacteria. PET glitter was associated with greater viable bacterial growth, possibly due to surface attachment, while motor oil was associated with slightly higher metabolic activity. However, the small differences and large spread in some data mean that the results should be interpreted carefully.
Conclusion This investigation examined how PET glitter and motor oil affected OEM bacterial growth and metabolic activity after 24 hours of incubation. Bacterial growth was measured through colony counts, while metabolic activity was measured through tetrazolium absorbance at 500 nm. PET glitter produced the highest average colony count at 392.17, followed by motor oil at 382.67 and the water control at 313.50. This suggests that PET glitter may have supported a greater number of viable bacteria, possibly by providing surfaces for bacterial attachment or short-term survival. However, this does not prove that OEM bacteria degraded PET plastic. The tetrazolium absorbance results showed a different trend. Motor oil produced the highest average absorbance at 0.167, followed closely by the water control at 0.163, while PET glitter produced the lowest average absorbance at 0.145. This suggests that motor oil was associated with slightly higher metabolic
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Therefore, the hypothesis was partially supported. Motor oil produced the greatest metabolic activity, but PET glitter produced the greatest colony count. Overall, the findings suggest that PET glitter and motor oil affected OEM bacteria differently, but further research over a longer incubation period would be needed to determine whether these effects continue over time.
Artificial intelligence was used as an assisting tool during the development of this report. AI assisted with identifying possible peer-reviewed sources relevant to OEM bacteria, hydrocarbon degradation, PET microplastics, biofilm formation, colony counts and tetrazolium absorbance. These suggested sources were then checked alongside my own Google Scholar research to ensure that they were suitable, reliable and relevant to the investigation. AI was also used to help organise the literature review, refine scientific wording, and improve the clarity of explanations in the results, discussion and conclusion sections. AI was not used to generate the experimental data. The colony count and tetrazolium absorbance results were collected from the practical investigation and recorded in the results tables. The interpretation of the results was based on the collected data and supported by scientific literature. All AI-assisted sources included in the reference list were reviewed for suitability before being used in the report.
Acknowledgements I would like to acknowledge Mrs Virginia Ellis, Mrs Kirsten Prior and Mrs Kathy Haigh for their guidance and support throughout the investigation. I would also like to thank Dr Laura Elia for preparing laboratory equipment and assisting with microbiology techniques, and Dr Matthew Hill for supporting data analysis and further research development.
References Atlas, R. M., & Bartha, R. (1992). Hydrocarbon biodegradation and oil spill bioremediation. Advances in Microbial Ecology, 12, 287–338. https://link.springer.com/chapter/10.1007/978-1-46847609-5_6 Bailey, R. (2024, June 4). Phases of the bacterial growth curve. ThoughtCo. https://www.thoughtco.com/bacterialgrowth-curve-phases-4172692 Head, I. M., Jones, D. M., & Röling, W. F. M. (2006). Marine microorganisms make a meal of oil. Nature Reviews
Scientific Research in School Volume 8 Issue 1 2026 Microbiology, 4(3), https://www.nature.com/articles/nrmicro1348
173–182.
Khalid, F. E., Lim, Z. S., Sabri, S., Gomez-Fuentes, C., Zulkharnain, A., Ahmad, S. A., & Bacosa, H. P. (2021). Bioremediation of diesel contaminated marine water by bacteria: A review and bibliometric analysis. Journal of Marine Science and Engineering, 9(2), 155. https://www.mdpi.com/2077-1312/9/2/155 Oberbeckmann, S., Löder, M. G. J., & Labrenz, M. (2015). Marine microplastic-associated biofilms: A review. Environmental Chemistry, 12(5), 551–562. https://connectsci.au/en/articleabstract/12/5/551/24503/Marine-microplastic-associatedbiofilms-a-review?redirectedFrom=fulltext Rochman, C. M., Hoh, E., Hentschel, B. T., & Kaye, S. (2013). Long-term field measurement of sorption of organic contaminants to five types of plastic pellets: Implications for plastic marine debris. Environmental Science & Technology, 47(3), 1646–1654. https://pubs.acs.org/doi/abs/10.1021/es303700s Wright, R. J., Langille, M. G. I., & Walker, T. R. (2021). Food or just a free ride? A meta-analysis reveals the global diversity of the Plastisphere. The ISME Journal, 15, 789– 806. https://academic.oup.com/ismej/article/15/3/789/7474453
Zettler, E. R., Mincer, T. J., & Amaral-Zettler, L. A. (2013). Life in the “Plastisphere”: Microbial communities on plastic marine debris. Environmental Science & Technology, 47(13), 7137–7146. https://pubs.acs.org/doi/10.1021/es401288x EDVOTEK. (2015). EDVO-Kit #956: Bioremediation by oil-eating bacteria. EDVOTEK, Inc. https://www.edvotek.com/site/pdf/956.pdf Andrady, A. L. (2011). Microplastics in the marine environment. Marine Pollution Bulletin, 62(8), 1596– 1605. https://www.sciencedirect.com/science/article/pii/S0 025326X11003055?via=ihub Das, N., & Chandran, P. (2011). Microbial degradation of petroleum hydrocarbon contaminants: An overview. Biotechnology Research International, 2011, https://onlinelibrary.wiley.com/doi/10.4061/2011/941810 Marczak, H. (2022). Energy inputs on the production of plastic products. Journal of Ecological Engineering, 23(4), 146–156. https://www.jeeng.net/Impacts-ofAnthropogenic-Factors-on-the-Groundwater-Ecosystemof-Fezouata-in-South,146678,0,2.html Webb, H. K., Arnott, J., Crawford, R. J., & Ivanova, E. P. (2013). Plastic degradation and its environmental implications with special reference to poly(ethylene terephthalate). Polymers, 5(1), 1– 18. https://www.mdpi.com/2073-4360/5/1/1.
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Quantification of microplastic release from synthetic, natural and mixed fabrics during simulated washing Seraphina Herwono Barker College Microplastic release from textiles has become a huge environmental and potential health concern with the rise in fast fashion such as Shein and Temu. Microplastic particles in washing machine discharge are not captured by standard filtration systems, risking environmental contamination of aquatic ecosystems. Bioaccumulation through the food chain represents a likely pathway for indirect human exposure to these microplastics, though the effects of ingestion are poorly characterised. As the microfibres degrade, leaching of associated chemical additives and dyes may pose additional risks to both ecological and human health. This study investigates whether polar fleece releases more microplastics than polyester and cotton on the first launder. Water samples from a simulated wash were viewed under a binocular dissecting microscope at 4× magnification. Any visible strands and beads were manually counted and recorded. A one-way ANOVA test suggests no significant statistic difference in the shedding of microfibres between different fabrics (p=0.62). Qualitative results from microscope images of the microfibres demonstrates the difference in microfibre shape between materials. Cotton released the highest average total number of particles, with 508 microfibres per plate, followed by polar fleece with 474 microplastics per plate and polyester with 425 microplastics per plate. A one-way ANOVA test was used, and the p-value of 0.619742 is >0.05, the null hypothesis was accepted, meaning there was no significant difference between the fabric microfibre shedding.
Literature Review Microplastics are small pieces of plastic that have been degraded to a size of 0.001-5mm and they are shed either by the breakdown of a larger piece of plastic or directly released in the microplastic form (e.g. glitter, peeling of coatings, washing or wearing of synthetic textiles, etc.) (Microplastics from Textiles, 2022). Microfibres are released throughout the manufacturing process, garment wearing and end of life disposal (See Figure 1). Approximately 34.8% of those microplastics come from the first laundering of synthetic textiles (Boucher & Friot, 2017) with the release of these microfibres is reduced every successive wash (Hartline, Bruce, Karba, Ruff, Sonar, Holden, 2016). The shedding of microplastics is affected by the weave structure and proportion of polyester in the fabric and one 6kg load of polyester clothing can discharge up to 700,000 microplastic fibres (Napper & Thompson, 2016). These microfibres are not retained by washing machines but are discharged through washing machine effluent (Microplastics from Textiles, 2022). Every fabric sheds fibres (CBC, 2019) and microfibres and microplastics aren’t the same. Microfibres are natural cellulose, whilst microplastics are small plastic fragments, usually from the breaking down of larger plastics over time.
Microplastics are ingested by all kinds of living organisms, ranging from plankton, fish and large mammals in marine environments to land animals and humans (Microplastics from Textiles, 2022) and have been reported in a wide range of human foods and beverages, including seafood, drinking water, beer, salt and sugar (Microplastics in Drinking-Water, 2019). Additional, chemicals can leach out of microplastics with the degradation and fragmentation of particles expected to further increase the potential for the leaching of chemicals (Microplastics from Textiles, 2022).
Figure 1: Where microfibres are released during clothing lifetime and environments they contaminate. (Source: European Topic Centre on Circular Economy and Resource Use)
Fast fashion has been on a rise within the last 10 years with brands such as Shein and Temu gaining increased interest over time (See Figure 2). 76% of Shein's
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Scientific Research in School Volume 8 Issue 1 2026 products use polyester, a fabric that easily sheds microplastics, and only 6% of those clothing items are recycled (Grist, 2024) and every year, the fast fashion industry contributes to 35% of microplastics polluted into the environment (Breen, 2022).
Figure 2: graph showing worldwide interest as a percentage in the last 10 years. (Source: Google Trends)
In 2024, polyester was the most widely produced fabric, with 78 million tons produced, making up 59% of global fabric production, followed by cotton, 24.5 million tons produced, making up 19% of global fabric production (“Materials Market Report 2025,” 2025). In 2024, the global polar fleece market size was evaluated at USD $8.5 billion and projected to reach USD $13.7 billion by 2033. The main drivers of the growth in the polar fleece market are attributed to a surge in outdoor tourism such as hiking and camping, with many people purchasing clothes from fast fashion brands that use polar fleece to create durable, lightweight, and thermally efficient clothing. (Patel, 2025) Past research indicates that polar fleece releases approximately 20,000-25,000 microplastics per gram in the first washing (Cai et al., 2020) and in a more recent article, 2-sided polar fleece released approximately 237.57mg/kg of microplastics in a 1.5kg wash load (Klinkhammer et al., 2024). Polyester releases approximately 10,000 microplastics per gram in the first washing (Cai et al., 2020) and around 44mg/kg of microplastics are released in a 40°C first wash (Choi et al., 2022)., Cotton, can release approximately 2279 microfibres per litre in a twill weave (Julapong et al., 2024) or 0.2-0.4mg/g of cotton washed (Zambrano et al., 2019). These numbers can vary heavily depending on the weave, tension and manufacturing of the fabric, as well as the wash cycle variables such as temperature, time and use of detergent.
Scientific Research Question To what extent does polar fleece release more microfibres than polyester and cotton on the first launder?
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Scientific Hypothesis Polar fleece will shed significantly more microfibres compared to polyester and cotton on the first launder.
Methodology Three black fabrics — polyester, polar fleece and cotton — were purchased. For each fabric type, eight 10 × 10 cm double-hemmed squares were prepared to mimic clothing used in daily wear. Each fabric square was placed in a 400 mL beaker with 200 mL of 40°C water and one teaspoon of ecofriendly detergent. Cling wrap was placed over each beaker to prevent spillage, and the beakers were shaken in an orbital shaker at 3000 rpm for 30 minutes. After shaking, the beakers were removed, and any liquid was lightly squeezed from each fabric square before the squares were discarded. Table salt was added at a ratio of 6 g per 20 mL of liquid to create a supersaturated salt solution. Approximately 15 mL of supernatant was transferred from each beaker into a 20 mL centrifuge tube, with three tubes prepared per beaker. The remaining liquid was discarded. This process was repeated for all eight beakers of the fabric type. The centrifuge tubes were spun at 2008 rpm for 8 minutes. Once the centrifuge had completely stopped, the tubes were kept upright in a test tube stand. A pellet containing microplastics and salt formed at the bottom of each tube. Most of the water was poured off, and distilled water was added until the pellet was covered. A stirring rod was used to break up and mix the pellet with the distilled water before the mixture was poured into a petri dish. This was repeated until no residue remained in the centrifuge tube. The process was completed for all centrifuge tubes and repeated for the other two fabric types. The petri dishes were left in a warm, dry place until all water had evaporated, leaving salt crystals and microplastics. Each petri dish was examined under a binocular dissecting microscope at 4× magnification and photographed. This was repeated for every petri dish. Images were grouped by fabric type and plate number, and the number of microplastics in each photo was manually counted and recorded in tables (see Table 3, 4 and 5 in appendix for results).
Results Each result was obtained by manually counting the number of strands and beads of microplastics/fibres that were visible in the images taken using a phone from the microscope at 4x magnification.
Scientific Research in School Volume 8 Issue 1 2026 Table 1: descriptive statistics for each fabric shedding Mean (number of Standard microfibres from deviation all plates) Cotton 509 225.86 Polyester 426 239.32 Polar fleece 474 229.41
Sample size (number of plates) 15 12 15
In the polar fleece plates, there was an average of 43 strands and 431 beads per plate, with the combined total average across all 12 plates being 474 microplastics present. In polyester plates, an average of 48 strands and 378 beads per plate, with the combined total average across 15 plates being 426 microplastics present. In cotton plates, an average of 507 strands and 2 beads per plate, with the average across all 15 plates being 509 microfibres and 2 microplastics (see Appendix for full tables of results). Table 4 shows the standard deviation for all three fabrics is large, meaning quantity of microfibres/plastics released were greatly varied (as seen in Tables 3, 4 and 5 in appendix). Overall, cotton shed the most from the 30-minute 40°C wash, followed by polar fleece then polyester (See Figure 3). This count may be inaccurate due to human error of miscounting, however when unsure of the values given, the image was recounted and reevaluated. The graph shows an average of the results per fabric and separated by microfibre shape in each column.
Figure 3: Average microfibre/plastic count per fabric, separated by shape, with error bars Table 2: one-way ANOVA test values Source Between Groups Within Groups
Sum of squares
Degree of Mean square F-value freedom
51691.53
2
25845.76
2081021.4
39
53359.52
0.4844
p-value 0.619742
Using a one-way ANOVA test with p=0.05, the total microfibre release from cotton, polar fleece and polyester were inputted. Since the p-value of 0.619742>0.05, there is insufficient evidence from the ANOVA table to reject the null hypothesis, therefore, there is no significant difference between the microfibre shedding of polar fleece, polyester and cotton (see Table 2).
Discussion Synthetic textiles are becoming more popular and with the rise in popularity, higher amounts of microplastics will be released yearly. The focus of this study was to test whether polar fleece released more microplastics than polyester and cotton. Whilst the values of polyester and polar fleece were not significantly different, the average total of polar fleece was greater by 49 microplastics. The hypothesis that whether polar fleece released significantly more microplastics than polyester and cotton is still rejected, as the ANOVA test showed that p-value was greater than 0.05, therefore suggesting that the differences between the mean microfibre counts for each fabric type were not statistically significant. The standard deviation of the results is around 228, which is unusually high. Possible reasons for this may include a small sample size and microfibre shedding not being consistent throughout each beaker. The large error bars indicate substantial variability within the data, which is consistent with the large p-value obtained from the one-way ANOVA. In the methodology, the supernatant needed to be supersaturated so that the microplastics were easier to extract. This was important because microplastics are polar and are attracted to the salt, helping them to separate from the liquid and made it easier to extract from the supernatant in the centrifuge. Majority of the microplastics shed by polar fleece and polyester were bead shaped (see Figure 4), however, microplastics “typically have a fibre shape” (Microplastics from Textiles, 2022) (see Figure 5). The reason for the bead shaped microplastics is that the fabrics were washed in 40°C water. In polyester fabrics, fibres keep their shape when washed in 30°C water or lower, however, if washed at a higher temperature, the fibres can deform, which causes the bead shaped microplastics (Zhejiang Hongtao Textile Technology Co., Ltd., 2025). Additionally, the beads present on the cotton plates are most likely cross contamination from the synthetic textiles during the drying process, as the air conditioner may have blown a few beads into the plates. In the future, the plates should be dried in separate shelves to prevent this.
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process (Wang et al., 2023). The quantification is faster and expected to be more accurate, whereas manually counting may yield different results as human errors are more likely to occur (Tiffin et al., 2022).
Figure 4: Microplastic ‘bead’ shaped fibres from polar fleece
A limitation of this experiment is that the process of drying the plates take a long time if the conditions aren’t right, which may lead to cross contamination if not stored properly. To combat this, the heater was left on inside the room, and the plates were placed in a high shelf that was left undisturbed for 3 days. The implication of this study is that on the laundering of synthetic textiles releases high amounts of microplastics. The type of fabric also influences the release of microplastics. From the 980cm2 swatches of fabric that were tested, polar fleece and polyester both released approximately 6 microplastics per cm2 of fabric.
Figure 5: Microplastic ‘strand shaped fibres from polyester
Figures 4 and 5, show the difference between strand shaped fibres and bead shaped fibres. The beads were much smaller and accumulated in large numbers. These images are only a small part of all the images taken for the counting. Cotton released the most microfibres – with an average total of 508 – but majority of the microfibres that are released will break down over time in the environment unlike microplastics. In fresh water, 86% of cotton microfibres will break down and in salt water 67% will break down over a period of 90 days, whilst compared to polyester, with 0% break down in 90 days in both fresh and salt water (Vulcan, 2019). The impact of cotton microfibres on the environment come from any dyes or chemicals they have been treated with, which may leak into the environment as the fibres break down (Periyasamy, 2023). A difficulty encountered during the study was inconsistent results from the fabric plates. In the polar fleece plates, the total varies from 107 up to 903 microplastics (see Table 3 in appendix). An initial count showed low values for the microplastics, so the images were recounted twice, and an average of the three amounts were taken for the table results. Previous research on similar topics have various methodologies that they followed, with major differences occurring in the quantification and washing method and the filter used (Tiffin et al., 2022). For example, the use of an image processing software such as ImageJ automates the quantification
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Further research into microplastics release from synthetic textiles can look towards how different methods of washing may affect microplastic release. For example, washing at lower temperatures or handwashing vs machine washing. This study would investigate how to reduce microplastic release into the environment from synthetic textiles. Another future study can investigate the amount of microplastic released over multiple washes for fabrics, to research the long-term effects of washing on microplastics release. how the weave of a fabric may affect microfibre shedding. This would research how microfibre shedding can be reduced through how a fabric is weaved in production.
Conclusion This study analysed the number and type of particles released from cotton, polar fleece and polyester fabrics during a simulated first launder. The one-way ANOVA test found a p-value of 0.619742, which is >0.05, therefore the microfibre shedding between polar fleece, polyester and cotton is not significantly different. Therefore, the hypothesis that polar fleece would release significantly more microfibres than polyester and cotton was not supported. Additionally, due to a high p-value, high standard deviation was expected, with it being around 228, meaning there was high variation within the data which is concurrent with a high p-value. The results showed that cotton released the highest average total number of particles, with 508 microfibres 100cm2. Polar fleece released an average of 474 microplastics per plate, while polyester released an average of 425 microplastics per plate. The results suggest that
Scientific Research in School Volume 8 Issue 1 2026 synthetic fabrics such as polar fleece and polyester can release large amounts of microplastics, while cotton can release large amounts of microfibres during everyday washing, especially during the first launder. This means that the environmental impacts are severe, with many microplastics being released and possibly entering the environment through sewage. Whilst cotton released the most microfibres overall, these are made of plant cellulose rather than plastics, meaning they will break down over time. However, as they break down, the cotton microfibres may leak any dyes or chemicals that it was treated with, potentially causing harm to the environment. From the experiment results, both polar fleece and polyester released approximately 6 units of microplastics per cm² of fabric, whilst cotton released approximately 5 units of microfibres per cm2 from its first wash.
I would like to thank Mrs Virginia Ellis and Mrs Kathy Haigh for helping refine my research question and for helping me brainstorm and finalising my methodology for filtering the microplastics. Additionally, I would like to thank them and Tim Wilson for looking through my study and suggesting changes that make it more concise and scientifically accurate.
These results will allow consumers to make a more environment-friendly decision for clothing and fabric shopping and allow scientists to make informed decisions about the next steps in microplastic research.
References
Declaration of use of AI I used AI for research and as a writing assistant throughout my Science Extension project on microplastic and microfibre release from fabrics. AI did not conduct the experiment or create my primary data. I used it to help me find sources, organise information, interpret data, count visible particles in images, and improve the clarity of my report.
I read everything the AI created and reworded parts of it that I found unclear or irrelevant.
Acknowledgements
I would also like to thank Ms Justine Allen for giving me access to scientific research articles that I used in my background research and as part of creating my methodology. Without them, I would not have been able to complete this paper and study.
Boucher, J., & Friot, D. (2017). Primary microplastics in the oceans: A global evaluation of sources. IUCN International Union for Conservation of Nature. https://doi.org/10.2305/IUCN.CH.2017.01.en Breen, R. (2022). The Evil Fashion Giant: Shein and the Perpetuation of Overconsumption. https://opentext.ku.edu/environmentalgeopolitics/chapter/e vil-fashion-giant-shein/\ Cai, Y., Yang, T., Mitrano, D. M., Heuberger, M., Hufenus, R., & Nowack, B. (2020). Systematic Study of Microplastic Fiber Release from 12 Different Polyester Textiles during Washing. Environmental Science & Technology, 54(8), 4847–4855. https://doi.org/10.1021/acs.est.9b07395
I used AI to research background information about fabric use and fast fashion. I asked it to find recent data on the worldwide use of polyester, polar fleece and cotton in clothing, including trends in fast fashion and the decline of cotton clothing from 2016 onwards. To ensure the information was correct, I told it to always link back to the source. Before using any of the information, I read through the linked article to ensure the information was reliable.
CBC. (2019, November 15). Your laundry and plastic pollution—Which fabrics shed the most microplastics. CBC Radio. https://www.cbc.ca/radio/quirks/nov-16-watchingwildfire-with-radar-the-return-of-race-science-and-more1.5359599/your-laundry-and-plastic-pollution-whichfabrics-shed-the-most-microplastics-1.5359608
I attempted to use AI as an image-counting assistant to count the microplastics in the plate images. I uploaded images and asked it to count only the black dots, or to count visible microplastics and separate them into “bead” and “string” shapes, however, I found the counts to be inaccurate, so I didn’t end up using the numbers it provided.
Grist, S. K. M. (2024, September 19). Shein is officially the biggest polluter in fast fashion » Yale Climate Connections. Yale Climate Connections. https://yaleclimateconnections.org/2024/09/shein-isofficially-the-biggest-polluter-in-fast-fashion-ai-is-makingthings-worse/
Finally, AI helped with scientific writing. I asked it to help write my conclusion, abstract and to reword my methodology to put it in past neutral tense. For my abstract and conclusion, I restricted it to only use information that I wrote from this report. I also used AI to write this AI-use statement.
Choi, S., Kim, J., & Kwon, M. (2022). The Effect of the Physical and Chemical Properties of Synthetic Fabrics on the Release of Microplastics during Washing and Drying. Polymers, 14(16), 3384. https://doi.org/10.3390/polym14163384
Hartline, N. L., Bruce, N. J., Karba, S. N., Ruff, E. O., Sonar, S. U., & Holden, P. A. (2016). Microfiber Masses Recovered from Conventional Machine Washing of New or Aged Garments. Environmental Science & Technology, 50(21), 11532–11538. https://doi.org/10.1021/acs.est.6b03045 Julapong, P., Srichonphaisarn, P., Meekoch, T., Tabelin, C., Juntarasakul, O., & Phengsaart, T. (2024). The Influence of Textile Type, Textile Weight, and Detergent Dosage on Microfiber Emissions from Top-Loading Washing
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210.
Klinkhammer, K., Kolbe, S., Brandt, S., Meyer, J., Ratovo, K., Bendt, E., & Rabe, M. (2024). Release of fibrous microplastics from functional polyester garments through household washing. Frontiers in Environmental Science, 12, 1330922. https://doi.org/10.3389/fenvs.2024.1330922 Materials Market Report 2025. (2025, September 18). Textile Exchange. https://textileexchange.org/knowledgecenter/reports/materials-market-report-2025/ Microplastics from textiles: Towards a circular economy for textiles in Europe. (2022, February 9). https://www.eea.europa.eu/en/analysis/publications/microp lastics-from-textiles-towards-a-circular-economy-fortextiles-in-europe Microplastics in drinking-water. (2019). World Health Organization. Napper, I. E., & Thompson, R. C. (2016). Release of synthetic microplastic plastic fibres from domestic washing machines. Marine Pollution Bulletin, 112(1–2), 39–45. https://doi.org/10.1016/j.marpolbul.2016.09.025 Patel, D. (2025, August 13). Polar Fleece Apparel Market Research Report 2033. https://marketintelo.com/report/polar-fleece-apparelmarket Periyasamy, A. P. (2023). Microfiber Emissions from Functionalized Textiles: Potential Threat for Human Health and Environmental Risks. Toxics, 11(5), 406. https://doi.org/10.3390/toxics11050406 Tiffin, L., Hazlehurst, A., Sumner, M., & Taylor, M. (2022). Reliable quantification of microplastic release from the domestic laundry of textile fabrics (method testing). The Journal of The Textile Institute, 113(4), 558–566. https://doi.org/10.1080/00405000.2021.1892305 Vulcan, R. (2019, February 18). How Quickly Do Textile Microfibers Degrade in Aquatic Environments? CottonToday. https://cottontoday.cottoninc.com/howquickly-do-textile-microfibers-degrade-in-aquaticenvironments/ Wang, C., Chen, W., Zhao, H., Tang, J., Li, G., Zhou, Q., Sun, J., & Xing, B. (2023). Microplastic Fiber Release by Laundry: A Comparative Study of Hand-Washing and Machine-Washing. ACS ES&T Water, 3(1), 147–155. https://doi.org/10.1021/acsestwater.2c00462 What Is Polyester Microfiber Fabric A Complete Guide. (2025). Retrieved June 14, 2026, from https://www.hongtaotextile.com/what-is-polyestermicrofiber-fabric-a-complete-guide/ Zambrano, M., Pawlak, J., Daystar, J., Ankeny, M., Cheng, J., & Venditti, R. (2019). Microfibers generated from the laundering of cotton, rayon and polyester based fabrics and their aquatic biodegradation. Marine Pollution Bulletin, 142, 394–407. https://doi.org/10.1016/j.marpolbul.2019.02.062
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Appendix Appendix 1: Microplastic count by strands and beads from polar fleece by plate Plate 1 2 3 4 5 6 7 8 9 10 11 12 Average
Strings 61 106 46 49 54 50 19 39 13 18 21 45 43
Beads 842 616 666 499 544 511 246 220 226 89 420 293 431
Total 903 722 712 548 598 561 265 259 239 107 441 338 474
Appendix 2 Microplastic count by strands and beads from polyester by plate Plate 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Average
Strings 41 99 59 52 32 35 20 35 70 82 26 46 25 48 56 48
Beads 566 437 507 341 205 306 93 247 967 542 491 160 189 296 329 378
Total 607 536 563 393 237 341 113 282 1027 624 517 206 214 344 385 425
Appendix 3: Microfibre count by strands and beads from cotton by plate Plate 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Average
Strings 752 345 653 452 568 193 238 904 573 239 310 634 364 865 509 506
Beads 0 2 0 6 0 0 11 0 0 4 0 3 0 0 4 2
Total 752 347 653 458 568 193 249 904 573 243 310 637 364 865 513 508
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Antimicrobial protection in water-based cosmetic foundations: A comparative review of contemporary preservative systems Olivia Klockmann Barker College It is well known that bacteria can effectively contaminate cosmetic products, especially those which are water based. Hence, as a result, preservatives must be added to formulations to prolong their shelf life and prevent skin diseases. The purpose of this study is to examine the benefits, efficacy, and mode of action of preservatives frequently found in water-based cosmetics. The literature review suggests that phenoxyethanol is one of the most common preservatives, which is less disturbing to the microbiome of the skin and is characterized by high efficacy against Gramnegative bacteria and yeasts. However, there is some uncertainty in its superiority since it may demonstrate insufficient antimicrobial efficiency alone. On the other hand, parabens have proved themselves effective in combating Gram-positive bacteria like S. epidermidis, S. aureus and fungi. Additionally, alcohol and organic acids demonstrate their specific mechanism of action; however, both types of preservatives depend on pH values significantly. Thus, based on the analysis of literature available today, one can claim that a combination of different preservatives is needed to achieve maximum antimicrobial efficacy in water-based cosmetics.
Literature Review Water-based cosmetic foundations have the potential to harbour microbial contaminants because of the moisture and nutrients contained in them which forms an ideal ground for bacterial, fungal, and yeast to spread (Attar & Imam, 2025). These microorganisms inhibit the action of preservatives, minimizing the shelf life of products, whilst birthing other dangers, such as causing skin irritation and infection. To diminish these problems, preservatives such as parabens, phenoxyethanol, alcohol, multifunctional boosters, and organic acids are employed. The chemically distinct preservatives work in the same way through the disruption of microbial cell membranes. Due to the worries around preservatives like parabens and the consumer shift towards cleaner cosmetics, knowledge of the efficacy of preservatives is vital. Phenoxyethanol has been known as the most used preservative for complexion cosmetics, according to current literature sources (Tarnowski et al., 2024) it was seen to be part of 68.6% of the singlepreservative complexion formulations and 50.4% of two-preservative formulations. This has been associated with its efficacy against Gram-positive bacteria, Gram-negative bacteria, and yeasts (Dréno et al., 2019). However, research showing its superiority over other preservatives has not been consistent.
preservative to the skin microbiome in comparison to others such as parabens. Yet several studies have indicated phenoxyethanol's positive effect against a wide range of bacteria and yeast (Dréno et al., 2019). Phenoxyethanol primarily acts against bacteria by inhibiting oxidation and phosphorylation processes within the cell, thus limiting their energy production, resulting in the death of the bacteria (Calogero et al., 2024). Comparatively, organic acids construct an acidic environment which inhibits the enzymes and osmotic imbalance within the cell (ScienceDirect, 2024), whilst parabens act by hindering the electron transport process; hence, the strength of action is increased with a larger length in the alkyl chain (Tarnowska et al., 2024). On the other hand, alcohol works by denaturing the protein and cell membrane, thus the overall functions, which only act effectively under pH levels that don’t exceed 5.5 (SpecialChem, 2023). Multifunctional boosters, including phenylpropane and caprylyl glycol, do not inhibit bacteria individually but act as a booster preservative to increase efficiency in the reduction of bacterial membrane surface tension (Tarnowska et al., 2024). It is also important to note that phenoxyethanol is a widely used preservative, as it disrupts the skin microbiome less than preservatives such as methylparaben and methylisothiazolinone (Tarnowska et al., 2024; Calogero et al., 2024).
According to Tarnowska et al. (2024) and Calogero et al. (2024), phenoxyethanol is a less disruptive
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Figure 1: Gram negative compared to gram positive bacteria cell wall
Although phenoxyethanol is widely recognized for its effectiveness and superiority over parabens, this remains debated by many researchers, such as Dreno et al. (2019). Tehran University of Medical Sciences published a study in the Journal of Clinical and Aesthetic Dermatology that found that propylparaben displayed more prominent antimicrobial activity against S. epidermidis and S. aureus (Kaur et al., 2021). Moreover, it is reported that phenoxyethanol alone produced a substandard amount of antimicrobial activity in shampoo, shower gel, and body milk formulations, thus requiring a combination of other preservatives, to work effectively. (Halla, et al., 2018). It is stated that parabens are more effective against fungi and Gram-positive bacteria (Pereira et al,. 2024), while phenoxyethanol performs better against Gramnegative bacteria (Drèno et al., 2019). Overall organic acids (sodium dehydroacetate) also vary in effectiveness depending on the formula's pH, as the overall activity decreases when the pH rises above 8.5 (Ribeiro et al., 2018). Current literature contains practical limitations that reduce the reliability of any comparisons. Many studies rely on Minimum Inhibitory Concentration (MIC) and disc diffusion tests, which are conducted in controlled vitro conditions. These methods cannot fully mimic preservative behaviour on human skin, for example dilution, skin interaction, and dermal substantivity influence performance (Murphy et al., 2021). In addition, preservatives are commonly tested in combinations rather than individually, thus are usually combined because no single preservative is sufficiently effective alone (Murphy et al., 2021).Additionally, diversity in sample size is a limitation, as studies use individuals from limited geographical regions (JCAD, 2022); as a result, differing regions may find that their skin type, climates, genetics, lifestyles, and cosmetic habits can affect the interaction of the makeup or its preservatives with the skin. Alongside this, there is also limited research using cosmetic foundations specifically as the testing base (Jiang et al., 2023).
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The literature directly informs the question of which preservative demonstrates the strongest antimicrobial activity in water-based foundations, yet gaps remain in comparing their singular uses. Each preservative class kills germs differently, and its effectiveness depends on the microorganism and formulation conditions, which may differ depending on environment and contamination exposure. Using a lawn plating diffusion method to compare isolated preservatives within commercial water-based foundations addresses a gap in this existing research. Unlike studies examining preservative blends or generic emulsions, the present study will compare representative preservatives within a foundation matrix, thus providing a more realistic measure of antimicrobial performance on skin. The literature highlights several key themes; specifically, phenoxyethanol is highly prevalent in complexion-based cosmetics and demonstrates a broad spectrum of antimicrobial activity. However, no preservative class is collectively superior as the effectiveness depends on multiple factors such as microorganism type, pH, environmental conditions, and concentration. Multifunctional boosters are used primarily as a supportive preservative rather than a standalone one, while sodium dehydroacetate is limited by the formula's pH dependency. Additionally, few studies compare the preservative effectiveness in water-based foundations when applied to human skin. Thus, important research gaps remain. Overall, no study directly compares preservatives like parabens, phenoxyethanol, alcohols, organic acids, and multifunctional boosters within a water-based formulation using a standardized zone of inhibition method. The research that supports multifunctional boosters as an independent antimicrobial property is also limited, and little data exists observing how preservative classes affect microorganisms that are naturally present on facial skin. This research addresses these gaps by comparing five commonly used preservatives under standardized conditions using experimental testing. The study aims to identify which preservative demonstrates the greatest microbial resistance in water-based foundations. The hypothesis that phenoxyethanol will produce the largest zone of inhibition is supported by the broadspectrum of evidence surrounding its antimicrobial activity, although conflicting evidence around parabens regards further research. The findings may assist cosmetic manufacturers in selecting effective preservatives.
Scientific Research Question To what extent does phenoxyethanol exhibit superior antimicrobial activity relative to other predominant
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preservative classes (parabens, alcohols, organic acids, and multifunctional boosters) when used in water-based cosmetic formulations?
Scientific Hypothesis When added as an ingredient to water-based cosmetic formulations, phenoxyethanol will exhibit greater antimicrobial activity, as highlighted by a significantly larger zone of inhibition, in comparison to other predominant preservative classes, such as parabens, alcohols, organic acids, and multifunctional boosters.
by the zone of inhibition of 7.86 mm formed by parabens when in contact with the same bacteria. Conversely, it can also be stated that the preservative multifunctional boosters did not create any zone of inhibition with both bacteria, as seen by the result of 0 mm. Additionally, it can also be noted that alcohol was the most consistent preservative in water-based foundations, followed by phenoxyethanol. The preservatives parabens and sodium dehydroacetate only created a zone of inhibition for the bacteria of L. sakei found on the skin, not maintaining consistent antimicrobial effectiveness for both bacteria.
Methodology To investigate the effects of different preservative classes on the zone of inhibition, five brand-new, sealed, water-based makeup brands were utilized to ensure minimal microbial contamination: Estée Lauder, Ilia, Sephora, Neutrogena, and Shiseido. All products were opened and tested on the same day. Initial microbial solutions of E. coli and L. sakei were prepared by mixing a frozen culture with the insert amount of saline solution via manual shaking to activate the bacteria. For each bacterial strain, a sterile pipette was used to extract the solution and apply three drops onto a sterile agar plate. The bacterium was then dispersed evenly across the plate using a triangular cell spreader that had been sterilized via alcohol and a Bunsen burner. Next, forceps were sterilized in the Bunsen burner and used to dip a circular filter paper disk into the first selected foundation bottle. The soaked disk was dropped from a height of 1.5 cm onto the agar plate. This process was repeated until three separate filter paper disks were positioned on the single plate. The agar plate was then isolated by closing the lid and securing it with tape. The name of the bacteria, foundation brand, researcher initials, class, and date were recorded around the perimeter of the plate using a permanent marker. This entire procedure was repeated for both bacterial strains across all five foundation brands to complete the collection. Once the procedure is finished, all plates enter an incubator for a period of 32 hours, which is set at 30 degrees Celsius. Once removed the agar plates was used to measure the zone of inhibition using a standard ruler, measuring the radius of the stagnant area to the nearest mm.
Results From Table 1’s results, sodium dehydroacetate created the largest zone of inhibition at 8.3 mm when tested against the bacteria L. sakei, followed closely
Figure 2: Zone of complete inhibition using a paraben formula Table 1: The average results show the zone of inhibition radius for each water-based foundation preservative against the skin bacteria L. sakei and E. coli. Preservative Phenoxyethanol Alcohol Parabens Multifunctional boosters Sodium dehydroacetate
Zone of Inhibition radius formed (mm) L. sakei E. coli 5.23 1.3 3.786 2.53 7.86 0 0 0 8.3 0.3
Figure 3: Bar Graph representing the Zone of Inhibition radius averages each water-based foundation preservative formed when in contact with L. Sakei and E. coli.
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Discussion After conducting the investigation, the results indicate a correlation between the type of preservative class used in water-based foundations and their antimicrobial effectiveness on the tested bacteria as seen through the zones of inhibition radius’s created. It also appears that there is a correlation between the effectiveness of these preservative classes and particular types of bacteria found on the skin. From the results, it appears that the preservative class of alcohols was the most consistent in its antimicrobial properties, seen through its creation of an average of a 3.786 mm zone of inhibition and a 2.53 mm zone of inhibition for L. sakei and E. coli, respectively, as seen in Table 1, whereby it is noted this is the smallest difference in these zones for each bacterium compared to the other preservatives. Phenoxyethanol closely followed as the second most consistent antimicrobial preservative found in the tested water-based foundation, as it created a 5.23 mm zone of inhibition and a 1.3 mm zone of inhibition for L. sakei and E. coli, respectively, as represented in Figure 3. Figure 3 also shows that the preservative classes of sodium dehydroacetate and parabens were only effective against L. sakei and had no antimicrobial effect against E. coli. The preservative class of alcohol was seen in the results as the most consistent among its antimicrobial actions against both L. sakei and E. coli, as it is considered a broad-spectrum preservative, wherein it universally destroys the bacteria's lipid cell membrane and denatures the cells' proteins, allowing this mechanism of action to be performed consistently. Similarly, phenoxyethanol is identified in the results as a consistent preservative against both types of bacteria, as its mechanism of action for causing cell death is universal among all bacteria; it penetrates the cell membrane, leading to the leakage of essential nutrients and a reduction in energy synthesis, which is characteristic of broad-spectrum preservatives. Sodium dehydroacetate, although it is also known as a broad-spectrum preservative, was only effective against L. sakei, as the bacteria lacks an outer membrane. This characteristic resulted in sodium dehydroacetate more easily penetrating the peptidoglycan wall and performing its further destructive internal cell mechanisms of action. Comparatively, E. coli has a robust outer lipid membrane that effectively acts as a physical barrier against the preservative, as seen through only a 0.3 mm zone of inhibition created in Table 1. For this same reason, the preservative class of parabens was also only effective on L. sakei due to Gram-positive bacteria such as L. sakei and others lacking an outer protective membrane and maintaining a highly porous 74 • Science Extension Journal
peptidoglycan layer. In doing so, it is not as effective in penetrating gram-negative thick outer membranes such as E. coli's, as seen through the lack of a zone of inhibition created (Figure 3). Additionally, although not consistent with both bacteria, it appears from the results that sodium dehydroacetate formed the largest zone of inhibition, 8.3 mm, compared to all other preservatives when tested against bacteria L. sakei, followed closely by parabens' zone of inhibition of 7.86 mm formed in contact with L. sakei. This is because sodium hydroacetate has more specific mechanisms of action against L. sakei than alcohol's and phenoxyethanol's broad mechanisms of action used on all bacteria. In particular, it works through a targeted enzyme-inhibiting mechanism suited directly for L. sakei. Similarly, parabens created a greater zone of inhibition for L. sakei than both alcohol and phenoxyethanol, as they too have a very particular and specifically suited mechanism of action against it compared to their broad actions. Here parabens' chemical alkyl chain specifically targets and dissolves the lipid membrane of L. sakei. However, multifunctional boosters appear ineffective against both strains of bacteria, as seen through a zone of inhibition of 0 mm created for both strains. Multifunctional boosters are an addition to makeup which destabilizes the microbial wall. Without the protection it triggers osmotic lysis within the cell (Yadav, et al,. 2018), (Siegert, et al., 2008). Ultimately, the results of the experiments are considered accurate. Since the results produced consistent areas of inhibition which Align with the predicted trends of antibacterial activity, this highlights how the lack of systematic error was minimized. The experimental equipment allowed for a high level of accuracy, as all measurements of volume and radius were precise. This accuracy is displayed through the use of a pipette to measure and distribute the bacteria across multiple agar plates, whereby the amount distributed by each drop of the pipette was identical. Additionally, the amount of foundation measured and used was precise due to the sterilised filter paper being the same size, hence absorbing an identical volume of foundation with each dip. A ruler used to measure zone of inhibition also allowed relatively precise measurements of the radius, up to the exact mm. However, the accuracy can be improved by measuring the radius with precise technology, such as digital calipers or even an automated colony and inhibition
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zone analyser, to minimize the risk of human error and human judgment while providing more precision than a ruler to the nearest mm. One limitation is that manual dipping can cause inconsistencies in time and angle, which may change the amount of foundation absorbed by the filter paper. Additionally, the assumption that all inhibition zones are round shaped also reduces accuracy because of the asymmetrical boundary growth, which makes the measurement of one radius inadequate. To increase accuracy in future studies, mechanical timed dipping arms and digital imaging software will need to be employed. Even though the test was done on each bacterium with respect to each preservative in three separate agar plates, there was a great difference in the repeated result in at least half of them, as shown in Figure 3. It can also be noted that the reliability of the results of testing bacteria E. coli was better than that for bacteria L. sakei, as demonstrated in the appendix. Whereas some repeated results, such as the case of E. coli with respect to phenoxyethanol, were reliable since their results were similar, at 0.13, 0.10, and 0.16, and the case of testing E. coli against preservative sodium dehydroacetate produced results of 0.3 for all, most of the other results were not reliable. The main reason that contributed to low reliability among results was the inconsistency in density of the bacterial lawn at the first stage of testing due to uneven spreading of the bacteria on each agar plate. Consequently, diffusions of preservatives would experience unequal resistance, resulting in different sizes of the inhibition zones. Moreover, variations in conditions of incubation of the bacteria in the same incubator could occur. For greater reliability of results obtained from further tests, the sample size could be increased to five or more for each variable through a larger number of repetitions, which would result in better determination of the mean value and spotting of outliers. Another solution to minimizing variance could be to use either a mechanical spreader or a fixed liquid bacterial suspension with definite turbidity (e.g., McFarland standard). In conclusion, the experiment can only be partially validated, as although the method used for conducting the experiment had a direct relationship to the objective of investigating the impact of different preservatives on bacterial growth, the high variability in the reliability results and the presence of certain uncontrolled variables impacted the validity of the experiment. One of the significant limitations of the
study that impacted validity was related to the fact that controlling the effect of the independent variable (preservative) on the dependent one (inhibition zone) was rather complicated due to the potential influence of such a variable as the speed of diffusion through agar, which may vary depending on the density and thickness of the agar across different plates. Also, an important source of error was that the concentration of the preservative was unknown or possibly unequal among all the foundation samples. Therefore, the independent variable was not properly isolated, since the zones of inhibition may vary due to different concentrations rather than the preservative's effectiveness. Strict adherence to control variables is essential for enhancing validity in future experiments, and standardisation procedures can achieve this outcome. The use of pre-frozen commercial agar plates will ensure that an even depth exists, hence a uniform diffusion rate. Additionally, the use of a negative control, whereby the filter paper is soaked in sterile water, ensures that any negative reaction observed in the test is due to the preservatives themselves rather than the filter paper. A positive control would help confirm that the bacteria strains used are sensitive to antibiotics. This can be achieved by running all the inoculation tests in a laminar flow hood. The area of research that deserves particular attention in the future includes creating the most efficient combinations of preservatives, rather than the use of individual agents. Given that phenoxyethanol does not have strong individual antimicrobial properties when used against fungi and Gram-positive bacteria, it is important to perform thorough research aimed at determining what ratio needs to be observed between this preservative and multifunctional enhancers and organic acids. Moreover, considering the increased interest in natural products, there is an urgent need for research that will help determine how using synergic preservatives will affect the well-being of skin microbiomes.
Conclusion The scientific report has evaluated the effectiveness of antimicrobials, mode of action, and comparative advantages when using preservatives in cosmetic to prevent microbial infections within water-based cosmetic foundations. Through researching existing literature which concern bacteria, fungi, and yeast growth, this study has acknowledged the way in which certain chemicals such as phenoxyethanol, parabens, organic acids, alcohols, and multifunctional boosters interfere with cell membrane formation and energy production, resulting in the death of the bacteria. The
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obtained research results partly confirmed the hypotheses, as it confirms the initial purpose and emphasizing the particularities in the work of preservatives. While the goal of phenoxyethanol being a prevalent, less aggressive alternative for the skin microbiome was achieved to an extent, the expectations that this preservative would be universally superior to parabens were not solely confirmed by the data. It was determined that phenoxyethanol shows a distinct advantage in contesting Gram-negative microorganisms and yeasts, with parabens such as propylparaben remaining significantly better and stronger in their effects on fungi and Gram-positive strains like S. epidermidis and S. aureus. Besides, the effectiveness of the secondary substances in question turned out to depend highly on external conditions, with pH levels acting as a critical limiting factor, while multifunctional additives solely served the purpose of boosting efficacy. Hence, the initial hypothesis of the existence of one adequate modern preservative was disproved; consequently, it can be concluded that the accomplishment of the initial purpose will require multiple preservatives.
Declaration of use of AI Throughout the report AI was utilised in areas to refine and clarify details, statistics and references, before being independently verified against other sources. Specific AI prompts such as “Is there an author and date of publication for this reference” and “What specific multifunctional preservative is in ILIA foundation”. This allowed accuracy and verification of specific details used within the report, while enabling the process of identifying related terminology and bibliographic information. Moreover, AI was used to correct grammatical and spelling errors using prompts like “Please do not change the wording yet correct all grammatical and spelling errors within this report” which improved overall readability, whilst maintaining the meaning and wording of content. The use of AI was essential in the early phases by formulating ideas. It contributed to structuring thoughts on the basis of the research question and developing ideas into a research question and a title.
Acknowledgements Thank you to the Science Extension Supervisors Mrs Kathy Haigh, Mrs Virginia Ellis, and Dr Matthew Hill.
References Attar, R. M. S., & Imam, M. A. (2025). Assessing the Levels and Types of Bacterial Contamination in Cosmetic Brushes: Implications for Beauty and Hygiene in Jeddah City. International Journal of Microbiology, 2025(1). https://doi.org/10.1155/ijm/2128581 Cosmetics Ingredients Database | Online Raw Materials Search. (2023). Cosmetics.specialchem.com. https://cosmetics.specialchem.com Dréno, B., Zuberbier, T., Gelmetti, C., Gontijo, G., & Marinovich, M. (2019). Safety review of phenoxyethanol when used as a preservative in cosmetics. Journal of the European Academy of Dermatology and Venereology: JEADV, 33 Suppl 7(S7), 15–24. https://doi.org/10.1111/jdv.15944 Halla, N., Fernandes, I., Heleno, S., Costa, P., BoucheritOtmani, Z., Boucherit, K., Rodrigues, A., Ferreira, I., & Barreiro, M. (2018). Cosmetics Preservation: A Review on Present Strategies. Molecules, 23(7), 1571. https://doi.org/10.3390/molecules23071571 Home | JCAD | The Journal of Clinical and Aesthetic Dermatology. (2024, August 6). JCAD | the Journal of Clinical and Aesthetic Dermatology. https://jcadonline.com Jiang, Y., Xu, Z., Qiu, Y., Chen, G., & Wang, S. (2023). Evaluation of the antiwear‐ability/scratch‐resistance efficacy of makeup products by in vitro test method application. Skin Research and Technology, 29(8). https://doi.org/10.1111/srt.13420 Lam, E. C. S., Li, R., Rodrigues, M. R., Vires, L., Adams, R. L., Sherrill, J. D., & Oblong, J. E. (2020). Enhanced retinoid response by a combination of the vitamin A ester retinyl propionate with niacinamide and a flavonoid containing Ceratonia siliqua extract in retinoid responsive in vitro models. International Journal of Cosmetic Science, 43(1), 102–106. https://doi.org/10.1111/ics.12669 Lee, S. H., Song, I. H., & Jang, H. (2021). Feasibility of deep learning‐based fully automated classification of microsatellite instability in tissue slides of colorectal cancer. International Journal of Cancer, 149(3), 728–740. https://doi.org/10.1002/ijc.33599 Leschke, M., & Siegert, W. (2008, September 1). Boosting efficacy of preservatives. https://www.researchgate.net/publication/232273504_Boos ting_efficacy_of_preservatives Milani, M., & Puviani, M. (2018). Anti-Aging Efficacy of Melatonin-Based Cream: Clinical and Instrumental Skin 60. Evaluation. Cosmetics, 5(4), https://doi.org/10.3390/cosmetics5040060 Pereira, A. R., Gomes, I. B., & Simões, M. (2024). Parabens alter the surface characteristics and antibiotic susceptibility of drinking water bacteria. Chemosphere, 368, 143704– 143704. https://doi.org/10.1016/j.chemosphere.2024.143704 Rajabi-Estarabadi, A., Hartman, C. L., Iglesia, S., Kononov, T., & Zahr, A. S. (2024). Effectiveness and tolerance of multicorrective topical treatment for infraorbital dark circles and puffiness. Journal of Cosmetic Dermatology, 23(2), 486–495. https://doi.org/10.1111/jocd.16122 Science Direct. (2023). ScienceDirect.com | science, health and medical journals, full text articles and books. Sciencedirect.com. https://www.sciencedirect.com
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ScienceInsights - Exploring the Frontiers of Knowledge. (2025). ScienceInsights. https://www.scienceinsights.org
Environmental Threats. Frontiers in Microbiology, 9. https://doi.org/10.3389/fmicb.2018.02064
Specialty Chemicals and Dyes Intermediates Manufacturer in India. (2025, December 25). Specialty Chemicals and Dyes Intermediates Manufacturer in India. https://www.matangiindustries.com/blog/phenoxyethanolvs-parabens-complete-preservative-comparison
Zhang, J., Zhou, Y., Zhou, F., Li, X., Lu, Y., Wu, F., Han, Y., Liu, Q., Chang, S., Zhu, W., Li, B., & Pan, Y. (2024). Development and validation of a prospective questionnaire for assessing oily sensitive skin. International Journal of Cosmetic Science. https://doi.org/10.1111/ics.12946
Yadav, A. K., Espaillat, A., & Cava, F. (2018). Bacterial Strategies to Preserve Cell Wall Integrity Against
Appendix 1 Raw results of makeup experiment Type of preservative
phenoxyethanol
alcohols
parabens
organic acids (sorbic/benzoic acid)
multifunctional boosters
No preservative
Makeup product Estee lauder Estee lauder Estee lauder Estee lauder Estee lauder Estee lauder Shiseido Shiseido Shiseido Shiseido Shiseido Shiseido Neutrogena Neutrogena Neutrogena Neutrogena Neutrogena Neutrogena Sephora Sephora Sephora Sephora Sephora Sephora Ilia Ilia Ilia Ilia Ilia Ilia
Bacteria L.Sakei L.Sakei L.Sakei E. coli E. coli E. coli L.Sakei L.Sakei L.Sakei E. coli E. coli E. coli L.Sakei L.Sakei L.Sakei E. coli E. coli E. coli L.Sakei L.Sakei L.Sakei E. coli E. coli E. coli L.Sakei L.Sakei L.Sakei E. coli E. coli E. coli E. coli L.Sakei N/A
Diameter of ZOI (cm) 2.7 6.0 7.0 1.3 1.0 1.6 0.46 5.3 5.6 5.0 0.6 2.0 7.3 9.0 7.3 0 0 0 4.0 4.3 1.67 0.3 0.3 0.3 N/A N/A N/A N/A N/A N/A
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Impact of Using OEM to Bioremediate Oil Spills Jimmy Li Barker College This study evaluates the viability of utilising oil-eating microbes (OEM; Pseudomonas bacteria) alongside immobilised algal balls for large-scale bioremediation and environmental recovery of kerosene oil spills. Using carbon-deficient Bushnell-Haas Broth as a specialised growth medium, 10mL of Pseudomonas cultures were incubated for 24 hours with variable kerosene treatments (0mL, 1mL, 5mL, and 10mL) to assess hydrocarbon metabolism via changes in pH caused by byproduct carbonic acid (H2CO3) formation. Immobilised algal balls were subsequently introduced for 24 hours to counteract acidification through photosynthetic CO2 uptake. The results demonstrated that OEM successfully metabolised the hydrocarbons, inducing a dose-dependent drop in mean pH from an initial 6.74–6.77 down to 6.550 (1mL), 6.377 (5mL), and 6.210 (10mL). Algal balls effectively mitigated mild acidification, restoring the 0mL and 1mL environments to baseline levels (0.000 pH change and -0.019 pH change, respectively). However, at the 10mL threshold, severe acidification stagnated around a lower limit of 6.199, overcoming the restorative capacity of the algae (-0.243 pH change). These findings suggest that while high localised pollutant concentrations cause critical ecosystem acidification, an integrated OEM-algal system holds strong potential if deployed within controlled waste-management frameworks.
Literature Review Impact of Oil Spills on Local Environments Hydrocarbons are a large area of chemical products predominantly produced from the processing of crude oil, coal, and natural gas after drilling and fracking (Al-Haleem et al., 2013), causing irreparable damage from contaminants in the 300 million total barrels of waste produced in the US in 1992. Structurally composed of only carbon and hydrogen atoms, these compounds vary from simple, straight-chain alkanes to complex, ring-structured aromatic hydrocarbons (Kingston, 2002). When introduced to aquatic environments, hydrocarbons act as long-lasting pollutants, resisting immediate breakdown while also being extremely reactive, threatening fragile, local ecosystems. Kerosenes are a subset of all hydrocarbons, as large organic molecules consisting of between 10 and 16 carbon atoms (Wikipedia, 2025) and are used in a large variety of industries as solvents or fuels, due to their non-polar and large structure. They are most commonly used as energy-dense jet fuel. In general, dodecane (C12H26) is used as an approximation for chemical equations, as the median of all kerosenes (Figure 1).
Figure 1: Approximation of kerosenes: dodecane (C12H26); drawn on (Source: MolView, 2026)
As (neo, 2025) highlighted, the 2010 Deepwater Horizon Oil Spill was catastrophic to the Gulf of Mexico’s marine systems, due to the accidental release of approximately 4.9 million barrels of crude oil into the immediate ocean surroundings. Because crude oil has a lower density than water and possesses strong hydrophobic properties, it forms an expansive surface layer, (Shamasundar et al., 2018), acting as a physical barrier that drastically limits gas exchange at the air-water interface, effectively killing the local ecosystem’s food web (National Oceanic and Atmospheric Administration, 2021), from phytoplankton to predators due to the hydrocarbon’s ability to bioaccumulate within animal organs (Ke C.Y. et al., 2020). Following CSIRO’s (Cranney, 2022) updated methods, current methods of cleaning oil spills and waste contaminants involve skimming the top layer off using oil booms and sponges, where it is then stored and transported. This process is not only ineffective, as small droplets remain at sea uncollected, but is also a very expensive process, costing up to $3 billion for the Deepwater Horizon spill alone (Paul, 2010). As such, extensive efforts have been undertaken to mitigate the impacts or clean up spills, with current research focussed on soil samples which have been contaminated with oil, with (Hemly et al., 2015)’s method of isolating and adding biosurfactants (Bacillus cereus, Pseudomonas stuzeri, and Acinetobacter) to oil sludge contaminated soil
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contributing a 46% increase in removal efficiency. Pseudomonas strains remediate oil through the emulsification and metabolisation of the carbon through the enzymes “oxygenase”, which inserts an oxygen atom into the structure, thus weakening the intramolecular bonds of the kerosene (Heider et al., 1998), and “alkane hydroxylase”, converting the end carbon of the kerosene into an alcohol with the substitution of carbon with a hydroxyl functional group (Kalme et al., 2008). These processes thereby reduce the harmful hydrocarbon into the less harmful CO2 as a byproduct through the following reaction: 2C12H26 + 37O2 → 26H2O + 24CO2 + Energy The introduction of CO2 into an aquatic environment causes water (H2O) to split into hydronium ions [H+] and hydroxide ions [OH-] and bond with the CO2 , forming a dynamic equilibrium solution where the formation and splitting of H2CO3 occurs at the same rate as CO2 and H2O. CO2 (aq) + H2O (l) ⇌ H2CO3 (aq) H2CO3 (aq) ⇌ HCO3- (aq) + H+ (aq) HCO3- (aq) ⇌ CO32- (aq) + H+ (aq)
However, the major increase in carbon dioxide concentration does still affect the environment, as the introduction of the molecules promotes the formation of carbonic acid, as the CO2 forms bonds with water. Following (Thomas et al., 2022), the carbonic acid produced has both beneficial and detrimental effects, such as playing a crucial role in the formation of marine habitats from the increased amount of calcium carbonate (CaCO3), but concentrations intolerable for organisms will cause a major disruption in the population of microbes and therefore health of the ecosystem. Testing for Carbon Interaction with Bacteria using a Specialised Growth Medium For testing the OEM’s ability to metabolise specific elements, specialised broths were produced to provide the bacteria with essential nutrients other than the element being tested, and following (HiMedia Laboratories, 2020), Bushnell-Haas Broth was utilised to provide all necessary nutrients other than carbon for the bacteria. This allows for the bacteria’s interaction with any carbon sources added to be investigated, as any change in results can be concluded to be from the introduced carbon (kerosene for the experiment). Algae Remediation of CO2 through Photosynthesis To remediate elevated environmental carbon dioxide, microalgae utilise an efficient photo-autotrophic pathway that transforms gaseous or dissolved CO2
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directly into organic biomass. Photosynthesis operates via a light-dependent phase generating cellular energy (ATP), driving carbon fixation (Raven et al., 2011). Elevated CO2 concentrations can thereby enhance the photosynthesis capability of macroalgae and microalgae alike, allowing them to capture carbon up to 50 times more efficiently than terrestrial crops and neutralise roughly 1.83 grams of environmental CO2 for every gram of biomass produced (Li et al., 2023).
Scientific Research Question This study addresses whether OEM can be utilised in large-scale bioremediation projects, determining the level of impact the Pseudomonas bacteria has on the pH of the environment and whether algae is able to correct for the increase in byproduct through photosynthesis.
Scientific Hypothesis The conical flask with more kerosene (10mL) will result in a lower pH than the flask with less kerosene after 24 hours of Pseudomonas bacterial metabolisation, forming carbonic acid as a byproduct and therefore resulting in a lower pH. In addition, the algal balls will be able to account for the reduction in pH, using dissolved carbon dioxide as a reactant in photosynthesis to produce glucose, therefore increasing the pH of the solution through the uptake of H2CO3 to its original value of 6.8 average.
Methodology 1. Buffer Solution test for Aquatic Environment To test if the bacteria and algal balls were able to survive in a marine environment, 500mL of buffer solution was made using 17.5 grams of non-iodised table salt (NaCl), 1 gram of Sodium hydrogen carbonate (NaHCO3), and 0.0074 grams of Sodium carbonate (Na2CO3), weighed on an electronic balance to produce a simulated marine environment. This solution was split among 12 conical flasks with each pH measured, where 10mL of Pseudomonas bacteria from (Edvotek, 2015), and 1mL, 5mL, and 10mL of kerosene were pipetted into 3 flasks each, for repetition. 6 algal balls from (Southern Biological, n.d.) were also added into each conical flask, where the flasks were then placed on a magnetic stirrer for 24 hours, with 3 unchanged flasks; a control for no kerosene, no bacteria, and no algal balls (Figure 2).
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Figure 2: Conical flasks with BH Broth (black), Buffer Solution (red), OEM, and kerosene on magnetic stirrers, where the algal balls in the Buffer Solutions have disappeared after 3 hours.
The buffer solution was then to be extracted from beneath the oil layer and the pH measured by a wireless probe from (PASCO Scientific, n.d.). 2. Kerosene test using Bushnell-Haas Broth In order to test whether the kerosene would be toxic to the bacteria and local environment, 500mL of Bushnell-Haas Broth was prepared by school lab staff according to (HiMedia Laboratories, 2020), in order to test the Pseudomonas strain’s ability to metabolise carbon from the additionally added kerosene. The following chemicals (Table 1) were measured on an electronic balance and stirred into a sterile, 500mL bottle, then autoclaved at 15 PSI (121°C). Table 1: Materials used to make 500mL of Bushnell-Haas Broth Chemical Distilled water (H2O) Magnesium sulfate (MgSO4) Calcium chloride (CaCl2) Potassium dihydrogen phosphate (KH2PO4) Potassium hydrogen phosphate (K2HPO4) Ammonium nitrate (NH4NO3) Iron (III) chloride (FeCl3)
Amount 500mL 0.1g 0.01g 0.5g 0.5g 0.5g 0.025g
The solution was split among 12 sterilised, 100mL conical flasks, along with 10mL of Pseudomonas bacteria, and 0mL, 1mL, 5mL, and 10mL of kerosene; similarly to Step 1. The flasks’ initial pH were measured with the wireless pH sensor for their initial pH, and averaged. These solutions were allowed to incubate for 24 hours (Figure 3), where the BushnellHaas Broth was tested for pH again, and extracted into 24, 10mL vials with 6 algal balls each and 2 control vials for each flask without algal balls, following the methodology from (Southern Biological, n.d.). With half of the vials (4 from kerosene amount) placed under a biolamp for 24 hours and the other half in darkness, the solutions were then tested for their final change in pH after the introduction of the algal balls.
Figure 3: 3 flasks of 0mL, 1mL, 5mL, and 10mL kerosene with OEM in Bushnell-Haas Broth, incubated for 24h.
Results 1. Buffer Solution test for Aquatic Environment The buffer solutions’ pH were not measured, as the algal balls had disappeared after being allowed to react on the magnetic stirrer for 3 hours; (See Appendix). Due to the reaction ending prematurely, Stage 1 of the experiment was deemed invalid and therefore concluded, as the buffer solution was mainly meant as a proof of concept for ocean-water. 2. Kerosene test using Bushnell-Haas Broth 0mL Kerosene: The 6 flasks which did not have any kerosene added exhibited a low change in pH of the solution, reducing between -0.016 and -0.213 pH after adding bacteria, and increasing an average of 0.094, resulting in a net 0 average change in the pH of the final solution (Table 2 and Figure 4). These control flasks demonstrate that the lack of kerosene added results in an insignificant amount of H2CO3 produced by the OEM. Table 2: Measured pH of solutions with 0mL kerosene added, to 3 d.p. Flask pH
After Adding OEM
After Adding Algal Balls
Total
Initial
Final
∆
Initial
Final
∆
1
6.763
6.659
-0.104
6.659
6.692
0.033
-0.071
2
6.761
6.735
-0.026
6.735
6.812
0.077
0.051
3
6.759
6.602
-0.157
6.602
6.855
0.253
0.096
4
6.747
6.697
-0.050
6.697
6.784
0.087
0.037
5
6.816
6.603
-0.213
6.603
6.748
0.145
-0.068
6
6.793
6.777
-0.016
6.777
6.747
-0.030
-0.046
Ave
6.773
6.679
-0.094
6.679
6.773
0.094
0.000
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Figure 4: Each flasks’ pH after adding 0mL kerosene and reacting with OEM then algal balls, with an overall 0 pH change from the initial pH, showing a decrease to an average of 6.679 pH after bacteria has reacted, and an increase in pH to 6.773 with the algal balls.
Figure 5: Each flasks’ pH after adding 1mL kerosene and reacting with OEM then algal balls, with an overall -0.019 pH change from the initial pH, showing a decrease to an average of 6.550 pH after bacteria has reacted, and an increase in pH to 6.682 with the algal balls.
1mL Kerosene: The solutions with 1mL kerosene added resulted in a higher amount of change in the pH of each solution, with an average change of -0.151 pH compared to the unaltered flasks’ -0.094 pH average. In addition, the algal balls were able to react to the increased concentration of H2CO3, raising the pH by 0.132 to an average slightly below the initial pH of 6.701 at 6.682, as seen in Table 3 below. There was also a wider range in the final pH compared to the control flasks, with a range of 0.252 compared to 0.163 from Table 2.
5mL Kerosene: The increase in kerosene added resulted in twice the concentration of carbonic acid produced by the Pseudomonas bacteria, seen with the -0.325 mean pH difference after adding bacteria. This change is greater than the -0.151 pH difference in the 1mL flasks, but there is an outlier in Flask 17, where there is a pH change of only -0.177, in comparison to the high 0.416 and -0.455 pH difference in Flasks 14 and 18 respectively (Table 4). There is also a large range of pH values after the algal balls had photosynthesised, with the initial range of 0.206 pH doubling to 0.408.
Table 3: Measured pH of solutions with 1mL kerosene added, to 3 d.p. Flask pH
After Adding OEM
After Adding Algal Balls
Total
Initial
Final
∆
Initial
Final
∆
7
6.652
6.548
-0.104
6.548
6.648
0.100
-0.004
8
6.728
6.519
-0.209
6.519
6.689
0.170
-0.039
9
6.615
6.622
0.007
6.622
6.812
0.190
0.197
10
6.830
6.535
-0.295
6.535
6.695
0.160
11
6.757
6.568
-0.189
6.568
6.688
12
6.623
6.510
-0.113
6.510
Ave
6.701
6.550
-0.151
6.550
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Table 4: Measured pH of solutions with 5mL kerosene added, to 3 d.p. Flask pH
After Adding OEM
After Adding Algal Balls
Total
Initial
Final
∆
Initial
Final
∆
13
6.655
6.445
-0.210
6.445
6.645
0.200
-0.010
14
6.653
6.266
-0.387
6.266
6.346
0.080
-0.307
-0.135
15
6.837
6.421
-0.416
6.421
6.601
0.180
-0.236
0.120
-0.069
16
6.693
6.389
-0.304
6.389
6.519
0.130
-0.174
6.560
0.050
-0.063
17
6.631
6.454
-0.177
6.454
6.754
0.300
0.123
6.682
0.132
-0.019
18
6.739
6.284
-0.455
6.284
6.654
0.370
-0.085
Ave
6.701
6.377
-0.325
6.377
6.587
0.210
-0.115
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Figure 6: Each flasks’ pH after adding 5mL kerosene and reacting with OEM then algal balls, with an overall -0.115 pH change from the initial pH, showing a decrease to an average of 6.377 pH after bacteria has reacted, and an increase in pH to 6.587 with the algal balls.
Figure 7: Each flasks’ pH after adding 10mL kerosene and reacting with OEM then algal balls, with an overall -0.243 pH change from the initial pH, showing a decrease to an average of 6.210 pH after bacteria has reacted, and an increase in pH to 6.502 with the algal balls.
10mL Kerosene: The highest amount of pH change was measured from the 10mL of kerosene added, with the greatest change from bacteria being -0.637 pH and lowest of -0.436, with an average of -0.534 pH. The pH values converged toward 6.199, with the majority of data results within a range of ±0.02 pH (Flasks 20 to 23). There is a similar amount of pH increase after having added the algal balls, with the pH change being 0.292, 0.082 higher than the 5mL kerosene. This similarity is likely due to the outlier in Flask 17 in Table 4, increasing the mean pH to its higher value.
Two independent hypothesis tests were conducted on the data collected via ANOVA:
Table 5: Measured pH of solutions with 10mL kerosene added, to 3 d.p. Flask pH
After Adding OEM
After Adding Algal Balls
Total
Initial
Final
∆
Initial
Final
∆
19
6.663
6.177
-0.486
6.177
6.477
0.300
-0.186
20
6.762
6.192
-0.570
6.192
6.582
0.390
-0.180
21
6.830
6.220
-0.610
6.220
6.500
0.280
-0.330
22
6.641
6.205
-0.436
6.205
6.485
0.280
-0.156
23
6.836
6.199
-0.637
6.199
6.449
0.250
-0.387
24
6.733
6.267
-0.466
6.267
6.517
0.250
-0.216
Ave
6.744
6.210
-0.534
6.210
6.502
0.292
-0.243
First, it was conducted on the change in pH from bacteria for each different amount of kerosene, and it was found that the pH change of the 10mL kerosene flask was a significantly different to the flask without kerosene, with the P-Value of 4.6861×10-7 being far below the Alpha (α) Value of 0.05. Table 6: ANOVA results showing F-Statistic and P-Value of the pH change from bacteria Source
Sum of Squares SS
Degrees of Freedom
Mean Square MS
F-Statistic
P-Value
Treatment
0.7607
3
0.2356
25.6282
Error Total
0.1838 0.9445
20 23
0.0092
4.6861 ×10-7
Table 7: ANOVA results showing F-Statistic and P-Value of the pH change from algal balls Sum of Squares SS
Degrees of Freedom
Treatment
0.1615
3
0.0538
Error Total
0.1323 0.2938
24 27
0.005
Source
Mean Square MS
F-Statistic
P-Value
9.7657
0.0002
Discussion Effectiveness of Pseudomonas Bacteria The OEM sourced from Edvotek proved to be sufficient in being able to metabolise the kerosene layer of the solution, utilising the carbon to produce energy-dense ATP molecules through the use of alkane hydroxylases (Bekele et. al, 2023), which can be inferred from the production of carbonic acid formed through the increase in CO2 concentration, a
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byproduct of hydrocarbon metabolism and cellular respiration while the kerosene is being broken down (Tsang, 2020). The CO2 gas formed through the equation for this reaction dissolves into water, forming H2CO3, allowing for the dissociation of [H+] ions therefore reducing the pH of the overall solution. The Bushnell-Haas Broth was therefore key to this deduction, using the broth’s lack of carbon-containing molecules to test for the OEM’s ability to metabolise kerosene, contributing to the change in the pH through the requirement of carbon. This could be seen from the data, as the pH change of the experiments with kerosene were significantly different to the control group of flasks, with changes of more than 0.15 pH compared to the stagnant -0.094 change. The amount of kerosene added also influenced the amount of carbonic acid produced, as the 10mL flasks exhibited a much lower pH than the 5mL, and similarly 5mL to 1mL flasks, demonstrating twice the concentration of hydronium ions from the increased amount of kerosene available to metabolise from the 10mL to 5mL (Figure 6). However, as stated in 10mL results, the pH of the majority of solutions coincided at 6.199 pH, demonstrating a lower limit in the Pseudomonas bacteria’s metabolism of the hydrocarbon. This halt in production thereby implies there is a negative feedback loop of the carbon dioxide production, where the further formation of CO2 suppresses further alkane hydroxylase activity as stated by (Beilen & Funhoff, 2007). Effects on Local Environment However, due to the acidification of the local environment (represented by the conical flask solution), the byproducts of ATP production may cause collateral damage to other organisms, due to the denaturation of the microorganisms’ enzymes. The algal balls were generally able to restore the pH of each solution to the initial pH from after bacteria was added, with the 1mL test demonstrating a -0.019 total change in pH, and 5mL kerosene test added showing -0.115 pH. It can therefore be inferred that the algal balls were able to restore the local environment of each solution, using up CO2 and therefore H2CO3, thereby reducing the impact of freefloating [H+] ions. However, the algal balls were not able to counteract the solutions with 10mL of kerosene added within the 24 hours, as the pH range increased to a maximum of 6.582 pH, still lower than the initial minimum of 6.641 (Figure 6). This shows the OEM’s potentially major impact on any environments it is introduced to, altering the ecosystem through the major increase in hydronium ions produced whilst
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also outcompeting native species, consisting up to 90% of the microbial community (Tsang, 2020).
Figure 8: Compared averages of solution pH with 0mL, 1mL, 5mL, and 10mL kerosene added after reacting with OEM, then algal balls, showing the pH lowering from H2CO3 and increasing from photosynthesis CO2 uptake.
Experiment Limitations The experiment's scope was shaped by predefined scheduling boundaries and material availability. The 2-3 week timeline required coordinating the preparation of solutions and culture with lab staff availability and school schedules. Additionally, the study was narrowed to specific resources available: select Pseudomonas strains from Edvotek, a single hydrocarbon type (kerosene), and immobilised algal balls rather than a marine algae species. Future Directions Future related research projects should build on the use of the Pseudomonas bacteria, for example performing experiments with different vessel shapes, such as wider necks promoting more aeration from the larger surface area interface between the kerosene and broth, simulating ocean conditions better while allowing for more metabolism of kerosene.
Conclusion In conclusion, the small-scale testing of the OEM Pseudomonas bacteria was able to demonstrate its ability to metabolise hydrocarbons of 2.4mL and 12mL of kerosene per 100mL of water within 24 hours. However, a critical limit of kerosene was measured at 24mL per 100mL water, where the concentration of carbonic acid produced by the OEM surpassed the capabilities of the algal balls to restore the pH to the original 6.8 pH of the Bushnell-Haas Broth. Therefore, this imbalance highlights that while a large-scale bioremediation effort may fail due to the
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acidification of the local ecosystem, this method of using oil eating microbes and algae in tandem could be utilised in a controlled waste management system for oil spills.
Declaration of use of AI Artificial Intelligence was utilised throughout the research project, starting as a way of finding and streamlining tailored research reports, such as the method of researching hydrocarbon interactions with OEMs with organic molecules without the effect of other elements (HiMedia Laboratories, 2020). In addition, AI was consulted after Step 1 of the methodology for potential reasons for the algal balls splitting, discerning the presence of the kerosene toplayer and magnetic stirrer damaging the external structure of the algae (See Appendix). Lastly, AI was also used to proofread the report for errors and draft the Declaration of AI Usage, to ensure an accurate declaration without missing any detail.
Acknowledgements I’d love to truly thank my research supervisors, Mrs. Kirsten Prior and Mrs. Virginia Ellis for their tireless, guiding me with their bottomless wisdom and assistance in each step of the project. In addition, I would also like to extend my gratitude to Barker College and the Lab Staff for providing amazing assistance during the research project, such as preparing the Bushnell-Haas Broth and activating the starter culture of Pseudomonas bacteria.
References Al-Haleem, A. A., Awadh, S. M., & Saeed, E. A.-J. (2013). View of Environmental Impact from Drilling and Production of oil Activities: Sources and Recommended Solutions. In Uobaghdad.edu.iq. https://www.ijs.uobaghdad.edu.iq/index.php/eijs/article/vie w/11966/4528 Anova Tukey HSD Calculator. (2026). Astatsa.com. https://astatsa.com/OneWay_Anova_with_TukeyHSD/_res ult/ Beilen, J. B. van, & Funhoff, E. G. (2007). Alkane hydroxylases involved in microbial alkane degradation. In Applied Microbiology and Biotechnology (Vol. 74, Issue 1). Bekele, G. K., Gebrie, S. A., Abda, E. M., Sinshaw, G., Haregu, S., Negie, Z. W., Tafesse, M., & Assefa, F. (2023, March 6). Kerosene Biodegradation by Highly Efficient Indigenous Bacteria Isolated From HydrocarbonContaminated Sites. Nih.gov. https://pmc.ncbi.nlm.nih.gov/articles/PMC9989413/ Cranney, K. (2022, April 21). How our new technology improves oil spill response. Csiro.au; CSIRO. https://www.csiro.au/en/news/all/articles/2022/april/howour-new-technology-improves-oil-spill-response
Edvotek. (2015). EDVO-Kit 956 Bioremediation by OilEating Bacteria. EDVOTEK. Heider, J., Spormann, A. M., Beller, H. R., & Widdel, F. (1998). Anaerobic bacterial metabolism of hydrocarbons. FEMS Microbiology Reviews, 22(5), 459–473. https://doi.org/10.1111/j.1574-6976.1998.tb00381.x Hemly, Q., Laksmono, R., & Kardena, E. (2015). Bioremediation of Aged Petroleum Oil Contaminated Soil: from Laboratory Scale to Full Scale Application (pp. 326– 333). Elsevier Science Ltd. HiMedia Laboratories. (2020). Bushnell Haas Broth Technical Data. HiMedia Publications. Kalme, S., Parshetti, G., Gomare, S., & Govindwar, S. (2008). Diesel and kerosene degradation by Pseudomonas desmolyticum NCIM 2112 and Nocardia hydrocarbonoxydans NCIM 2386. PubMed. Ke, C.-Y., Qin, F.-L., Yang, Z.-G., Sha, J., Sun, W.-J., Hui, J.-F., Zhang, Q.-Z., & Zhang, X.-L. (2020). Bioremediation of oily sludge by solid complex bacterial agent with a combined two-step process. Ecotoxicology and Environmental Safety, 208(1). Kingston, P. F. (2002). Long-term Environmental Impact of Oil Spills. Spill Science & Technology Bulletin, 7(1-2), 53– 61. Li, G., Xiao, W., Yang, T., & Lyu, T. (2023). Optimization and Process Effect for Microalgae Carbon Dioxide Fixation Technology Applications Based on Carbon Capture: A Comprehensive Review. C, 9(1), 35. https://doi.org/10.3390/c9010035 MolView. (2026). Molview.com; MolView. https://app.molview.com/ National Oceanic and Atmospheric Administration. (2021). Oil spills. Noaa.gov. https://www.noaa.gov/education/resourcecollections/ocean-coasts/oil-spills neo. (2025, May 13). The Deepwater Horizon Disaster. Www.youtube.com. https://www.youtube.com/watch?v=lyyIvxy2Z4 PASCO Scientific. (n.d.). Wireless pH Sensor PS-3204. PASCO Scientific. Paul, R. (2010, July 5). Oil Spill Costs Rise to $3 Billion as BP Tests World’s Largest Skimming Ship. Inhabitat - Green Design, Innovation, Architecture, Green Building | Green Design & Innovation for a Better World. https://inhabitat.com/oil-spill-costs-rise-to-3-billion-as-bptests-worlds-largest-skimming-ship/ Pete, A. J., Bharti, B., & Benton, M. G. (2021, April 29). Nano-enhanced Bioremediation for Oil Spills: A Review. Pubs.acs.org/Estengg; ACS ES&T. https://pubs.acs.org/doi/10.1021/acsestengg.0c00217 Raven, J. A., Giordano, M., Beardall, J., & Maberly, S. C. (2011). Algal and aquatic plant carbon concentrating mechanisms in relation to environmental change. Photosynthesis Research, 109(1-3), 281–296. https://doi.org/10.1007/s11120-011-9632-6 Shamasundar, B., Collier-Oxandale, A., Blickley, J., Sadd, J., Chan, M., Navarro, S., Hannigan, M., & Wong, N. J. (2018). Community-Based Health and Exposure Study around Urban Oil Developments in South Los Angeles.
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International Journal of Environmental Research and Public Health, 15(1).
ASM.org. https://asm.org/articles/2020/april/howmicrobes-clean-up-oil-lessons-from-the-deepwat
Southern Biological. (n.d.). BIO LAB: Algal Ball Photosynthesis. Parts of the method were used in the experiment, comparing the rate of CO2 uptake in vials with differing amounts of kerosene.
Wikipedia. (2025, March 5). Kerosene. Wikipedia.org; Wikimedia Foundation, Inc. https://en.wikipedia.org/wiki/Kerosene
Thomas, A., Ramkumar, A., & Shanmugam, A. (2022). CO2 acidification and its differential responses on aquatic biota – a review . Environmental Advances, 8. Tsang, J. (2020, April 19). How Microbes Clean up Oil: Lessons From the Deepwater Horizon Oil Spill | ASM.org.
Wu, H., Zou, D., & Gao, K. (2008). Impacts of increased atmospheric CO2 concentration on photosynthesis and growth of micro- and macro-algae. Science in China Series C: Life Sciences, 51(12), 1144–1150. https://doi.org/10.1007/s11427-008-0142-5
Appendix Results of Methodology Step 1: Buffer Solution After placing each of the 12 conical flasks on their respective magnetic stirrers, Both buffer and Bushnell-Haas solutions were allowed to stir, unaltered, with the belief that the bacteria and algal balls would be able to survive the artificial ocean environment. However, after 3 hours, most of the algal ball surfaces were discovered to have begun cracking, and had also started to disappear in 4 of the conical flasks, dissolved within the Buffer Solution. This effect, however, was not as noticeable within the Bushnell-Haas Broth, where the algal balls had only become slightly cracked. As such, the magnetic stirrers were deactivated, where a discussion was held to identify potential reasons, for which the magnetic stirrer was determined to be the major contributing factor. AI was consulted for potential causes for the algal balls’ disappearance. It provided 3 additional possible reasons; lack of oxygen, high salt concentration, and enzyme byproducts dissolving the algal matrix. These combined factors would have resulted in a solution with lower pH than the algal balls could tolerate, and along with a consistent lack of oxygen from the hydrophobic layer of kerosene above the water, would have broken through the outer surface where digestive enzymes could then fully dissolve the algae from the increased surface area. As such, for the Bushnell-Haas Broth test, the algal balls were separated into a secondary step, where the solution with bacterial byproducts was transferred into a vial with the algal balls, preventing the balls’ structural loss from the excessively harsh environment within the conical flask.
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Antimicrobial inhibition of Vinegar and Salt against Escherichia coli (E.coli) Baxter McLaughlin Barker College This investigation examined whether varying the ratio of acetic acid (vinegar) and salt (NaCl) across a fixed total concentration of 2% influences the inhibition of Escherichia coli growth, as measured by zone of inhibition (ZOI) using the disk diffusion method. Six conditions were tested, ranging from 2% acetic acid / 0% NaCl to 0% acetic acid / 2% NaCl, with five replicates per condition across 30 nutrient agar plates inoculated with E. coli and incubated at 30°C for 48 hours. Results demonstrated a bell-shaped pattern of inhibition, with the peak ZOI of 11.4 mm recorded at the equal 1% acetic acid / 1% NaCl ratio, and lower values at both extremes. The asymmetry of the curve, whereby acetic acid-dominant conditions consistently outperformed salt-dominant conditions, confirmed acetic acid as the primary antimicrobial agent, with NaCl acting synergistically to enhance its efficacy through osmotic membrane disruption. These findings support the hypothesis that combined stressors produce greater inhibition than either agent alone at equivalent concentrations, consistent with established MIC data and existing literature on synergistic antimicrobial mechanisms (Entani et al., 1998). Limitations of the disk diffusion method, including the influence of agar diffusion properties on ZOI measurements, are acknowledged.
Literature Review Escherichia coli overview Escherichia coli (E. coli) is a rod-shaped bacterium commonly found in the intestinal tract of warm-blooded animals. Most strains are harmless and play important roles in digestion, nutrient processing, and gut microbiome health. The nature of E. coli as an efficient reproductive neutrophile (Figure 1), therefore delineates its adaptation to environmental change, consequently becoming a model organism in microbiology research. (World Health Organization, 2018). Considering their typical environment of the digestive tract, the pH in this region of warm-blooded mammals is typically pH 5.7 - 7.4, while in a study whereby E. coli was grown in a solid medium, it was determined that the range of E. coli growth ranged from approximately pH 4.4 - 9.0. This therefore presents constructive theory that suggests that the addition of an acidic stressor (vinegar) will reduce the pH of the solution, inhibiting metabolic output, ultimately decreasing optimal growth and survival of E. coli, therefore theoretically triggering subsequent biofilm formation and Adenosine Triphosphate (ATP) conservation. (Yagnik et al., 2018)
Figure 1: Optimum bacterial pH (Source: Lumen Learning, 2019)
Antimicrobial properties of vinegar Chemically, culinary vinegar is a dilute compound of acetic acid and water and is widely recognised for its antimicrobial properties. Its effectiveness arises both from chemical characteristics and its ability to disrupt essential cellular processes in bacteria. Acetic acid being a weak acid with a pKa of approximately 4.76 (Morsch, 2015), incites partial dissociation; consequently, the compound will dissociate as shown in Figure 2: This undissociated form is particularly important in antimicrobial activity because it can freely diffuse across the selectively permeable membrane of E.coli. Therefore, this diffusion of partially dissociated CH3COOH into E. coli cells, decreases the internal pH through dissociation into CH3COO- and H+ ions, having a detrimental effect on growth and survival. (Maurer et al., 2005) This dissolution places stressors on vital enzymes involved
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Scientific Research in School Volume 8 Issue 1 2026 in protein synthesis or the processes of binary fission, decreasing the rate of reaction and in some cases, causing the enzymes to denature yielding no reaction at all. Furthermore, in response to metabolic inhibition, E. coli expends significant amounts of energy attempting to restore pH homeostasis, whereby this impairment of sufficient enzyme capability slows metabolic processes essential for decreasing Ea essential to maintain practical efficiency for cell function and cell processes. As a result, the cell reproduction is limited. (Maurer et al., 2005)
Figure 2: Partial dissociation of Acetic Acid
Antimicrobial properties of salt (NaCl) Sodium chloride (NaCl) has been used for centuries as a preservative due to its ability to inhibit microbial growth. Its antimicrobial action is primarily linked to osmotic stress. When bacteria are exposed to high-salt environments, water is drawn out of the cell through osmosis, leading to plasmolysis — the shrinkage of the cytoplasm away from the cell wall. This loss of water disrupts essential biochemical reactions, reduces enzyme activity, and slows or halts cell division (Pilizota & Shaevitz, 2013). While many bacteria can tolerate moderate salt concentrations, high levels of NaCl create conditions that are unfavourable for growth. E. coli possesses some degree of salt tolerance but still experience significant stress under hypertonic conditions (Li et al., 2021). Salt is generally considered bacteriostatic rather than bactericidal, meaning it inhibits growth without necessarily killing the cells (Medvedova et al., 2021). Antimicrobial properties of salt (NaCl) Sodium chloride (NaCl) has been used for centuries as a preservative due to its ability to inhibit microbial growth. Its antimicrobial action is primarily linked to osmotic stress. When bacteria are exposed to high-salt environments, water is drawn out of the cell through osmosis, leading to plasmolysis — the shrinkage of the cytoplasm away from the cell wall. This loss of water disrupts essential biochemical reactions, reduces enzyme activity, and slows or halts cell division (Pilizota & Shaevitz, 2013). While many bacteria can tolerate moderate salt concentrations, high levels of NaCl create conditions that are unfavourable for growth. E. coli possesses some degree of salt tolerance but still experience significant stress under hypertonic conditions (Li et al., 2021). Salt is generally considered bacteriostatic rather than
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bactericidal, meaning it inhibits growth without necessarily killing the cells (Medvedova et al., 2021). Antimicrobial properties of conditions Salt can also interact synergistically with other antimicrobial agents. Research in food microbiology has shown that combining salt with weak acids, such as acetic acid, enhances antimicrobial effectiveness, (Kavita Bhavin Sorathiya et al., 2025). Therefore, the synergistic effects of acetic acid and salt is such that theoretically, there will be a greater and more complex strain, including elements that target internal pH and osmotic regulation systems, cumulating in physiological stress. Salt-induced membrane stress can increase the permeability of the outer membrane, increasing proton influx in the cytoplasm to ultimately decrease internal pH and reduce metabolic processes such as reproduction (Pilizota & Shaevitz, 2013). With 2% acetic acid concentration, its nature as an antimicrobial substance will lower the internal pH and reduce metabolic efficiency of E.coli. Similarly, for 2% salt concentration, due to osmotic gradients and ultimately plasmolysis (Figure 3), the cell becomes hypertonic, which can weaken the E.coli's ability to regulate internal pH (Tamang, 2023). Therefore, in the intermediate tests, whereby the E.coli are exposed to two simultaneous stresses in vinegar and salt, can impair metabolic and overall cell function, consequently increasing the zone of inhibition (ZOI) in a bell-curve shape.
Figure 3: Plasmolysis of a cell (Source: Tamang, 2023)
Justification of zone of inhibition By measuring results using a zone of inhibition, the results are reliably linked to the degree which E.coli growth is inhibited by the antimicrobial agents. This is due to the diameter of the zone reflecting the distance at which the substance's concentration remains above the minimum inhibitory concentration (MIC) required to supress visible growth (Ericsson et al., 1960). For vinegar, the MIC is typically at 0.30.5%, and above 1% for rapid bactericidal activity (Lang, 2013). For salt (NaCl), the MIC is typically 3%, while above 8% yields complete suppression
Scientific Research in School Volume 8 Issue 1 2026 (Bidlas & Lambert, 2008). This therefore justifies salt to not be used independently, rather in conjunction with acetic acid. As seen in (Lyth, 2024), the use of a zone of inhibition test worked sufficiently and was an easily replicable method. Entani’s paper A large influence on my experiment was the graph demonstrated by (Entani et al., 1998) (Figure 4). In this experiment, the colony forming unit (CFU) abundance was measured against varying concentrations of stressors when combined with E. coli. Most notably, his experiment differs in methodology, whereby CFU was measured directly via liquid broth culture rather than via agar diffusion over an extended incubation period, that ultimately yielded different results that would not be expected to be replicated in my investigation. In his experiment, the results demonstrated that as acetic acid concentration was highest (2%), the CFU measurement was also the highest at 6.7 log CFU/mL, indicating minimal inhibition. This trend continued whereby as acetic acid concentration decreased and NaCl concentration increased to the ratio 1.6:0.4, the CFU measurement reached its lowest at 1.9 log CFU/mL, representing the point of maximum inhibition. At the common ratio 1:1, the CFU measurement was 4.2 log CFU/mL, before increasing again to 6.7 log CFU/mL as acetic acid concentration decreased further and NaCl concentration increased to 0:2. The significance of this graph is such that it stands as evidence for the antimicrobial properties of varying combinations of salt and vinegar. The minimum in the graph indicates the lowest CFU and logically the test that exhibits maximum inhibition of E.coli, indicative of a high ZOI. Contrastingly, the peaks indicate the lowest inhibition of metabolic function and similarly would induce the lowest ZOI in my independent investigation.
Research gap E.coli is one of the most studied bacteria, due to its replicable and responsive nature, therefore making it an ideal bacterium to use in investigation. This consequently invoked me to centre my investigation around E.coli, whereby I then researched into antimicrobial substances that would be applicable to a high-school class environment, easily accessible and would provide adequate results. My research ranged from studying biofilm formation, supported by (Landini, 2009), a paper which explores the acid resistance pathways (ARS) and proton-consuming systems (PCS), a route that involved stress responses, to using different types of vinegar, such as apple cider vinegar supported by (Yagnik et al., 2018), whereby his paper informed my understanding of acid regulation of internal pH. Ultimately, after extensive research, the combination of vinegar and salt in the inhibition of E.coli was limited. The research paper from (Entani et al., 1998) used this method to test the log CFU/mL of E.coli, albeit used a different method that involved a liquid broth culture. Therefore, to differentiate my investigation, I adopted a method combined with (Lyth, 2024) whereby the plating of conditions on nutrient agar would provide a related but similar relationship to the one proposed in Entani’s paper.
Scientific Research Question Do varying concentrations of vinegar and NaCl increase zone of inhibition against Escherichia coli (E.coli) on a nutrient agar?
Scientific Hypothesis If the ratio of vinegar to salt is varied across a fixed total concentration, then inhibition of E. coli will follow a bell-shaped pattern, with maximal inhibition at an intermediate vinegar–salt ratio, because acetic acid exerts a stronger direct antimicrobial effect on E. coli when combined with salt due to acidic and osmotic stresses.
Methodology
Figure 4: Data gathered that provides information for the CFU abundance when under varying concentrations of Acetic acid and NaCl (Source: Entani et al., 1998)
Sterilisation All windows and doors were closed, fans and air conditioning units were turned off to reduce the risk of contamination, while the work surfaces were sanitised with antibacterial spray and dried with clean paper towels. The Bunsen Burner was lit to ensure a controlled and sterile environment. Preparation of solutions Using a measuring cylinder, Spirit Vinegar (10% acetic acid) and commercial salt was diluted with
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Scientific Research in School Volume 8 Issue 1 2026 distilled water into 6 different sterile reagent bottles with respect to Table 1. The bottles were labelled with respect to their condition and dated. Table 1: : Dilution in reagent bottles Percent Percent Salt Dilution Acetic Dilution Mass (NaCl) Acid Salt (CH3COOH) (NaCl) (g) 2.00 0.00 0.00 1.50 0.50 0.25 1.00 1.00 0.5 0.50 1.50 0.75 0.00 2.00 1.00 0.00 0.00 0.00
Vinegar Mass (g)
Distilled water Mass (g)
Total Solution Mass (g)
50.00 37.50 25.0 12.50 0.00 0.00
0.00 12.25 24.50 36.75 49.00 50.00
50 50 50 50 50 50
Agar dressing and application The bottom faces of 6 plates were dated, labelled with their respective concentration of acetic acid (e.g. 2%, 1.5%, 1%, 0.5%, 0%) and salt (e.g. 0%, 0.5%, 1%, 1.5%, 2%), while the plate with 0% acetic acid and salt was labelled control as seen in Figure 5. This process was repeated five more times until 30 agar plates were accounted for. To each plate, 3mL of E.coli was applied using a 10mL disposable pipette. An L-shaped glass lawn spreader was sterilised with methylated spirits and then exposed to a Bunsen burner flame for 5 seconds, whereby the lawn plating procedure was used on every plate with E.coli, spreading the bacteria in one direction, then rotating the plate 90 degrees and proceeding to spread in the same direction. This process was repeated until the agar were covered evenly in E.coli. The agar was then sealed. Disk application 30 sterile disks were prepared using a sterile hole puncher on sterile filter paper to create equal size disks. After dipping forceps into methylated spirits and then exposing to a Bunsen burner flame for 5 seconds then left to cool for 10 seconds, a sterile disk was dipped into a condition until coated evenly. The disk was placed in the centre of a relevant agar plate. The plate was then resealed and incubated at 30 degrees. This process was repeated 5 times for each of the 6 conditions, totalling 30 separate applications, whereby the forceps were sterilised in between each trial.
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Figure 5: Experimental Agar Plating
Analysis The zone of inhibition was recorded by measuring the diameter of the clear area surrounding the antimicrobial agent on the agar plate using a metric ruler as seen in Figure 6
Figure 6: Analysis of ZOI (Source: Burton and Lalande, 2021)
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Results Table 2: : Experimental Data for Zone of Inhibition when E.coli is under varying concentrations of vinegar and salt Acid concent ration (%) 0.00 0.00 0.50 1.00 1.50 2.00
NaCl concen tration (%) 0.00 2.00 1.50 1.00 0.50 0.00
Zone of inhibition (mm) 1
2
3
4
5
Mean
StDev
0.00 10.0 12.0 10.0 10.0 11.0
0.00 11.0 12.0 11.0 11.0 9.00
0.00 9.00 10.0 11.0 10.0 10.0
0.00 10.0 10.0 13.0 11.0 11.0
0.00 11.0 12.0 12.0 11.0 9.00
0.00 10.2 11.2 11.4 10.6 10.0
0.00 0.80 0.98 0.49 1.32 1.17
The results obtained (Table 2) support the curve presented in Entani’s paper. At 2% acetic acid concentration (AAC) and 0% NaCl concentration (NCC), the tests produced an average ZOI of 9.8mm, to where this increased at a decreasing rate to 11.2mm for 1.5% AA and 0.5% NCC. The ZOI for the 5 samples peaked at 11.4mm for 1% AA and 1% NCC, whereby the average ZOI for 0.5% AA and 1.5% NCC dropped noticeably to 10.6mm. The final data point measured was 0% AAC and 2% NCC, whereby the average ZOI was 10.2mm. The trend in Figure 7 demonstrates an obvious trend that at a point where the antimicrobial substance is in sufficient effect (1% each), they are able to inhibit E.coli growth most effectively. The ZOI increases until this point in conditions 1 and 2, then decreases again after this point in conditions 4 and 5. Noticeably, the average ZOI was not uniform providing a symmetrical curve, rather the tests that were vinegardominant against salt, tests 1 and 2, had a higher average ZOI when compared to the tests that were saltdominated against vinegar, tests 4 and 5.
Figure 7: Zone of Inhibition Experimental Data Gathered
Notably, in Figure 8, the error bars demonstrate considerable variation in consistency across conditions — conditions 2 and 3 display the tightest standard deviations, indicating high reproducibility at acetic acid-dominant and equal ratio conditions, while tests 4 and 5 exhibit the largest error bars, spanning approximately 9.5–11.5mm and 9–10.8mm respectively, reflecting greater variability in the salt-
dominant trials. Test 3, despite containing the dataset's largest individual measurement of 13mm, maintained a relatively narrow error bar, suggesting that the majority of trials clustered closely around the mean and lending statistical credibility to the identified peak.
Figure 8: Standard Deviation of Experimental Data - Test number correlates to condition tested, in ratio vinegar to salt, {1 – 2:0, 2 – 1.5:0.5, 3 – 1:1, 4 – 0.5:1.5, 5 – 0:2}
Discussion The investigation aimed to determine how antimicrobial agents influenced the growth of E.coli on nutrient agar plates. It was hypothesised that a bellshaped curve would be produced, with a peak ZOI at test 3, (1:1 condition). Overall, the experiment indicates that there were a noticeable effect and logical pattern after multiple tests. These outcomes provide insight into the underlying mechanisms governing the response measured and align with established scientific principles, while also proving the hypothesis. The data-analysis approach used in this method was largely modelled around Tristyn Lyth’s 2024 paper. In his methodology, his use of agar plating supported my understanding and approach in my scientific experimental design. The order of steps and content from his methodology was ultimately very similar to my final methodology as a result. The disk diffusion method was selected as it enables direct comparison
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Scientific Research in School Volume 8 Issue 1 2026 of multiple antimicrobial conditions under controlled and standardised conditions, while remaining practical, cost-effective, and accessible within the constraints of a school laboratory setting. As a widely validated technique with established precedent in antimicrobial research, it also allows for meaningful comparison with existing literature. Based on the trendline gathered and interpreted in the Figure7, it is evident that neither antimicrobial agent alone produced the greatest ZOI. Rather, it was condition 3 that had the combination of agents in a 1:1 ratio. This outcome suggests that the two stressors are not simply additive but rather synergise to enhance overall inhibitory effect against the growth of E.coli on nutrient agar. This can be explained by the behaviour of salt and vinegar in low concentrations. NaCl can act independently to induce osmotic stress that compromises the structural integrity of the outer membrane of E.coli (Conner & Kotrola, 1995), increasing its permeability and channelling acetic acid molecules more effectively into the cytoplasm. Here, with both NaCl and CH3COOH being equivalent with 1% concentration, there is sufficient acid to acidify the intracellular environment’s pH substantially, all while the osmotic pressure dually amplifies delivery, therefore delineating the condition 3 as the most effective at inhibiting growth of E.coli. Moreover, the asymmetricity of the curve suggests that acetic acid was the dominant component in comparison to the NaCl. This asymmetry is directly consistent with the known MIC data: acetic acid begins inhibiting E. coli growth at concentrations as low as 0.3–0.5%, meaning even small amounts exert measurable bacteriostatic effect (Lang, 2013). NaCl, by contrast, does not reach its MIC independently until approximately 3% — a threshold not reached under any single condition in this study (Bidlas & Lambert, 2008). The salt therefore contributes to inhibition not through independent bacteriostatic action, but by allowing acetic acid to become more effective. Conditions with more acetic acid still present, even at reduced concentrations, therefore, consistently outperform equivalent conditions where salt dominates. Furthermore, this understanding of the dominance of vinegar further supports the previous paragraph establishing the vinegar as the primary stressor in test 3 (1:1 concentration). The use of a control verifies the absence of background inhibition, as no zones of inhibition were measured, validating that observed ZOI values were attributed to the test solutions rather than factors in the agar preparation. By extrapolating the results from (Entani et al., 1998) as seen in Figure 9, the graph was created.
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Figure 9: Extrapolated Results From (Entani et al., 1998) with Common Datapoints
The noticeable local minimum in the curve of the graph suggests that the growth of E. coli is at its lowest when at the 1.5:0.5 ratio between vinegar and salt, whereby the 1:1 ratio also delineates considerable inhibition of E. coli. The minimum at 1.5:0.5 differs from the maximum ZOI in test 3 measured in my investigation, most probably caused by the differing methodologies employed between the two studies. The referenced experiment measured CFU directly via liquid broth culture, whereas the present investigation employed agar diffusion over an extended incubation period — fundamentally different assay environments that influence how acetic acid and NaCl exert their effects. Additional contributing factors may include differences in total stressor concentration between the two studies, strain-specific variation in acid and osmotic tolerance, differences in incubation temperature, and the growth phase of the bacterial culture at the time of exposure, all of which are known to influence bacterial stress responses (Kanjevac et al., 2021). As such, this discrepancy likely reflects methodological variation rather than a contradiction in the underlying antimicrobial mechanisms. The standard deviation data (Figure 8) provides important insight to the central findings of this investigation. The comparatively tight error bars observed in tests 2 and 3 suggest that acetic acid, even at reduced concentrations, produces more consistent and reproducible inhibitory outcomes than NaCl at equivalent concentrations, a finding that directly corroborates the asymmetry argument and the identification of acetic acid as the dominant antimicrobial agent. Conversely, the larger error bars in tests 4 and 5 suggest that NaCl at higher relative concentrations introduces greater experimental variability, most plausibly attributable to the inconsistent nature of osmotic stress as a mechanism within an agar diffusion model. Unlike acetic acid, which diffuses predictably through the agar matrix, NaCl's inhibitory action is contingent on establishing a sustained hypertonic environment around individual bacterial cells, a condition that solid agar mediates less
Scientific Research in School Volume 8 Issue 1 2026 uniformly than liquid broth, producing the observed inconsistency across trials. This therefore reinforces the methodological limitation that ZOI measurements in salt-dominant conditions may underrepresent or inconsistently represent NaCl's true inhibitory potential, and future investigations employing broth microdilution assays would likely yield tighter, more reliable data across all tests. Several limitations effect the results yielded. For example, in the experiment’s Test 3, the measured solutions had ZOI that ranged from 10mm to 13mm, whereby a 3mm margin for discrepancies begins to induce uncertainty into the legitimacy of the acclaimed average peak. Likely caused by inconsistent disk saturation and errors in measuring, it would be best recommended that subsequent trials adopt a standardised soaking protocol with fixed immersion time. While the disk diffusion method is a widely accepted and reproducible tool for comparing relative antimicrobial activity, it should be noted that ZOI diameter reflects the diffusion distance of the solute through the agar matrix as much as it reflects direct inhibitory potency, therefore hindering the reliability of results. Future research could extend the total concentration range beyond 2% to determine whether the synergistic peak persists or shifts at higher concentrations, and to approach the independent MIC for NaCl gathered from the literature review. Moreover, producing more datapoints to align with Entani’s paper would make the two experiments more comparable from a dataoriented perspective.
Conclusion This investigation aimed to determine whether varying the ratio of acetic acid and sodium chloride across a fixed total concentration of 2% would influence the inhibition of E.coli growth, as measured by zone of inhibition on nutrient agar, with reference to Entani’s paper. The results broadly supported the hypothesis, demonstrating a bell-shaped pattern of inhibition across the six conditions tested, with maximal inhibition recorded at the equal 1% acetic acid / 1% NaCl ratio, producing an average ZOI of 11.4 mm. The data confirmed that neither agent alone produced the greatest inhibitory effect at the concentrations tested. Instead, the combination of both stressors at equal concentrations yielded the largest ZOI, consistent with the proposed synergistic mechanism whereby osmotic stress induced by NaCl increases outer membrane permeability, potentiating the
intracellular delivery of acetic acid. The asymmetry of the bell curve further confirmed acetic acid as the dominant antimicrobial agent, with NaCl functioning as an amplifier rather than an independent bacteriostatic agent at 2% concentration, a finding consistent with known MIC thresholds for both substances. Comparison with existing CFU-based literature in (Entani et al., 1998) revealed a broadly consistent inhibitory trend, with minor discrepancies in the position of the inhibitory peak attributable to fundamental methodological differences between liquid broth and agar diffusion assays. Future research should employ broth microdilution assays alongside disk diffusion to directly compare inhibitory outcomes independent of agar diffusion properties, extend the total concentration range to approach the independent MIC for NaCl, and test additional bacterial strains to determine the broader applicability of the observed synergistic mechanism. Additionally, by measuring the individual microbial effectiveness of vinegar and NaCl across increasing concentrations, there can be a deeper understanding as the synergistic effectiveness of the two, as well as their overall contribution to the microbial inhibition and stress used paired together.
Declaration of use of AI AI was used in the process of crafting this report and experiment. Specifically, Copilot was used for idea generation, whereby its role in providing topics to pursue, using prompts like “areas of scientific investigation with research gaps that can be tested in a school environment”, was used to create a starting point for investigation, whereby more in-depth research into the antimicrobial properties of both vinegar and salt and also the process of inhibition via these antimicrobial properties, that included the plasmolysis of cells, subsequently followed by the diffusion of undissociated acetic acid into the bacterium. Copilot was also used to help shape my understanding of concepts and predicted results, as with individual research used to direct and support the AI, its research into the potential responses of E.coli, that albeit were not measured directly, were used to shape understanding of the reason behind outcome of results. This included the ARS and PCS systems in E.coli that would counteract prominent acidic influx in order to mitigate stress and stabilise internal pH to maintain metabolic function. Moreover, Claude AI was used in the process of assisting writing my report. Claude AI was used to write a draft for the abstract and conclusion, after the discussion, literature review and results section was created individually, whereby afterwards, I edited sections of the abstract and conclusion in terms of phrasing and succinctness. Claude AI was used to write the part of the results
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Scientific Research in School Volume 8 Issue 1 2026 section that analyses the standard deviation graph, whereby, it then also wrote completely the discussion section that was related to the standard deviation data. Finally, Claude AI was used to edit the phrasing of my discussion, literature review and conclusion whereby it proof-read for mistakes and ensured a flow, working ultimately as a grammatical tool to check for any errors. Copilot was also used in the assistance of gathering relevant educational and credible sources that I then used to write my background research.
Acknowledgements I would like to thank Mrs Virginia Ellis and Mrs Kathy Haigh in the oversight and mentorship in my investigation. I would like to thank Dr Matthew Hill for his editing and formatting of the final investigation. I would like to acknowledge Etani’s paper that assisted in the ideation and processes of the investigation, as well as acknowledging Lyth’s methodology and its assistance in creating the methodology used in this investigation.
References Bidlas, E., & Lambert, R. J. W. (2008). Comparing the antimicrobial effectiveness of nacl and kcl with a view to salt/sodium replacement. International Journal of Food Microbiology, 124(1), 98–102. https://doi.org/10.1016/j.ijfoodmicro.2008.02.031 Conner, D. E., & Kotrola, J. S. (1995). Growth and survival of escherichia coli O157:H7 under acidic conditions. Applied and Environmental Microbiology, 61(1), 382–385. https://doi.org/10.1128/aem.61.1.382-385.1995 Entani, E., Asai, M., Tsujihata, S., Tsukamoto, Y., & Ohta, M. (1998). Antibacterial action of vinegar against foodborne pathogenic bacteria including echerichia coli. Journal of Food Protection, 61(8). Ericsson, B. H., Tunevall, G., & Wickman, K. (1960). The paper disc method for determination of bacterial sensitivity to antibiotics: Relationship between the diameter of the zone of inhibition and the minimum inhibitory concentration. Scandinavian Journal of Clinical and Laboratory Investigation, 12(4), 414–422. https://doi.org/10.3109/00365516009065406 Kanjevac, M., Bojović, B., Todorović, M., & Stanković, M. (2021). Effect of seed halopriming on improving salt
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tolerance in Raphanus https://doi.org/1450-9636
sativus
L. Kg.ac.rs.
Kavita Bhavin Sorathiya, Melo, A., Hogg, M. C., & Pintado, M. (2025). Organic acids in food preservation: Exploring synergies, molecular insights, and sustainable applications. Sustainability, 17(8), 3434–3434. https://doi.org/10.3390/su17083434 Lang, U. (2013, July). Minimal inhibitory concentration of natural vinegar and of aluminium acetate-tartrate solution. Ontology and Neurotology. https://journals.lww.com/otologyneurotology/abstract/2013/07000/minimal_inhibitory_conc entration_of_natural.4.aspx Li, F., Xiong, X.-S., Yang, Y.-Y., Wang, J.-J., Wang, M.M., Tang, J.-W., Liu, Q.-H., Wang, L., & Gu, B. (2021). Effects of NaCl Concentrations on Growth Patterns, Phenotypes Associated With Virulence, and Energy Metabolism in Escherichia coli BW25113. Frontiers in 705326. Microbiology, 12, https://doi.org/10.3389/fmicb.2021.705326 Lyth, T. (2024). Effects of essential oils extracted from native australian plants and conventional antimicrobial agents against staphylococcus albus (s.albus). Science Extension Journal, 6. Maurer, L. M., Yohannes, E., Bondurant, S. S., Radmacher, M., & Slonczewski, J. L. (2005). PH regulates genes for flagellar motility, catabolism, and oxidative stress in escherichia coli K-12. Journal of Bacteriology, 187(1), 304–319. https://doi.org/10.1128/JB.187.1.304-319.2005 Medvedova, A., Kocis-Koval, M., & Valik, L. (2021). Effect of salt and temperature on the growth of escherichia coli PSII. Acta Alimentaria, 50(2). https://doi.org/10.1556/066.2020.00213 Morsch, L. (2015, April). 5.2: Acid strength and pKa. Chemistry LibreTexts. https://chem.libretexts.org/Courses/University_of_Illinois_ Springfield/CHE_267%3A_Organic_Chemistry_I_(Morsc h)/Chapters/Chapter_02%3A_Acids_and_Bases/5.2%3A_ Acid_Strength_and_pKa Pilizota, T., & Shaevitz, Joshua W. (2013). Plasmolysis and cell shape depend on solute outer-membrane permeability during hyperosmotic shock in e. coli. Biophysical Journal, 104(12), 2733–2742. https://doi.org/10.1016/j.bpj.2013.05.011 Yagnik, D., Serafin, V., & J. Shah, A. (2018). Antimicrobial activity of apple cider vinegar against Escherichia coli, Staphylococcus aureus and Candida albicans; downregulating cytokine and microbial protein expression. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-017-18618-x.
Scientific Research in School Volume 8 Issue 1 2026
Chemistry
Three students undertook diverse Chemistry projects involving plant biochemistry, sunscreen performance and fermentation.
Chloe Woodhead investigated the effect of ultraviolet light on chalcone synthase enzyme activity in Purple Heart plants (Tradescantia pallida). Her project examines how environmental exposure may influence a key enzyme involved in the production of plant pigments and protective compounds. Patrick Anderson combined a passion of surfing and science and compared the water resistance of chemical and mineral sunscreens under simulated dynamic beach agitation. By recreating conditions associated with movement in water, his study provides a practical comparison of how different sunscreen formulations may retain their protective properties in realistic settings. Gracie Anderson investigated whether bulk or stepwise chaptalisation better mitigates osmotic stress during cherry wine fermentation. Her research applies chemical principles to a complex biological process, examining how the timing of sugar addition may influence fermentation conditions and outcomes.
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Effect of UV Light on Chalcone Synthase Enzyme Activity in Purple Heart Plants (Tradescantia Pallida) Chloe Woodhead Barker College Chalcone synthase is a gateway enzyme in the biosynthesis pathway of anthocyanins, which are a type of flavonoid and a powerful antioxidant responsible for the purple, blue, and red pigments in plants. Anthocyanin intake is linked to reduced rates of cancer, neurodegenerative diseases and inflammation due to its antioxidant properties. This study investigated the effect of UV-C exposure on the activity of the chalcone synthase enzyme in Purple Heart Plants (Tradescantia Pallida). 15 purple T. Pallida plants were exposed to different durations of UV-C radiation (0, 2, 6, 12, 20 hours), and the relative anthocyanin content of each exposure time was then quantified using colorimetric analysis of an aluminium anthocyanin assay at 450nm. This revealed that the 6-hour exposure time had the highest absorbance and anthocyanin concentration and was significantly higher than other values, indicating that the UV-C exposure had an obvious effect on the activity of the chalcone synthase and anthocyanin synthesis. After the 6-hour mark, the concentration of anthocyanins decreased, indicating reduced enzyme activity.
Literature Review Chalcone Synthase Chalcone synthase (CHS) is a key enzyme in the flavonoid biosynthesis pathway shown in figure 1, which is activated when the plant is placed under stressors such as UV, wounding, or pathogen exposure (Dao et al., 2011) The enzyme is involved in the early biosynthesis pathway of flavonoids (W. Li et al., 2020). The pathway is activated as flavonoids are reactive oxygen species (Z. Li & Ahammed, 2023), and are able to protect plants against damage from stressors such as UV radiation, cold and heat stress, pathogen exposure, heavy metal toxicity, and other forms of oxidative damage (Gould, 2004). For this reason, when plants are exposed to stressors, this biosynthesis pathway is activated to help protect the plant.
Anthocyanins Anthocyanins are a class of flavonoids which are found in, and are responsible for the pigments in red, blue, and purple fruits and vegetables (Mattioli et al., 2020). In addition to its role in plant colouring, anthocyanins have been implicated in many roles in the plant stress response and health benefits in humans when consumed (Cavaiuolo et al., 2013). The purple colour of the T. pallida is mainly due to the large amounts of anthocyanins that are found in the plant tissues (Parab & Vootla, 2026). Plants produce anthocyanins to assist in the absorption of light to defend against UV waves, as the anthocyanins present in the plant tissues are able to absorb the UV light (Steyn et al., 2002). Anthocyanins also have the ability to scavenge reactive oxygen species and free radicals, due to their high reactivity with the free radicals and ability to oxidise, and the high stability of the flavonoid radical that is formed when this occurs, preventing further oxidation by a single electron transfer reaction, by which the free radical is neutralised (Mattioli et al., 2020; Sadowska-Bartosz & Bartosz, 2024). The alternating carbon double bonds in the A, C, and B rings and the large numbers of hydroxyl groups present on the anthocyanin molecule (figure 2) allow them to easily donate electrons to free radicals to neutralise them (Lü et al., 2010; Reis et al., 2016; Sadowska-Bartosz & Bartosz, 2024)
Figure 1: process of biosynthesis of anthocyanins (Source: Liu et al., 2018)
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Figure 2: structure of anthocyanin backbone and derivatives of anthocyanins (Source: Lin et al., 2017)
Health benefits The ability of anthocyanins to scavenge free radicals and reactive oxygen species gives it its strong antioxidant properties. Free radicals, specifically reactive nitrogen and reactive oxygen species are byproducts of cellular respiration, and cause oxidative stress to the body, which involves the oxidative modifications of proteins, cellular molecules, and DNA. The damage that results from these modifications has been linked with increased inflammation, cancer, and neurodegenerative disorders (Speer et al., 2020). Due to their ability to reduce oxidative stress and damage, anthocyanin intake has been linked to a reduction in many health conditions, such as cardiovascular and neurodegenerative diseases, certain types of cancers, and other inflammatory and age-related diseases through the reduction of chronic inflammation and gene mutation from oxidative damage, which is often the precursor to these diseases (Lin et al., 2017; Speer et al., 2020). Furthermore, anthocyanins have been found to have stronger antioxidant properties than other flavonoids, due to the positively charged oxygen atom and presence of many hydroxyl groups (Liu et al., 2018). As Anthocyanins are found widely in many fruits and vegetables, such as blueberries, blackberries, red cabbage, red grapes and other purple and red fruits (Speer et al., 2020), maximising the anthocyanin content in the plant is highly important for public health, as increasing the anthocyanin intake of the population would help to decrease the prevalence of these diseases.
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Stimulation of anthocyanin production using UVC light UV radiation causes oxidative damage to cells through the interaction of the UV radiation with chromogenic groups and photosensitisers in cells, which produces reactive oxygen species such as superoxide and hydroxyl groups (Wei et al., 2024). At low doses of UV radiation, it can be beneficial to the organism, as it stimulates the synthesis of flavonoids such as anthocyanins in order to reduce the oxidative stress being applied to the plant, but at larger doses the UV becomes detrimental to the plant (Guo et al., 2008; Tian et al., 2024). This process is known as hormesis. Hormesis is a response of biological systems to exposure of a stressor, where low-dose exposures cause beneficial effects, while high-dose exposures lead to detrimental or toxic effects (Calabrese & Mattson, 2017). The low dose of UV-C light stimulates the chalcone synthase enzyme and the biosynthesis pathway of anthocyanins, allowing the plant to increase its antioxidant properties, but at high doses the UV radiation becomes detrimental to the plant tissues and causes damage which results in a decline in the enzyme activity and production of flavonoids. This phenomenon results in the biphasic curve seen in figure 3, where the enzyme activity increases with low doses of UV-C exposure, reaching a point of the highest activity, before declining due to cell damage.
Figure 3: biphasic dose-response of hormesis (Source: Alì et al., 2021)
Anthocyanin extraction and quantification Anthocyanins are highly polar and can be dissolved in polar inorganic and organic solvents, such as ethanol, to allow it to be extracted from the leaf matter. These extracted anthocyanins and other flavonoids (most of which are anthocyanins in T. pallida plants) then form an essay with aluminium chloride which has a yellow colour, as a result of the change in the pH of the solution, which causes the solution to take on a dark yellow colour, which displays maximum absorption at 420nm (Mammen & Daniel, 2012; Sultana et al., 2024). This then allows the flavonoid content to be estimated by determining the absorption of the
Scientific Research in School Volume 8 Issue 1 2026 flavonoid assay. These values can then be compared to determine the relative concentrations of anthocyanins across the different exposure times to UV-C radiation.
Scientific Research Question To what extent does UV-C radiation exposure effect the activity levels of chalcone synthase enzyme in T. pallida?
Scientific Hypothesis At low doses of UV-C exposure, the activity of the chalcone synthase enzyme will increase, which will result in a rise of the concentration of anthocyanins produced. The concentration of the flavonoids will continue to rise until a critical point is reached, where further UV-C exposure will result in oxidative damage to the plant and chalcone synthase, resulting in a decrease in the production and concentration of anthocyanin as measured by absorbance of aluminium anthocyanin assay at 450nm.
Methodology 15 T. pallida plants of approximately the same age and size were selected to be used in this experiment. These plants were all kept in the dark for a week before exposure to UV-C radiation, and 3 were selected randomly to be kept in pots in the dark without UV exposure to act as controls. The remaining 12 plants were selected randomly to be exposed at 2, 6, 12, and 20 hour intervals, at 240nm in a UV-C cabinet, with 3 plants being exposed at each interval. After exposure, the plants were left in the UV-C cabinet, as it kept them away from strong light for 24 hours to let the enzyme respond to the UV exposure. After the plant was left to rest, 2 leaves from each plant were selected, 0.6g of each leaf was sampled and ground with 10mL ethanol with a mortar and pestle. The mixture of leaf matter and ethanol was then transferred into a centrifuge tube and centrifuged for 10 minutes and 3700 rpm. The supernatant, containing the extracted flavonoids and enzymes was then collected and stored in the dark to prevent degradation. A flavonoid solution was then made, containing 0.5mL of plant extract, 1.5mL of pure ethanol, 0.1mL of 10% Aluminium Chloride, 0.1mL of 1M sodium acetate, and 2.8mL of distilled water, to make a final volume of 5mL, and then mixed to ensure it was homogenous. The solutions were then incubated for 30 minutes at room temperature and kept away from
strong light by covering the samples with aluminium foil. The reaction between aluminium chloride and the flavonoids in the leaf extract produced an aluminium flavonoid assay which had a dark yellow colour. The intensity of this colour was then measured using a PACSO colourimeter at 450nm to determine the concentration of flavonoids in the solution. Leaf solutions were then made for each of the 6 leaves at the 4 exposure times, and an average was produced of each of the absorbance of the leaves at each exposure time, and the value was plotted on a graph to determine the exposure time with the highest flavonoid concentration and determine the time with the highest enzyme activity. Once the absorptivity data was collected, an ANOVA with a Tukey HSD test was performed to determine if the results from the different times of exposure were significantly different from each other.
Results The results of the experiment are displayed in table 1, which shows the values of absorption of each test at each exposure time. Figure 4 shows the graph of the average absorption of the solution across the exposure times to UV-C radiation, as well as the standard deviation of each test. finally, table 3 displays the results of an ANOVA and post hoc Tukey HSD test. Table 1: absorption values of each sample at 450nm and exposure time to UV-C radiation UV-C exposure time 0 2 6 12 20
Absorption at 450nm
#1
#2
#3
#4
#5
#6
Average
0.293 0.384 0.935 0.574 0.390
0.352 0.386 0.611 0.430 0.487
0.340 0.435 0.495 0.369 0.638
0.438 0.429 0.807 0.372 0.503
0.405 0.382 0.627 0.381 0.473
0494 0.308 0.653 0.375 0.408
0.387 0.387 0.688 0.417 0.483
Figure 4: graph of absorption values and the time of exposure to UV radiation
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Scientific Research in School Volume 8 Issue 1 2026 The ANOVA values were as follows: P-Value: 4.19x10-5 F-Statistic: 10.41 F-Critical: 4.15 The results from the ANOVA test reveal that there is a significant difference between at least two of the tests, as the P value of 4.19x10-5 is lower than the alpha value of 0.05. Table 2: Post hoc Tukey HSD test values, grey rows indicating a significant difference between the treatments Treatment comparison pair 0 – 2 hrs 0 – 6 hrs 0 – 12 hrs 0 – 20 hrs 2 – 6 hrs 2 – 12 hrs 2 – 20 hrs 6 – 12 hrs 6 – 20 hrs 12 – 20 hrs
Tukey HSD Q statistic 0.0085 7.6652 0.7597 2.4489 7.6567 0.7512 2.4405 6.9054 5.2162 1.6892
Tukey HSD p-value 0.8999947 0.0010053 0.8999947 0.4353760 0.0010053 0.8999947 0.4387771 0.0010053 0.0088442 0.7304346
The results of the post hoc Tukey HSD test revealed that the absorption values of the 6-hour UV-C exposure time were significantly different from all other tests. This means that the chalcone synthase activity was significantly higher at the 6-hour exposure time.
Discussion The results of this experiment support the hypothesis that low doses of UV-C exposure will increase the activity of the chalcone synthase enzyme and result in a higher concentration of flavonoids, and that this effect reverses at higher doses, due to the denaturisation of the chalcone synthase enzyme. The results of the Tukey HSD show that the exposure time of 6 hours had a significantly higher flavonoid concentration compared to each of the other times of exposure. The average absorption of the solution of flavonoids at the 6-hour time was 0.688. This value was 77.78% higher than the control value of 0.387 average absorption at 0 hours exposure. In addition to this, reduced absorption data was recorded at exposure times above and below 6 hours. This trend of a rise and then a fall in the anthocyanin concentration is consistent with the process of hormesis and the biological hormetic response. This results in the activation of the chalcone synthase and the biosynthesis pathway of flavonoids as a result of the minor oxidative stress caused by the UV-C radiation which is placing stress on the plant. This results in the increase in the concentration of flavonoids which is demonstrated in table 1 and figure 4. The concentration of the flavonoid then falls after the 6-hour mark, when the prolonged UV-C exposure
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results in oxidative and cellular damage as well as potential denaturisation of the chalcone synthase, which results in the lower concentrations. Sources of error for this experiment arise in the quantification of the anthocyanin content. Anthocyanin content was estimated using absorption data to compare relative flavonoid content by colourimetry analysis of the aluminium flavonoid assay, as it produced a dark yellow colour, which was able to be analysed using a wavelength of 450nm. The method of comparison of the flavonoid content by comparing the raw absorption data could have resulted in the differential statistics incorrectly calculating that the differences were not significant, as the small absorption data differences could have correlated to large differences in flavonoid content. The creation of a standard curve of flavonoid concentration using quercetin to plot the absorption data to quantify the amount of anthocyanin in the sample would have minimised this error. Unfortunately, due to issues that were encountered in the creation of a quercetin standard solution, such as difficulty to make due to an issue of unknown cause where the solution would display darker colours and stronger absorption at lower concentrations, a standard curve could not be created. In addition to this, the solution absorption could only be tested at 450nm due to limitations in the colourimeter used to analyse the absorption and an inability to access a more precise colourimeter. This resulted in a wavelength of 450nm being used rather than the wavelength of 420nm where the aluminium flavonoid assay would have displayed maximum absorption (Sultana et al., 2024). Due to a wavelength of lower absorptivity being used, the absorption of the solution and estimation of anthocyanin concentration would have been consistently underestimated. However, as this error source is systematic, it would not impact the comparison between the treatments. Future work To further improve the validity of the experiment, several refinements could be made to the experiment. Firstly, a standard solution of quercetin could be made, would allow the precise concentration of flavonoids to be concentrated, to provide more precise data on the activity of the chalcone synthase enzyme. This would be more precise than comparing the average absorption data to determine the relative flavonoid levels, and therefore the relative enzyme activity, and provide more quantitative data relative to the enzyme levels. Further work could also refine the period of UV exposure, to determine the precise time of UV exposure that correlates to the peak of the enzyme
Scientific Research in School Volume 8 Issue 1 2026 activity. The data collected shows that the peak of enzyme activity is around 6 hours of exposure. Further work could expose plants at times from 2-10 hours to determine the exact time and the shape of the graph around the time of the peak to determine if the shape is more parabolic. This could determine whether the peak enzyme activity is reached, then immediately declines, or if it stays high for more time then declines.
Conclusion This study demonstrated that UV-C exposure significantly increases the concentration of flavonoids in leaf tissue, and therefore the activity of the chalcone synthase enzyme which is responsible of synthesising these flavonoids. Low doses of UV-C radiation promote the production of flavonoids, specifically anthocyanins, with the average concentration of flavonoids at 6 hours exposure to UV-C being significantly higher than every other exposure time, with an average absorption of 0.688 at a wavelength of 450nm. After the optimal 6-hour time of exposure, the flavonoid concentration then significantly decreased with further UV-C exposure, likely due to oxidative damage from the UV damaging the DNA and proteins in the cell. This damage would have caused damage to the chalcone synthase enzyme and other key enzymes in the biosynthesis pathway of flavonoids, resulting in a decrease in flavonoid production and concentration.
Declaration of use of AI AI was used in the creation of the methodology for this experiment. Chat GPT was given the premise of my experiment and asked to create a methodology and provide sources for the steps. I also asked the AI to provide me with the sources for each step of the method to verify the method with research papers and ensure that the method would work as intended, and that each step and material had a purpose. I then further confirmed the methodology with my research supervisor to further ensure the validity of the method. In addition to this I read over the research papers that Chat GPT provided me with regarding the method to see what other researchers performing similar experiments about quantifying flavonoid content had done. AI was further used in finding research papers that would provide me with more background knowledge on the topic and also information that could be used in writing my literature review. I used Elicit AI to find research papers on the effect of UV light on anthocyanin content and chalcone synthase activity in plants, as it allowed me to reduce the amount of time
spent searching for specific research papers which had relevant information to my topic of research.
Acknowledgements I would like to thank Mrs Kathy Haigh for assisting me with choosing my topic of research, as well as with the preparation, data collection, and advice in writing the scientific report. I would also like to thank Mrs Virginia Ellis for also aiding in the data collection for the experiment. I would also like to thank Dr Matthew Hill for providing assistance and feedback regarding the statistics that were used to analyse the data.
References Alì, S., Davinelli, S., Accardi, G., Aiello, A., Caruso, C., Duro, G., Ligotti, M. E., Pojero, F., Scapagnini, G., & Candore, G. (2021). Healthy ageing and Mediterranean diet: A focus on hormetic phytochemicals. Mechanisms of Ageing and Development, 200, 111592. https://doi.org/10.1016/j.mad.2021.111592 Calabrese, E. J., & Mattson, M. P. (2017). How does hormesis impact biology, toxicology, and medicine? NPJ Aging and Mechanisms of Disease, 3, 13. https://doi.org/10.1038/s41514-017-0013-z Cavaiuolo, M., Cocetta, G., & Ferrante, A. (2013). The Antioxidants Changes in Ornamental Flowers during Development and Senescence. Antioxidants, 2(3), 132–155. https://doi.org/10.3390/antiox2030132 Dao, T. T. H., Linthorst, H. J. M., & Verpoorte, R. (2011). Chalcone synthase and its functions in plant resistance. Phytochemistry Reviews, 10(3), 397–412. https://doi.org/10.1007/s11101-011-9211-7 Gould, K. S. (2004). Nature’s Swiss Army Knife: The Diverse Protective Roles of Anthocyanins in Leaves. Journal of Biomedicine and Biotechnology, 2004(5), 314– 320. https://doi.org/10.1155/S1110724304406147 Guo, J., Han, W., & Wang, M. (2008). Ultraviolet and environmental stresses involved in the induction and regulation of anthocyanin biosynthesis: A review. African Journal of Biotechnology, 7(25). https://www.ajol.info/index.php/ajb/article/view/59709 Li, W., Tan, L., Zou, Y., Tan, X., Huang, J., Chen, W., & Tang, Q. (2020). The Effects of Ultraviolet A/B Treatments on Anthocyanin Accumulation and Gene Expression in Dark-Purple Tea Cultivar ‘Ziyan’ (Camellia sinensis). Molecules, 25(2), 354. https://doi.org/10.3390/molecules25020354 Li, Z., & Ahammed, G. J. (2023). Plant stress response and adaptation via anthocyanins: A review. Plant Stress, 10, 100230. https://doi.org/10.1016/j.stress.2023.100230 Lin, B., Gong, C., Song, H., & Cui, Y. (2017). Effects of anthocyanins on the prevention and treatment of cancer. British Journal of Pharmacology, 174(11), 1226–1243. https://doi.org/10.1111/bph.13627 Liu, Y., Tikunov, Y., Schouten, R. E., Marcelis, L. F. M., Visser, R. G. F., & Bovy, A. (2018). Anthocyanin Biosynthesis and Degradation Mechanisms in Solanaceous Vegetables: A Review. Frontiers in Chemistry, 6. https://doi.org/10.3389/fchem.2018.00052
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Lü, J.-M., Lin, P. H., Yao, Q., & Chen, C. (2010). Chemical and molecular mechanisms of antioxidants: Experimental approaches and model systems. Journal of Cellular and Molecular Medicine, 14(4), 840–860. https://doi.org/10.1111/j.1582-4934.2009.00897.x Mammen, D., & Daniel, M. (2012). A critical evaluation on the reliability of two aluminum chloride chelation methods for quantification of flavonoids. Food Chemistry, 135(3), 1365–1368. https://doi.org/10.1016/j.foodchem.2012.05.109 Mattioli, R., Francioso, A., Mosca, L., & Silva, P. (2020). Anthocyanins: A Comprehensive Review of Their Chemical Properties and Health Effects on Cardiovascular and Neurodegenerative Diseases. Molecules, 25(17), 3809. https://doi.org/10.3390/molecules25173809 Parab, H., & Vootla, S. K. (2026). Cyanidin-3-glucoside a natural antioxidant from Tradescantia pallida as cytotoxic agent against triple negative breast cancer. Pharmacological Research Natural Products, 11, 100765. https://doi.org/10.1016/j.prenap.2026.100765 Reis, J. F., Monteiro, V. V. S., de Souza Gomes, R., do Carmo, M. M., da Costa, G. V., Ribera, P. C., & Monteiro, M. C. (2016). Action mechanism and cardiovascular effect of anthocyanins: A systematic review of animal and human studies. Journal of Translational Medicine, 14, 315. https://doi.org/10.1186/s12967-016-1076-5
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Sadowska-Bartosz, I., & Bartosz, G. (2024). Antioxidant Activity of Anthocyanins and Anthocyanidins: A Critical Review. International Journal of Molecular Sciences, 25(22), 12001. https://doi.org/10.3390/ijms252212001 Speer, H., D’Cunha, N. M., Alexopoulos, N. I., McKune, A. J., & Naumovski, N. (2020). Anthocyanins and Human Health—A Focus on Oxidative Stress, Inflammation and Disease. Antioxidants, 9(5), 366. https://doi.org/10.3390/antiox9050366 Steyn, W. J., Wand, S. J. E., Holcroft, D. M., & Jacobs, G. (2002). Anthocyanins in vegetative tissues: A proposed unified function in photoprotection. New Phytologist, 155(3), 349–361. https://doi.org/10.1046/j.14698137.2002.00482.x Sultana, S., Hossain, M. L., Sostaric, T., Lim, L. Y., Foster, K. J., & Locher, C. (2024). Investigating Flavonoids by HPTLC Analysis Using Aluminium Chloride as Derivatization Reagent. Molecules, 29(21), 5161. https://doi.org/10.3390/molecules29215161 Tian, X., Hu, M., Yang, J., Yin, Y., & Fang, W. (2024). Ultraviolet-B Radiation Stimulates Flavonoid Biosynthesis and Antioxidant Systems in Buckwheat Sprouts. Foods, 13(22), 3650. https://doi.org/10.3390/foods13223650 Wei, M., He, X., Liu, N., & Deng, H. (2024). Role of reactive oxygen species in ultraviolet-induced photodamage of the skin. Cell Division, 19, 1. https://doi.org/10.1186/s13008-024-00107-z
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An investigation into the comparative differences between Chemical and Mineral sunscreen water resistance under simulated dynamic beach agitation Patrick Anderson Barker College This study investigates the impact of different durations of ultraviolet-C (UV-C) radiation exposure This study investigated the effectiveness of different sunscreen formulations (mineral and chemical) on simulated skin following conditions designed to emulate beach exposure. The experiment aimed to determine whether mineral and chemical formulations differed in resistance to wash-off during simulated water agitation using turbidity measurements. It was hypothesized that chemical formulations would demonstrate greater water resistance through a smaller turbidity value compared to mineral sunscreens, as chemical sunscreens absorb into the skin whilst mineral sunscreens work as a physical barrier which is more exposed to the beach elements. Experimental trials exposed sunscreen samples on 3x3 cm of pig epidermal layer, to 3.5% saltwater exposure over a 15-minute period within an orbital shaker, with wash-off values quantified through measurements of turbidity (NTU), taken with a Pasco colorimeter under turbidity settings. Results indicated that chemical formulations demonstrated a greater turbidity value compared to mineral formulations throughout testing. The results provide evidence to contradict the original hypothesis and suggest that mineral sunscreens may be more water resistant under simulated beach conditions. The investigation contributes to a greater understanding of sunscreen behavior in an alternative environment to 'water resistance' testing conditions.
Literature Review printing method for polymers include a low cost and consistent layered manufacturing (Zhao et al., 2019). However, one downside of using FDM 3D printing for
Scientific Research Question How will different durations of UV-C exposure affect Polystyrene (HIPS) and nylon?
Literature Review
variation of sunscreen application and the scoring method of how close samples are to their claimed Ultra Violet protection is not explained. Moreover, the experiment uses human samples which due to ethical restrictions, are unavailable for the intended project. Thus, a suitable replacement may be pig skin due to their similar epidermal integrity to humans, (Uhm et al., 2023). However, the experiments of pig skin do point out slight differences in pores absorption of the epidermal layer compared to human skin.
Recent studies Recent analyses have shown that many commercial sunscreens have failed to meet the standard SPF (sun protection factor) claims, raising concerns about their real-world effectiveness. (We Tested the SPF Claims of 20 Sunscreens. 16 Failed, 2025). The testing method of the study is reliable as it uses an independent, TGA approved laboratory and follows the AS/NS ISO testing standards, human samples for increased accuracy and controlled UV exposure to control variables, in addition to a standard control sunscreen with a known SPF to strengthen results. However, the small sample size (10 adults) may not reflect the real-world
Figure 1: data findings of animal epidermal integrity compared to humans
Further, the effectiveness after repeated wave exposure remains uncertain through ineffective testing, as sunscreen water effectiveness is based on The International Organization for Standardization
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Scientific Research in School Volume 8 Issue 1 2026 (ISO16217) which tests water effectiveness of sunscreen water resistance through a swim test avoiding direct impulses of jet-propelled water. (Administration (TGA), 2025). Creating uncertainty about the safety of individuals reliant on sunscreens for sun protection whilst exposed to moving bodies of water. Sunscreen categories Sunscreens are categorized into chemical and mineral sunscreens, Chemical sunscreens absorb into the skin and convert light rays into heat, whilst mineral based sunscreens create a physical barrier to block the UV light. (How Does Sunscreen Work? | Cancer Council NSW, n.d.) Chemical sunscreens are categorized by their sun protective factor (SPF) indicating the level of UV that can penetrate the sunscreen relative to without the sunscreen application ranging 0-50. Mineral based sunscreens such as Zinc oxide provide an SPF rating of between 30-40 on the higher end (Couteau et al., 2008). The study uses an in Vitro model to isolate external variables such as external environment and participant activities allowing for more objective measures of photoprotection. However, the study was conducted almost 2 decades ago, making some of their findings potentially outdated.
Figure 2: SPF value and its % of UV protection (Source: Dembny, 2019).
These differing mechanisms of action suggest that sunscreen composition may influence retention under exposure to dynamic bodies of water. These mechanisms generate conflicting theories about which sunscreen type may be most proficient. Therefore, theory alone cannot predict which sunscreen formulation will demonstrate greater water resistance. Further, a key limitation to the experiment was their sunscreens used a maximum of 25% mineral composition as per European goods administration guidelines whereas Australian sunscreens do not have a limit to their mineral composition. (Naturkosmetik, n.d.).
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Justification Individuals such as surfers use varying Chemical and Mineral agents with varying compositions; however, given the extreme variables present in a beach environment, Standard testing methods and results may not fully account for these conditions. This can significantly impact the health of surfers by increasing their risk of skin damage and melanoma (Lewandowska et al., 2023) if sunscreen is washed away by these variables such as the crashing of waves, potentially resulting in cumulative loss of sun protection. It is therefore crucial to not only consider the sun protection, but also how well the sun block can withstand the surfer’s environment. ‘Water resistant’ Properties Sunscreen wash-off behaviour is influenced by the chemical structure and polarity of the active ingredients and formulating components (Water Properties, Polarity and Role as a Universal Solvent, n.d.). These collective elements suggest water resistance is likely affected greatly by supporting sunscreen components such as waxes and polymers. Hence, it may be important to consider ingredients not stated on the ‘active agents’ section of sunscreen bottles. Mineral sunscreens such as zinc oxide typically include surface film barriers such as beeswax and clay to increase retention onto the skin. In contrast, chemical sunscreen formulations contain organic compounds designed to absorb into the epidermal layer and convert UV radiation into heat through energy conversions (The Science behind Sunscreen and SPF, n.d.). The solubility and retention of chemical sunscreens are impacted by intermolecular forces, where more hydrophobic (nonpolar) compounds show reduced solubility in polar solvents such as water (3.3, 2020). Additionally, film forming polymers contribute to improved resistance of wash-off by enhancing stability of the protective layer of the epidermal layer (Hydrophobic Polymers, n.d.). Existing research suggests multiple influences on water resistance, creating uncertainty around formulations that demonstrate superior water resistance, providing justification for the investigation of sunscreen performance under simulated beach exposure.
Scientific Research Question How effective is adherence of different sunscreen formulations in conditions that simulate dynamic water exposure? What formulations contribute to water resistance?
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Scientific Hypothesis
Table 1: List of sunscreens tested.
Mineral sunscreens will demonstrate a greater turbidity value (wash-off) under simulated wave exposure compared to chemical sunscreens due to their surface film layer being more susceptible to mechanical disruption, compared to chemically absorbed formulations that strongly adhere to skin by absorbing into the epidermal layer.
Label
Chemical sunscreen
A
Banana Boat Sport
B
Bondi Sands- Sport
Methodology Each sunscreen condition was intended to be repeated, (minimum n = 3 trials per product type). Reference standard samples without sunscreen were initially prepared and placed in the orbital shaker under identical experimental conditions to establish baseline comparative values with 250mg of sunscreen being mixed with the water in each solution until homogenous to produce the maximal turbidity value. Clean 3 x 3 cm pig skin samples from the same supplier, consisting of pig epidermal layer with minimal subcutaneous fat layer were prepared with 250mg of sunscreen applied uniformly to each sample, 5 mineral and chemical samples each. Samples were allowed 20 minutes to absorb into the sample prior to testing. Samples were placed in 150mls of 3.5% saltwater and subject to the orbital shaker at a constant speed of 250rpm for 15 minutes. Every 5 minutes, samples were removed and turbidity values of each sample were recorded 3 times and averaged to reduce recording instrument variance.
C D E
Nivea Sun- Protect and Moisture Cancer Council, Active Neutrogena Beach Defence
Mineral sunscreen Invisible Zinc Sport Priceline Zinc Sunscreen Lotion Baby Bum, mineral Key Sun, Clear Zink Surf Mud Tinted Covering
Results Controls
Figure 3: Control samples: maximal mineral turbidity over time
Instrument was calibrated with a 0 and 100 NTU standard. Sampling and recording techniques were kept constant, by suspending particulates prior to recording. Experiment was then to be repeated under identical experimental conditions to allow further statistical tests such as a two-way ANOVA and POST-HOC test, to identify significance. Statistical adjustments such as a t-test were conducted. Descriptive statistical analysis was conducted on samples to show turbidity changes and relative changes as % of maximum turbidity values (as per controls) for each sample and groups (chemical/mineral) were compared using Cohen's d to determine effect size.
Figure 4: Control samples: maximal chemical turbidity over time
Chemical sunscreen average: ≈ 407 NTU Mineral sunscreen average: ≈ 35 NTU Chemical samples demonstrated a substantially greater turbidity values compared to mineral samples and when compared relatively to control samples suggesting greater wash-off under identical experimental conditions. Chemical samples A-E were collected at the 15 minute mark of agitation in water and added together to create an average final turbidity
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Scientific Research in School Volume 8 Issue 1 2026 value, the same was done for mineral samples and these values were then compared to each other in addition to their controls: Chemical samples average turbidity at 15 minutes was (407.36 ± 153.27 NTU, n = 5) whilst mineral samples average turbidity at 15 minutes was ≈ (34.95 ± 10.13 NTU, n = 4). Further, mineral samples displayed a much lower turbidity value as a relative % of the control groups when compared to the relative wash-off rate of chemical samples. On average chemical samples showed a relative wash off 70.42 ± 27.12% (SD, n = 5), compared to their maximal turbidity scores Indicating that around 70% of chemical sunscreen was lost from the sample into the water.
Cohen's d calculation: 2.34
Figure 5: Chemical group turbidity (wash-off) values over time.
Mineral sunscreens displayed an average wash-off of 19.72 ± 12.06% (SD, n = 4). meaning around 20% of mineral sunscreen was lost from the sample into the water. Table 2: Mineral and chemical groups relative wash-off Experiment Maximum Relative Sunscreen Sample Turbidity Turbidity Wash Type (NTU) (NTU) Off (%) A 255.7 552.7 46.3 B 573.1 565.9 101.3 Chemical C 579.0 586.9 98.7 D 293.2 555.5 52.8 E 335.8 631.9 53.1 Mean 70.4 A 27.2 178.0 15.3 B 46.0 122.0 37.7 Mineral C 41.1 339.0 12.1 E 25.5 185.0 13.8 Mean 19.7
Difference between means is 50.71. Table 3: Mineral and chemical groups standard deviation Experiment Maximum Relative Sunscreen Sample Turbidity Turbidity Wash Type (NTU) (NTU) Off (%) Chemical
A B C D E
46.3 101.3 98.7 52.8 53.1
Mineral
A B C E
15.3 37.7 12.1 13.8
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-24.14 +30.86 +28.26 -17.64 -17.34 Mean -4.43 +17.97 -7.63 -5.93 Mean
582.7 952.8 798.6 311.3 300.7 70.4 19.6 323.0 58.2 35.2 19.7
Figure 6: Mineral group turbidity values over time.
A Welch's two-sample t-test comparing the mean wash-off percentages revealed t(5.4) = 3.63, p = 0.007 with a significance value of 0.05. Meaning the p < 0.05 (see reliability section for justification of welches test and uneven n sizes). Therefore, the results can be regarded as statistically significant and the null hypothesis that there would be no significant difference in turbidity between sunscreen types, can be rejected. There is significant data to contradict the original hypothesis and suggest that mineral sunscreen formulations provided greater retention under the experimental conditions. Table 4: Welch’s t-test values
T-test Statistic Mean difference (%) t-statistic
Value 50.7
Degrees of freedom (Welch) p-value (one-tailed Significance level (α) 0Result Cohen's d Effect size interpretation
5.4 0.007 0.05 Significant 2.34 Very large
3.63
Scientific Research in School Volume 8 Issue 1 2026 Discussion The data suggests that chemical sunscreens displayed a significantly higher wash off than mineral sunscreens under agitated bodies of water, suggesting that Mineral sunscreens were far greater at adhering onto the sample compared to chemical sunscreens and may therefore be far more effective in more aggressive bodies of water. The results that both sunscreen types are substantially lost in water is evident through other studies, (WaterResistant Sunscreens for Skin Protection: An in Vivo Approach to the Two Sources of Sunscreen Failure to Maintain UV Protection on Consumer Skin - PubMed, n.d.) which determined that within mineral sunscreens and mild agitation of a water bath, sunscreen filters are lost at a considerably greater rate than brand claims. The difference in means relative to controls was 50.71 percentage points, indicating substantially higher turbidity in chemical formulations under identical experimental conditions. The chemical sunscreen groups had a greater standard deviation of 27.2 compared to the mineral group of 12.1, suggesting a more inconsistent wash off rate from chemical sunscreens. The magnitude of the observed differences was objectified using Cohen's d, which standardizes the difference between group means using pooled standard deviation and difference between means. The pooled standard deviation was about 21.05 resulting in a Cohen's d value of 2.34, indicating an extremely large effect size (difference between groups) which suggests a substantial influence of sunscreen type on wash-off behavior under simulated water agitation. Sample A performed best upon assessment of A's water-resistant molecules, it contained reinforced film-system-combining acrylate cross polymers, silicone (acetyl dimethicone), and beeswax. This created a flexible, highly cohesive and non-polar hydrophobic layer that resisted both water exposure and mechanical breakdown. (Administration (TGA), 2022). Providing multiple factors of water resistance that may have attributed to its resistance to mechanical disruption during agitation.
Figure 7: Acrylates/C10-30 Alkyl Acrylate Cross polymer
Sample D also performed strongly, using a multipolymer system (VP/Eicosene Copolymer and acrylate copolymers) with dimethicone providing additional water repellence as silicone is highly nonpolar (Commissioner, 2026). However, the absence of wax-based reinforcement likely made the film slightly more prone to mechanical disruption compared to sample A, which is less favorable under conditions simulating wave exposure. :
Figure 8: Acrylates/C12-22 Alkyl Methacrylate Copolymer
Overall, performance seemed to correlate strongly with the presence of multi-polymer film systems and hydrophobic reinforcement agents such as silicones and waxes, which improved film cohesion and resistance to wash-off. Mineral sample analysis: One of the top performers in the mineral samples group was 'E' which was 'Surf Mud tinted covering cream'. In comparison to the chemical samples, the mineral sunscreen top performer contained only 6 ingredients: Zinc Oxide, Coco Caprylate/Caprate (1: coconutbased conditioner), Cera Alba (1: beeswax), Lanolin (1: moisturiser), Kaolin (1: clay), Iron Oxides (CI77491, CI77492, CI77499)(3: colourant) (The Original - Tinted Covering Cream 45g, n.d.). Comparatively the mineral samples are far more simple in composition compared to chemical samples. Unlike the chemical samples which contained complex polymers and silicon-based molecules to repel water, mineral sample ‘E’ had a far simpler composition that aimed to create a physical barrier against sunlight and water, which demonstrated a
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Scientific Research in School Volume 8 Issue 1 2026 substantially lower wash-off, suggesting formulation complexity alone may not determine water resistance performance.
Figure 9: Mineral sample E
Anomalies Anomalous trends observed were in chemical samples E and B where turbidity decreases throughout the experiment rather than increasing as expected when considering the notion that as the water is agitated it makes it more difficult for sunscreens to adhere to the sample and Turbidity, therefore increases. Chemical Sample E, Trend: 382.5 → 337.5 → 335.8 Chemical sample B, Trend: 540 → 586 → 573 These results may be due to a couple of reasons: the settling of particulates at the bottom of the solution and cuvette when being read by the colorimeter, and sampling inconsistencies during pipetting. A proposed theory may also be that the particulates of the sunscreen are water resistant and therefore many are non-polar, lipophilic molecules. During agitation sunscreen was lost into the water but then adhered to the oily surface of the sample which may have been observed as a smaller turbidity value. Although the decrease in turbidity is not present in any other chemical or mineral sample, therefore, random experimental variation remains the likely explanation for the anomalous trends. Further, Chemical sample B had a relative wash-off rate of 101%. Most likely the majority of sunscreen was washed off in experiments and then differences in pig skin samples increased the turbidity past the reference sample of 100% turbidity. Reliability was strengthened through the standardization of methodology and experimental conditions such as constant volume of water and % of salt within the water, identical pig skin size and sunscreen mass. Samples were all uniformly applied to sample and given 20 minutes for sunscreen to absorb into sample as per the body of knowledge regarding sunscreen testing (How to Apply Sunscreen, n.d.). The Pasco colorimeter was also calibrated after each sample was recorded. Each sample reading was
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recorded 3 times, and then an average was conducted. All of which with the aim to control as many random variables as possible to reduce random error. There are similar studies that affirm the loss of sunscreen filters in mild water agitations (Water-Resistant Sunscreens for Skin Protection: An in Vivo Approach to the Two Sources of Sunscreen Failure to Maintain UV Protection on Consumer Skin - PubMed, n.d.). In addition to evaluations of polymeric film properties in regard to water resistance which affirm that the more networked polymers can create a greater barrier between skin and the water (Prettypaul & Fares, 2012). There are also multiple studies to affirm that sunscreen is lost in water and consequently present in the environment (National Academies of Sciences, Engineering, and Medicine, 2022). (Tovar-Sánchez et al., 2020). All of which provide evidence to support the findings of the experiment and increase reliability. Experimental replication was not possible due to time constraints and therefore a further ANOVA analysis and pots-HOC test was unable to be conducted but intended in the methodology to identify significance between groups. However, internal reliability controls improve confidence that observed differences between groups (mineral and chemical) were primarily caused by formulation differences. Further, effect size analysis through Cohen's d was conducted to quantify the magnitude of differences between groups, providing additional descriptive support despite limited experimental replication. A welches t-test was conducted as one replicate in the mineral group was excluded from analysis due to the sunscreen formulation's failure to adhere to the biological sample, resulting in unreliable turbidity readings that did not reflect the wash-off behaviour under standardised conditions. Consequently, sample sizes were uneven, and a Welch's t-test was used to account for unequal variances. Validity was strengthened by controlling variables that make the experiment more likely to be measuring wash-off. Such as simulated salt water of 3.5% to replicate the salinity of the ocean in accordance with the NASA satellite 'Aquarius', (NASA Salinity: Overview, n.d.). Controlled wave motions using an orbital shaker at 250 RPM. Turbidity measurement was also directly related to the particles of sunscreen present in my solution and therefore lost from the sample. However, these variables have been simplified and do not fully replicate the variables present in an environment such as the beach, variables
Scientific Research in School Volume 8 Issue 1 2026 such as rouge waves and variation in wave height, energy and frequency which may increase or decrease the amount of sunscreen lost from the sample. The sample itself was pig skin which does not fully replicate human skin and had to be used due to ethical considerations (Uhm et al., 2023); (Summerfield et al., 2015). This may have had differences in skin integrity and porousness and impacted the level of sunscreen lost from the sample. For at least one sample this was observable as sample ‘D’ of the mineral groups was unable to be tested as due to the composition of agents on the sunscreen it was unable to adhere to the sample and be tested for water resistance. Overall, the variables selected and controlled make it likely that the experiment was valid in its representation of the effects of water agitation on the wash-off of sunscreens. Limitations: Replication of trials was planned in the methodology to improve reliability and perform statistical significance checks. However, due to time constraints, full experimental repeats could not be completed. Subsequently, statistical confidence in the results was reduced and random variability between samples may have been a greater influence on the observed trends than intended. Further, the experiment was only conducted over 15 minutes, meaning that the overall conclusion is that over 15 minutes, mineral samples seemed to adhere more than chemical sunscreens. Further the samples of pig skin had fat attached to the bottom layer underneath the epidermal layer, which leaches into the water and increases the turbidity. Therefore, some of the variance between samples and types of sunscreens turbidity may be due to the amount of fat that was on each sample. This was attempted to be accounted for by removing as much fat as possible. However, Variations in fat layer may have led to higher turbidity values in some groups including the control groups. A substantial limitation that significantly altered the experimental procedure of the mineral samples was the aggregation of mineral particles in the water. Originally when conducting mineral control samples, it was observed that the mineral samples were displaying turbidity values close to 0 despite being completely in the water. It was hypothesised that this was due to the aggregation of the mineral particles together which were being misread by the colorimeter. To test this theory, particles were dispersed so that the solution became more homogenous and upon retesting, turbidity the value was significantly greater. Hence, the control values had to be repeated and before every collection of data, particles were disaggregated into a homogenous solution.
Figure 10: Aggregation of mineral sample ‘D’
Conclusion Against the proposed hypothesis, mineral sunscreens showed smaller wash-off to suggest a greater water efficiency compared to chemical sunscreens, as indicated by a lower NTU measurement. Results were gathered from descriptive analysis and a t-test to identify statistical significance between experimental group means, providing significant evidence to contradict the proposed hypothesis. The results provided valuable insight into the types of sunscreens responsible for water resistance as well as the agents responsible for providing greater water resistance than others. And challenged current notions of brand claims. Ultimately chemical sunscreens may be suitable for lighter duty water exposure in social settings that favors aesthetic and appeal, but for heavy duty water sports such as surfing mineral sunscreens may be more favorable. The experiment provides data to suggest accelerated loss of water resistance under more agitated water testing that undermines many sunscreen brand claims of the duration of water resistance. However, the period of testing was only over 15 minutes, and the nature of the data does not lend itself to identifying trends through the established graph in turbidity over time. Future research can repeat the experiment under longer time periods to establish the wash-off over a surfing session of 60 minutes or more, as well as improving the reliability of the experiment. Under different ethical standards the experiment may also be conducted using human samples for more accurate data.
Declaration of use of AI Artificial intelligence tools (specifically ChatGPT) were used throughout this investigation as an assistive tool to structure background research, methodology
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Scientific Research in School Volume 8 Issue 1 2026 refinement, data analysis, and scientific communication. AI assistance supported the sourcing of background research planning, identification of relevant literature (including skin models, testing methods, and industry standards), refinement of methodology, and confirmation of the scientific relevance of the investigation. AI was additionally used to assist with descriptive analysis of turbidity data, including percentage change calculations, relative wash-off comparisons, trend identification, anomaly analysis, graph planning, and discussion development. Assistance was also used to structure reliability, validity, limitations, and future research sections. All experimental design, methodology, data collection, turbidity measurements, calculations, interpretation of results, and final scientific conclusions were independently reviewed by the student. AI was used as an analytical and organisational support tool rather than a replacement for experimental work or scientific judgement. AI was also used to assist in generating this declaration as it provides an objective summery with all prompts used
Acknowledgements I wish to thank the Barker College Science Department for investment in the Science Extension program and in my project. The access to resources and advice was very important to this project’s success. Disclaimer (chemical samples C & D): Ingredient lists are reconstructed representative formulations based on publicly available INCI patterns for these sunscreen ranges in Australia. Exact product variant compositions may differ, and specific ARTG entries were not directly accessible via public brand-level search. Information is provided using AIassisted synthesis from consistent sunscreen formulation and regulatory-aligned ingredient data and should not be treated as a verbatim official label extract.
References (Administration (TGA), 2022, 2025; Australian Regulatory Guidelines for Sunscreens, 2023; How Does Sunscreen Work? | Cancer Council NSW, n.d.; How to Apply Sunscreen, n.d.; Hydrophobic Polymers, n.d.; ISO 16217, n.d.; Mineral vs. Chemical Sunscreen, n.d.; NASA Salinity: Overview, n.d.; The Original - Tinted Covering Cream 45g, n.d.; Water Properties, Polarity and Role as a Universal Solvent, n.d.; We Tested the SPF Claims of 20 Sunscreens.
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16 Failed, 2025; Commissioner, 2026; Couteau et al., 2008; Dembny, 2019; Granger et al., 2019; Lewandowska et al., 2023; Lowe, 1996; Naturkosmetik, n.d.; Puccetti & Fares, 2014; Summerfield et al., 2015; Tovar-Sánchez et al., 2020; Uhm et al., 2023) Administration (TGA), T. G. (2022, February 9). Search the TGA website | Therapeutic Goods Administration (TGA). Therapeutic Goods Administration (TGA). Administration (TGA), T. G. (2025, September 23). Advice for sunscreen sponsors and manufacturers: Acceptance of additional SPF testing information | Therapeutic Goods Administration (TGA). Therapeutic Goods Administration (TGA). Australian regulatory guidelines for sunscreens. (2023). Commissioner, O. of the. (2026, May 28). U.S. Food and Drug Administration. FDA. FDA. Couteau, C., Alami, S., Guitton, M., Paparis, E., & Coiffard, L. J. M. (2008). Mineral filters in sunscreen products – comparison of the efficacy of zinc oxide and titanium dioxide by in vitro method. Die Pharmazie - An International Journal of Pharmaceutical Sciences, 63(1), 58– 60. Dembny, D. (2019, June 28). Sun protection factor (SPF) rating system | Dr. Ken Dembny. Granger, C., Krutmann, J., Bustos, J., Sola, Y., Hosenally, M., Trullàs, C., Andres, P., & Passeron, T. (2019). New Methodology to Evaluate Sunscreens Under Outdoor Conditions: A Double-Blind, Randomized Intra-Individual Clinical Study of a Water-Based Broad-Spectrum SPF50+ Versus SPF15 (P3) and SPF50+. Dermatology and Therapy, 9(3), 589–599. How does sunscreen work? | Cancer Council NSW. (n.d.). Retrieved November 6, 2025, from How to apply sunscreen. (n.d.). Retrieved February 6, 2026, from Hydrophobic Polymers. (n.d.). Retrieved May 20, 2026, from ISO 16217:2020. (n.d.). ISO. Retrieved November 14, 2025, Lewandowska, A., Lewandowski, T., Rudzki, G., Próchnicki, M., Laskowska, B., Pavlov, S., Vlasenko, O., Rudzki, S., & Wójcik, W. (2023). The Risk of Melanoma due to Exposure to Sun and Solarium Use in Poland: A Large-Scale, Hospital Based Case - Control Study. Asian Pacific Journal of Cancer Prevention : APJCP, 24(7), 2259– 2269. Lowe, N. J. (1996). Sunscreens: Development: Evaluation, and Regulatory Aspects: Second Edition,. CRC Press. Mineral vs. Chemical Sunscreen: Which Is Better? (n.d.). Cleveland Clinic. Retrieved December 1, 2025, NASA Salinity: Overview. (n.d.). Retrieved May 29, 2026, Naturkosmetik, myrto. (n.d.). How safe is mineral sunscreen really? Myrto Naturkosmetik. Retrieved May 6, 2026, Puccetti, G., & Fares, H. (2014). A new approach for evaluating the water resistance of sunscreens on consumers: Tap water vs. salt water vs. chlorine water. International Journal of Cosmetic Science, 36(3), 284–290. Summerfield, A., Meurens, F., & Ricklin, M. E. (2015). The immunology of the porcine skin and its value as a model for
Scientific Research in School Volume 8 Issue 1 2026 human skin. Molecular Immunology, Non-Rodent Animal Models for Immunology Research, 66(1), 14–21.
Water Properties, Polarity and Role as a Universal Solvent. (n.d.). Retrieved May 20, 2026,
The Original—Tinted Covering Cream 45g. (n.d.). Surfmud. Retrieved June 5, 2026,
We tested the SPF claims of 20 sunscreens. 16 failed. (2025, September 30). CHOICE.
Tovar-Sánchez, A., Sparaventi, E., Gaudron, A., & Rodríguez-Romero, A. (2020). A new approach for the determination of sunscreen levels in seawater by ultraviolet absorption spectrophotometry. PLOS ONE, 15(12), e0243591.
Prettypaul, D., & Fares, H. (2012). Microscopic evaluation of polymeric film properties of anhydrous sunscreen compositions and their relation to absorption and water resistance. Journal of Cosmetic Science, 63(3), 213–221.
Uhm, C., Jeong, H., Lee, S. H., Hwang, J. S., Lim, K.-M., & Nam, K. T. (2023). Comparison of structural characteristics and molecular markers of rabbit skin, pig skin, and reconstructed human epidermis for an ex vivo human skin model. Toxicological Research, 39(3), 477–484.
Water-resistant sunscreens for skin protection: An in vivo approach to the two sources of sunscreen failure to maintain UV protection on consumer skin—PubMed. (n.d.). Retrieved June 14, 2026.
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Scientific Research in School Volume 8 Issue 1 2026
Mitigating Osmotic Stress Through a Comparison of Bulk and Stepwise Chaptalisation in Cherry Wine Fermentation Gracie Anderson Barker College This study investigates the impact of different durations of ultraviolet-C (UV-C) radiation exposure This investigation examined the effect of bulk and stepwise chaptalisation on fermentation kinetics and final alcohol yield in small-scale cherry wine fermentation. It was hypothesised that gradual sugar addition through stepwise chaptalisation would reduce osmotic stress on yeast cells, resulting in more efficient fermentation and increased alcohol production compared with bulk sugar addition. Six 1 L fermentation jars containing cherry must (juice) were divided into two treatments: bulk chaptalisation and stepwise chaptalisation, with three replicate jars per treatment. Daily measurements of specific gravity (SG), pH and temperature were recorded across a 12-day fermentation period. Yeast viability was assessed on Days 2 and 5 using methylene blue staining and light microscopy. The stepwise chaptalisation treatment demonstrated lower final SG values, higher mean yeast viability percentages and a greater mean final alcohol by volume (ABV) of 7.44%, compared with 6.26% in the bulk treatment. Mean yeast viability remained consistently higher in the stepwise treatment on both sampling days. These findings suggest that gradual sugar addition reduced the effects of osmotic stress on yeast cells and improved fermentation efficiency. Overall, the results supported the hypothesis that stepwise chaptalisation enhances fermentation performance in small-scale cherry wine fermentation systems.
Literature Review Fermentation is a metabolic process in which microorganisms convert sugars into other chemical products under anaerobic conditions (El-Mansi et al., 2018). In alcoholic fermentation, yeast converts glucose, fructose and other fermentable sugars into ethanol and carbon dioxide through enzymatic reactions (Barnett, 2003). This process underpins the production of beverages such as beer, cider, and wine, and in modern winemaking it is carefully controlled because fermentation conditions influence alcohol yield, efficiency, and the sensory qualities (such as taste, visual appearance, and aromatic profile) of the final product (Saranraj et al., n.d.). Saccharomyces cerevisiae is widely used in wine fermentation because of its high ethanol tolerance, efficient sugar metabolism, and ability to survive across varied conditions (Tronchoni et al., 2022). Commercial strains such as EC-1118 are selected for strong fermentation performance and stress resistance. During fermentation, yeast metabolises sugars through glycolysis, producing pyruvate, which is then converted into ethanol and carbon dioxide (Sarris & Papanikolaou, 2016). As this occurs, sugar concentration falls while ethanol and carbon dioxide increase (Sablayrolles, 2019).
Wine fermentation is influenced by several environmental factors (Cantarelli, 1989). Temperature affects enzymatic activity, with high temperatures reducing yeast viability and low temperatures slowing metabolism (Charoenchai et al., 1998). Nutrient availability, especially nitrogen, also affects yeast growth and fermentation efficiency (Kampen, 2014), while pH influences enzyme function and microbial stability in the must (Heard & Fleet, 1988). Of these factors, sugar concentration is particularly important because it directly affects osmotic pressure around yeast cells (Rose, 1975). High sugar concentrations can place yeast under osmotic stress, where water moves out of the cell because the external environment is hypertonic relative to the cell interior (Lopez & Hall, 2026). This cellular dehydration can impair enzyme activity, slow metabolism, and reduce reproduction. Osmotic stress has been shown to delay the lag phase of fermentation, reduce fermentation rate, and negatively affect overall fermentation efficiency (Gervais et al., 1992). Yeast responds to osmotic stress partly by producing glycerol, which helps maintain intracellular water balance and stabilise cellular function (Siderius et al., 2000). However, these adaptive responses require energy and resources that may otherwise contribute to growth and ethanol
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Scientific Research in School Volume 8 Issue 1 2026 production. Excessive osmotic stress can therefore reduce yeast viability and lead to slower or incomplete fermentation (Gervais et al., 1992), particularly in the early stages when populations are still establishing (Horváth et al., 2020; Ishmayana et al., 2011). In winemaking, sugar may be added to must (fruit juice) through chaptalisation to increase potential alcohol content (GASMALLA et al., 2012; Gough, 1998; Miller et al., 2022). This is commonly used when fruit contains insufficient natural sugar to achieve the desired ethanol yield (Foods, 1992). Sugar may be added as a single bulk addition at the start of fermentation or gradually through staggered or stepwise addition (Hung et al., n.d.; Johnson, 1987; Li et al., 2011). Bulk sugar addition rapidly increases sugar concentration in the must and may expose newly inoculated yeast to high osmotic pressure (Charoenchai et al., 1998; Horváth et al., 2020). This can prolong the lag phase and reduce metabolic efficiency (Gervais et al., 1992). In contrast, stepwise chaptalisation (or fed-batch processing / batch feeding) increases sugar concentration gradually, which may reduce initial osmotic stress and support more stable fermentation kinetics (Sablayrolles, 2019). Studies of microbial fermentation suggest that gradual substrate addition can reduce stress and maintain more consistent metabolic activity (Lorenz et al., 2017). Batch-fed chaptalisation is a specialized technique where sugar is added incrementally in distinct batches during active fermentation, rather than added to the must upfront in its entirety. In industrial fermentation systems, batch-fed processes are frequently used to prevent substrate inhibition and improve overall productivity (Hewitt & Nienow, 2007; Lennox et al., 2001). Similar principles may apply to wine fermentation, but limited research has examined bulk and stepwise chaptalisation in small-scale cherry wine fermentation (Frohman & Mira de Orduña, 2013). Cherry wine fermentation presents additional considerations due to the chemical composition of cherries, including their natural sugar concentration, acidity, and nutrient profile (Bianchi et al., 2024; Chockchaisawasdee et al., 2016; Serradilla et al., 2016). Variations in these factors may influence yeast activity and fermentation progression (Reddy & Reddy, 2011). Understanding how sugar addition methods affect yeast performance within cherry must may therefore contribute to improved fermentation management and alcohol production in small-scale fruit wine fermentation systems.
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This investigation aims to compare the effects of bulk and stepwise chaptalisation on fermentation kinetics and final alcohol yield in small-scale cherry wine fermentation. By measuring specific gravity, pH, and qualitative indicators of yeast activity throughout fermentation, this investigation seeks to determine whether gradual sugar addition reduces osmotic stress and supports more efficient fermentation compared to traditional bulk sugar addition methods. Although several foundational studies used in this investigation are older, they remain relevant due to the limited quantity of recent research directly comparing bulk and stepwise (fed-batch) chaptalisation methods in wine fermentation. Much of the available literature instead focuses on broader concepts such as yeast osmotic stress, fermentation kinetics, and industrial ethanol fermentation processes. A limited number of studies investigating fed-batch fermentation in fruit wines were identified, including research conducted on kiwi wine fermentation, although these studies were not primarily focused on increasing final alcohol concentration or directly comparing sugar addition strategies. (Huang et al., 2023). More recent research into fed-batch fermentation suggests that reducing initial sugar-induced osmotic stress may improve yeast viability and fermentation efficiency, supporting the theoretical basis of this investigation (Mears et al., 2017). Research into fermentation optimisation remains economically significant due to the large global wine and fermentation industries, where small improvements in fermentation reliability, yeast health, and alcohol yield may have substantial commercial value (Roberto, 2011). Overall, the limited quantity of literature directly comparing bulk and stepwise chaptalisation in smallscale fruit wine fermentation highlights a gap in current research, which this investigation aimed to explore. Older sources were included where necessary due to the limited number of studies available on this subject.
Scientific Research Question To what extent does stepwise chaptalisation, compared to bulk chaptalisation, reduce osmotic stress and influence fermentation kinetics and final alcohol yield in small-scale cherry wine fermentation?
Scientific Hypothesis If sugar is added gradually through stepwise chaptalisation rather than as a single bulk addition, then osmotic stress experienced by Saccharomyces
Scientific Research in School Volume 8 Issue 1 2026 cerevisiae during the early stages of fermentation will be reduced, resulting in a shorter lag phase, more stable fermentation kinetics, and an equal or greater final alcohol yield in small-scale cherry wine fermentation.
Methodology Approximately 6 kg of frozen cherries were thawed and processed to produce cherry juice. The juice was strained through a sanitised nut milk bag to reduce pulp content while retaining fine suspended particles. A total of 3.010 L of cherry juice was obtained. Filtered water was added to the juice to produce a final must volume of 3.600 L. The must was thoroughly homogenised prior to measurements and division into jars. Initial measurements of specific gravity (SG), pH, liquid temperature and ambient air temperature were recorded from the homogenised master batch prior to sugar addition and inoculation of Saccharomyces cerevisiae. SG values were temperature corrected to account for the influence of liquid temperature on hydrometer density measurements. Six sanitised 1 L jars were labelled AA, AB, AC, BA, BB and BC (as shown in Figure 1). Each jar was filled with 600 g of must, measured using a digital scale. The master batch was stirred regularly during pouring to minimise settling of suspended solids and ensure consistent distribution between jars.
Figure 1: Experimental setup showing six 1 L fermentation jars fitted with airlocks used for bulk and stepwise chaptalisation trials.
Two fermentation treatments were established. Jars AA, AB and AC represented the bulk chaptalisation treatment, while jars BA, BB and BC represented the stepwise chaptalisation treatment. Bulk chaptalisation jars each received the full 32 g dextrose addition on Day 0, whereas stepwise chaptalisation jars received the same total sugar mass distributed across three additions consisting of 11 g on Day 0, 11 g on Day 2 and 10 g on Day 4. On Day 0, all jars received their respective sugar amounts – entirety for A jars, and the first increment of 11g for B jars – and swirled gently prior to yeast inoculation. The B jars were also swirled
gently subsequent to their further sugar additions on Days 2 and 4. 0.25g of Lalvin EC-1118 yeast and 0.30g of Mangrove Jack’s wine yeast nutrient were measured individually for each jar using a digital balance. The yeast and nutrient were combined in individual labelled bags and rehydrated using approximately 5–10 mL of warm water at approximately 35–40°C for approximately 5– 10 minutes. The slurry from each bag was transferred into the corresponding jar. A small quantity of must from the corresponding jar was then added back into the bag to rinse residual yeast and nutrient into the jar. All jars were gently swirled and sealed using airlocks filled with distilled water. Fermentation was conducted under ambient indoor conditions for 12 days. Ambient air temperature was recorded daily. Jars were gently swirled prior to sampling to homogenise suspended yeast and dissolved sugars. Daily measurements of SG, pH and liquid temperature were recorded for all jars. Hydrometer samples were returned to their corresponding fermentation jars immediately following measurement to minimise sample loss. The hydrometer and measuring cylinder were rinsed thoroughly with tap water followed by distilled water between samples to minimise cross-contamination. Microscopy was conducted on Days 2 and 5 prior to sugar addition in the stepwise treatment jars. Samples containing suspended yeast cells were collected from each jar following gentle swirling. Methylene blue staining was used to distinguish metabolically active and inactive yeast cells. One drop of must and one drop of methylene blue stain were placed onto a microscope slide and mixed lightly before a coverslip was applied. Slides were observed using a compound light microscope at 400× magnification. Approximately 50 yeast cells per sample were counted and categorised as live or dead based on methylene blue staining. Live cells appeared translucent while dead cells appeared dark blue. Yeast viability percentage was calculated using the proportion of live cells relative to the total number of cells counted. Qualitative observations including bubbling activity, foam formation, aroma and sediment changes were also recorded throughout fermentation. Final Alcohol By Volume (ABV) was estimated using Equation 1 (Calculation - ABV, n.d.). 131.25 is an empirical constant derived from alcohol’s density in relation to water.
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Scientific Research in School Volume 8 Issue 1 2026 Equation 1:
ABV (%) = (Original specific Gravity – Final specific Gravity) × 131.25
Results Three replicate jars were used for each fermentation treatment. Jars AA, AB and AC represented the bulk chaptalisation treatment, while jars BA, BB and BC represented the stepwise chaptalisation treatment. Quantitative measurements of specific gravity (SG), pH and liquid temperature were recorded daily across a 12-day fermentation period. SG values were temperature corrected using the measured liquid temperature at the time of recording. Fermentation Kinetics The progression of fermentation was assessed through daily SG measurements. Mean SG values were calculated from the three replicate jars within each treatment group to reduce the influence of individual variability between jars.
Figure 2: Mean specific gravity values for bulk and stepwise chaptalisation treatments across the 12-day fermentation period.
The bulk chaptalisation treatment displayed a rapid decrease in SG during the early stages of fermentation. Mean SG values decreased sharply between Days 2 and 4 before stabilising at approximately 1.004–1.005 during the later stages of fermentation. In comparison, the stepwise chaptalisation treatment displayed a more gradual decrease in SG across the fermentation period. Temporary stabilisation in SG values was observed following staged sugar additions on Days 2 and 4. Variation between replicate jars was observed within both treatment groups; however, overall fermentation trends remained consistent between replicates. The stepwise chaptalisation treatment reached lower final SG values than the bulk chaptalisation treatment by the conclusion of the experiment. One anomalous SG value was recorded for replicate jar BA on Day 2, which displayed a substantially lower SG than the other replicate jars within the stepwise treatment group. This value may have resulted from measurement variability or uneven distribution of suspended solids during sampling; however, the overall fermentation trend for the
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treatment remained consistent across the remainder of the experiment. pH Changes During Fermentation A PASCO digital pH probe was calibrated using two standard pH buffer solutions, and pH readings were displayed via the connected SPARKvue software, then recorded. Daily pH measurements were recorded throughout fermentation for all jars. Mean pH values were calculated for each treatment group.
Figure 3: Mean pH values for bulk and stepwise chaptalisation treatments across the fermentation period.
Both the bulk and stepwise fermentation treatments showed a similar pH trend throughout the experiment, with pH initially decreasing from approximately 4.2 to 3.9 during early fermentation before gradually increasing towards the end of the fermentation period. The stepwise treatment maintained a slightly higher mean pH than the bulk treatment; however, the difference between treatments was minimal. The overall magnitude of pH change observed (approximately 0.3 pH units) was relatively small and unlikely to critically inhibit yeast fermentation. Saccharomyces cerevisiae typically ferments effectively within a pH range of approximately 3.0– 4.0, with larger deviations outside this range more likely to negatively affect yeast activity and fermentation efficiency. Yeast Viability Analysis Microscopy was conducted on Days 2 and 5 using methylene blue staining to assess yeast viability. Although three replicate jars were prepared for each condition (A and B), only one jar per condition was imaged and counted via microscopy, with approximately 50 cells counted and categorised as live or dead based on staining.
Scientific Research in School Volume 8 Issue 1 2026 stepwise treatment also reached lower final SG values and produced higher final ABV values compared with the bulk chaptalisation treatment.
Figure 4: Mean yeast viability by chaptalisation treatment Table 1: Mean yeast viability percentages for both treatment groups Day 2 5
Bulk mean viability (% ± SD) 83.3 ± 1.2 87.3 ± 6.1
Stepwise mean viability (% ± SD) 91.3 ± 3.1 94.0 ± 2.0
n 3 3
Oval-shaped yeast cells were observed in all samples under microscopy. Dead yeast cells appeared dark blue following methylene blue staining, while live cells appeared translucent. Variation in viability percentages was observed between replicate jars and between treatment groups. Final Alcohol Yield Final alcohol by volume (ABV) values were calculated using original specific gravity and final specific gravity measurements for each jar.
Figure 5: Mean estimated final alcohol by volume (ABV) of bulk and stepwise chaptalisation treatments following 12 days of fermentation. Table 2: Final calculated ABV values per jar. Treatment Bulk Stepwise
Jar
OG
FG
AA AB AC BA BB BC
1.052 1.052 1.052 1.052 1.052 1.052
1.004 1.004 1.005 0.992 0.998 0.996
Approx. ABV (%) 6.3 6.3 6.17 7.88 7.09 7.35
The bulk chaptalisation treatment had a mean final ABV of 6.26%, while the stepwise chaptalisation treatment had a mean final ABV of 7.44%. The
An independent two-sample t-test assuming unequal variances was conducted to compare final ABV between the bulk and stepwise treatments. The stepwise treatment had a higher mean final ABV (7.44%) than the bulk treatment (6.26%). The analysis indicated a statistically significant difference, t(2.14) = -4.999, p = 0.0164. Therefore, the null hypothesis was rejected, suggesting that stepwise chaptalisation produced a significantly higher final ABV than bulk chaptalisation under these experimental conditions. However, this result should be interpreted cautiously due to the small sample size (n = 3 per treatment).
Discussion This investigation examined the effect of bulk and stepwise chaptalisation on fermentation kinetics and final alcohol yield in small-scale cherry wine fermentation. The results suggest that stepwise chaptalisation reduced the impact of osmotic stress during fermentation and produced more efficient overall fermentation outcomes compared with bulk chaptalisation. Stepwise chaptalisation resulted in lower final SG values with a final mean difference of 0.009 (see Figure 2), higher yeast viability percentages (a mean increase of 7.35%) (see Figure 4) and a greater final estimated alcohol by volume (ABV) (Figure 5) than the bulk chaptalisation treatment. The SG data indicated clear differences in fermentation progression between treatments. As shown in Figure 2, the bulk chaptalisation treatment displayed a rapid decrease in SG during the early stages of fermentation before stabilising at approximately 1.004–1.005. In contrast, the stepwise chaptalisation treatment demonstrated a more gradual decrease in SG before reaching lower final SG values between 0.992 and 0.998. Lower final SG values indicate a greater consumption of fermentable sugars by yeast and therefore suggest more complete fermentation. These results support the hypothesis that gradual sugar addition improves fermentation efficiency. The observed differences in SG trends may be explained by the effects of osmotic stress on Saccharomyces cerevisiae during the early stages of fermentation (Gervais et al., 1992; Rose, 1975). In the bulk treatment, the immediate addition of the full sugar concentration likely created a hypertonic environment surrounding yeast cells, causing water to leave the cells through osmosis. Cellular dehydration
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directly linked to sugar metabolism during fermentation, the higher ABV observed in the stepwise treatment suggests that yeast cells were able to ferment a greater proportion of available sugars. These findings are consistent with previous research suggesting that gradual substrate addition can improve microbial productivity by reducing inhibitory environmental stress (Siderius et al., 2000).
Temporary increases and stabilisation in SG values were observed in the stepwise treatment following staggered sugar additions on Days 2 and 4. These fluctuations likely occurred because additional dissolved sugars temporarily increased the density of the must before being metabolised by yeast cells. Similar trends have been observed in fed-batch fermentation systems, where gradual substrate addition maintains microbial activity while avoiding substrate inhibition (Hung et al., n.d.; Li et al., 2011). Despite these temporary increases, the stepwise treatment ultimately achieved lower SG values than the bulk treatment by the conclusion of fermentation.
pH trends remained relatively similar between treatments throughout fermentation. As shown in Figure 3, both treatments experienced an initial decrease in pH during early fermentation before gradually increasing and stabilising towards the end of the experiment. The initial decrease in pH likely occurred due to the production of acidic metabolic byproducts during active fermentation (Coote & Kirsop, 1976; Pan et al., 2020). However, because both treatments displayed comparable pH trends, pH was unlikely to be a major contributing factor to the observed differences in fermentation efficiency between treatments.
Differences in yeast viability further supported the proposed effect of osmotic stress on fermentation performance. As shown in Figure 4 and Table 1, mean yeast viability remained consistently higher in the stepwise treatment than in the bulk treatment on both Days 2 and 5. On Day 2, the stepwise treatment displayed a mean viability of 91.3% compared with 83.3% in the bulk treatment. By Day 5, mean viability increased to 94.0% in the stepwise treatment and 87.3% in the bulk treatment. These findings suggest that gradual sugar addition reduced physiological stress on yeast cells and supported greater cellular survival throughout fermentation.
Several limitations may have influenced the reliability and accuracy of the investigation. Fermentation was conducted under ambient indoor conditions rather than within a temperature-controlled environment. Ambient temperatures fluctuated throughout the experiment, which may have influenced yeast metabolic activity and fermentation rate (Heard & Fleet, 1988). Additionally, airlock sealing inconsistencies were observed between some jars during fermentation, potentially affecting carbon dioxide release and oxygen exposure. Although replicate jars reduced the impact of random variation, only three replicates were used per treatment, limiting the statistical reliability of the investigation.
Microscopy observations further supported these results. Yeast cells appeared predominantly ovalshaped under light microscopy, while dead cells stained dark blue following methylene blue staining. Live cells remained largely translucent. Although variation was observed between replicate jars, the overall trend of higher viability in the stepwise treatment remained consistent. Previous research has demonstrated that osmotic stress can reduce yeast viability by disrupting membrane stability and cellular water balance (Rose, 1975). Higher viability within the stepwise treatment may therefore explain the increased fermentation efficiency and greater ethanol production observed in this investigation. The final ABV results also supported the hypothesis. The stepwise treatment produced a mean final ABV of 7.44%, compared with 6.26% in the bulk treatment (Figure 5; Table 2). Since ethanol production is
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Limitations were also associated with microscopy analysis. Approximately 50 cells were counted per sample, which provided only a small representation of the total yeast population within each jar. Human error during manual cell counting and categorisation may also have affected viability calculations. Furthermore, methylene blue staining distinguishes live and dead cells based on metabolic activity, but does not provide detailed information regarding long-term cellular health or fermentation capability (Cwalinski et al., 2020; Matsumoto et al., 2022; Sami et al., 1994). Future investigations could improve the reliability of results by increasing the number of replicate jars and conducting fermentation within a controlledtemperature environment. Additional investigations could also examine the effect of different sugar addition schedules, alternative yeast strains or varying
Scientific Research in School Volume 8 Issue 1 2026 sugar concentrations on fermentation performance. Measuring dissolved oxygen concentration, carbon dioxide production or yeast population density using more advanced techniques may also provide a more detailed understanding of the relationship between osmotic stress and fermentation kinetics. Overall, the results of this investigation supported the hypothesis that stepwise chaptalisation improves fermentation performance compared with bulk chaptalisation in small-scale cherry wine fermentation. Gradual sugar addition was associated with higher yeast viability, lower final SG values and increased final ABV, suggesting that reducing osmotic stress may improve fermentation efficiency and ethanol production in fruit wine fermentation systems.
Conclusion This investigation examined the effect of bulk and stepwise chaptalisation on fermentation kinetics and final alcohol yield in small-scale cherry wine fermentation. The results supported the hypothesis that gradual sugar addition reduces osmotic stress and improves fermentation performance compared with bulk sugar addition. The stepwise chaptalisation treatment consistently demonstrated lower final specific gravity (SG) values, higher yeast viability percentages and greater final estimated alcohol by volume (ABV) values than the bulk chaptalisation treatment. Mean SG values indicated that the stepwise treatment maintained a more gradual fermentation progression before reaching lower final SG values by the conclusion of the experiment. Temporary increases in SG following staggered sugar additions were observed; however, the overall fermentation trend remained more complete than that of the bulk treatment. Yeast viability analysis also demonstrated consistently higher viability percentages in the stepwise treatment on both Days 2 and 5, suggesting that gradual sugar addition reduced physiological stress on yeast cells during fermentation. In addition, the stepwise treatment produced a higher mean final ABV of 7.44%, compared with 6.26% in the bulk treatment. Overall, the findings suggest that stepwise chaptalisation improved fermentation efficiency within this investigation by reducing the negative effects of osmotic stress on yeast performance. These results support the use of gradual sugar addition as a potential method for improving fermentation
progression and ethanol yield in small-scale fruit wine fermentation systems.
Declaration of use of AI Artificial intelligence (AI) tools were used throughout the development and communication of this scientific research project. ChatGPT AI was primarily used to support organisational planning (e.g. proposing steps and timelines), scientific writing refinement (suggesting edits to my writing for scientific accuracy and clarity of communication) and clarification of scientific concepts relevant to fermentation chemistry and microbiology to assist my general understanding. ChatGPT was used to assist in refining the wording and structure of the scientific research question, hypothesis and methodology to ensure appropriate scientific language and formatting conventions were used. AI was also used to assist in converting procedural notes into passive voice and past tense for formal scientific reporting. During experimental planning, AI was used to discuss potential controlled variables, suggest suitable measurement intervals and provide guidance on practical fermentation procedures such as hydrometer use, yeast rehydration and microscopy techniques. Suggestions generated through AI assistance were independently evaluated and adapted where necessary to suit the available equipment and experimental conditions. AI was additionally used to assist in identifying potential sources of experimental error, refining data presentation methods and calculating estimated alcohol by volume (ABV) values from specific gravity measurements. All raw data collection, experimental procedures, observations, calculations and final interpretations were completed and verified independently by the researcher. No AI-generated data, fabricated results or automatically generated conclusions were used in this project. For this project all sources were found without deliberate use of AI, typically through Google Scholar.
Acknowledgements I would like to thank each of the Science Extension supervisors as they supported me in many ways including guiding me through the experiment and offering advice, but also helping me through the process.
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and Evaluation of Its Quality. J Food Process Technol, 3, 3:7. https://doi.org/10.4172/2157-7110.1000163 Gervais, P., Marechal, P. A., & Molin, P. (1992). Effects of the kinetics of osmotic pressure variation on yeast viability. Biotechnology and Bioengineering, 40(11), 1435–1439. https://doi.org/10.1002/bit.260401119 Gough, J. B. (1998). Winecraft and Chemistry in 18thCentury France: Chaptal and the Invention of Chaptalization. Technology and Culture, 39(1), 74–104. Heard, G. m., & Fleet, G. h. (1988). The effects of temperature and pH on the growth of yeast species during the fermentation of grape juice. Journal of Applied Bacteriology, 65(1), 23–28. https://doi.org/10.1111/j.13652672.1988.tb04312.x Hewitt, C. J., & Nienow, A. W. (2007). The Scale‐Up of Microbial Batch and Fed‐Batch Fermentation Processes. In Advances in Applied Microbiology (Vol. 62, pp. 105–135). Academic Press. https://doi.org/10.1016/S00652164(07)62005-X Horváth, B. O., Sárdy, D. N., Kellner, N., & Magyar, I. (2020). Effects of High Sugar Content on Fermentation Dynamics and Some Metabolites of Wine-Related Yeast Species Saccharomyces cerevisiae, S. uvarum and Starmerella bacillaris. Food Technology and Biotechnology, 58(1), 76–83. https://doi.org/10.17113/ftb.58.01.20.6461 Huang, D., Ren, X., Zhong, Y., Liu, Y., Jiang, J., Song, Y., Xin, L., Lu, Y., & Qin, Y. (2023). Effect of multi-step chaptalisation on physicochemical properties, potentially harmful alcohols, nutritional composition and volatile profiles of kiwi wine. International Journal of Food Science and Technology, 58, 12. https://doi.org/10.1111/ijfs.16474 Hung, Y.-H. R., Chae, M., Sauvageau, D., & Bressler, D. C. (n.d.). Adapted feeding strategies in fed-batch fermentation improve sugar delivery and ethanol productivity. Bioengineered, 14(1), 2250950. https://doi.org/10.1080/21655979.2023.2250950 Ishmayana, S., Learmonth, R., & Kennedy, U. (2011, November 24). Fermentation performance of the yeast Saccharomyces cerevisiae in media with high sugar concentration. Johnson, A. (1987). The control of fed-batch fermentation processes—A survey. Automatica, 23(6), 691–705. https://doi.org/10.1016/0005-1098(87)90026-4 Kampen, W. H. (2014). Chapter 4—Nutritional Requirements in Fermentation Processes. In H. C. Vogel & C. M. Todaro (Eds.), Fermentation and Biochemical Engineering Handbook (Third Edition) (pp. 37–57). William Andrew Publishing. https://doi.org/10.1016/B9781-4557-2553-3.00004-0 Lennox, B., Montague, G. a., Hiden, H. g., Kornfeld, G., & Goulding, P. r. (2001). Process monitoring of an industrial fed-batch fermentation. Biotechnology and Bioengineering, 74(2), 125–135. https://doi.org/10.1002/bit.1102 Li, S.-Y., Srivastava, R., Suib, S. L., Li, Y., & Parnas, R. S. (2011). Performance of batch, fed-batch, and continuous A–B–E fermentation with pH-control. Bioresource Technology, 102(5), 4241–4250. https://doi.org/10.1016/j.biortech.2010.12.078 Lopez, M. J., & Hall, C. A. (2026). Physiology, Osmosis. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK557609/
Scientific Research in School Volume 8 Issue 1 2026 Lorenz, E., Runge, D., Marbà-Ardébol, A.-M., Schmacht, M., Stahl, U., & Senz, M. (2017). Systematic development of a two-stage fed-batch process for lipid accumulation in Rhodotorula glutinis. Journal of Biotechnology, 246, 4–15. https://doi.org/10.1016/j.jbiotec.2017.02.010
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Mears, L., Stocks, S. M., Sin, G., & Gernaey, K. V. (2017). A review of control strategies for manipulating the feed rate in fed-batch fermentation processes. Journal of Biotechnology, 245, 34–46. https://doi.org/10.1016/j.jbiotec.2017.01.008 Miller, K. V., Arefaine, E., Arikal, A., Cantu, A., Cauduro Girardello, R., Oberholster, A., Heymann, H., & Block, D. E. (2022). Development and Analysis of an Intensified Batch-Fed Wine Fermentation Process. Fermentation, 8(6), 268. https://doi.org/10.3390/fermentation8060268 Pan, L., Chen, X.-S., Wang, K.-F., & Mao, Z.-G. (2020). Mechanisms of response to pH shock in microbial fermentation. Bioprocess and Biosystems Engineering, 43(3), 361–372. https://doi.org/10.1007/s00449-019-022324 Reddy, L. V. A., & Reddy, O. V. S. (2011). Effect of fermentation conditions on yeast growth and volatile composition of wine produced from mango (Mangifera indica L.) fruit juice. Food and Bioproducts Processing, 89(4), 487–491. https://doi.org/10.1016/j.fbp.2010.11.007 Roberto, M. A. (2011). The Changing Structure Of The Global Wine Industry. International Business & Economics Research Journal (IBER), 2(9). https://doi.org/10.19030/iber.v2i9.3835 Rose, D. (1975). Physical Responses of Yeast Cells to Osmotic Shock. Journal of Applied Bacteriology, 38(2), 169–175. https://doi.org/10.1111/j.13652672.1975.tb00517.x
Saranraj, P., Sivasakthivelan, P., & Naveen, M. (n.d.). Fermentation of fruit wine and its quality analysis: A review. Sarris, D., & Papanikolaou, S. (2016). Biotechnological production of ethanol: Biochemistry, processes and technologies. Engineering in Life Sciences, 16(4), 307–329. https://doi.org/10.1002/elsc.201400199 Serradilla, M. J., Hernández, A., López-Corrales, M., RuizMoyano, S., de Guía Córdoba, M., & Martín, A. (2016). Composition of the Cherry (Prunus avium L. and Prunus cerasus L.; Rosaceae). In M. S. J. Simmonds & V. R. Preedy (Eds.), Nutritional Composition of Fruit Cultivars (pp. 127– 147). Academic Press. https://doi.org/10.1016/B978-0-12408117-8.00006-4 Siderius, M., Van Wuytswinkel, O., Reijenga, K. A., Kelders, M., & Mager, W. H. (2000). The control of intracellular glycerol in Saccharomyces cerevisiae influences osmotic stress response and resistance to increased temperature. Molecular Microbiology, 36(6), 1381–1390. https://doi.org/10.1046/j.13652958.2000.01955.x Tronchoni, J., Gonzalez, R., Guindal, A. M., Calleja, E., & Morales, P. (2022). Exploring the suitability of Saccharomyces cerevisiae strains for winemaking under aerobic conditions. Food Microbiology, 101, 103893. https://doi.org/10.1016/j.fm.2021.103893
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Physics and Applied Mathematics
Four students explored Physics and Data Science through projects spanning materials engineering, biomedical data analysis, mathematical modelling, and innovative materials.
Matthew Toft investigated whether multilayer epoxy coatings could mechanically reinforce fused-deposition-modelled PLA. This was an interesting way to build on previous research published in this journal on the tensile strength 3D-printed materials. Ahan Sharma produced a novel computational approach to explore biomarkers that may predict Alzheimer’s disease. Using a process known as fractional dimensional analysis on pre-obtained brain scans, he showed that even school students can provide lifechanging answers in the medical field. Eric Hayman applied mathematics to increase the tools available for scientific research. Through this complex project investigating recursive equations and their generalised functional counterparts this interesting project sets up further questions worthy of exploration. Tong Zhu studied piezoelectric performance of Rochelle salt-filled, 3D-printed honeycomb composites. Bringing together materials science, structural design, and electromagnetism, the project explores how composite geometry and composition may be used to improve piezoelectric performance.
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Mechanical Reinforcement of FDM PLA through Multi-Layer Epoxy Coating Matthew Toft Barker College This investigation examined the effect of increasing numbers of epoxy resin coating layers on the peak force and ultimate tensile strength (UTS) of fused deposition modelling (FDM) printed polylactic acid (PLA) tensile specimens. Fifty ASTM D638 Type IV specimens were printed under identical conditions and divided into five batches: one uncoated control (B0) and four coated batches (B1–B4) receiving one to four coats of West System 105/205 epoxy respectively. Coating thickness was estimated gravimetrically and used to calculate a modified cross-sectional area for each specimen. Tensile testing was conducted using a custom-made machine lent for use, the Bridge Buster 3000. Peak force increased consistently and linearly across all batches from 938.4 N (B0) to 1423.5 N (B4), a cumulative increase of 51.7%, with all pairwise batch differences statistically significant (Tukey HSD, p < 0.01). UTS increased significantly from 48.88 MPa in B0 to 53.34 MPa in B1 (9.1%), then plateaued across B1 to B4 with no statistically significant differences between coated batches (p ≈ 0.90). The UTS plateau was attributed to the approximately equal tensile strengths of cured epoxy and FDM-printed PLA, and the initial UTS improvement to a Griffith-type surface sealing mechanism. Multi-layer epoxy coating is an effective strategy for increasing peak load capacity of FDM PLA parts, though additional coats beyond the first provide diminishing returns in intrinsic material strength.
Literature Review Additive manufacturing, commonly known as 3D printing, produces objects layer-by-layer from a digital model. Fused deposition modelling (FDM), where a thermoplastic filament is heated, extruded through a nozzle and deposited along a programmed path (Figure 1), is among the most widely used forms due to its low cost, accessibility and ability to produce complex geometries without specialist tooling (Gibson et al., 2021; Kristiawan et al., 2021; Rahim et al., 2019)
filament roads and stacked layers, producing a microstructure of aligned roads, interlayer bonds and interstitial voids whose quality depends directly on process settings (Figure 2) (Chacón et al., 2017; Frunzaverde et al., 2023; Hsueh et al., 2021).
Figure 2: Digital microscope cross-section of FDMprinted PLA specimen showing interstitial voids between adjacent horizontal filament roads (Source: Perez et al., 2021
Figure 1: Schematic diagram of the FDM printing process (Source: Rahim et al., 2019)
Polylactic acid (PLA) is one of the most common FDM materials: it is affordable, biodegradable, and with a bulk tensile strength of 45–70 MPa, mechanically adequate for many applications (Farah et al., 2016; Hsueh et al., 2021). However, FDMprinted PLA cannot be treated as equivalent to moulded PLA. The object is formed from adjacent
The layer-by-layer structure of FDM introduces mechanical limitations absent in bulk material. Printed specimens contain interlayer weaknesses, small voids and imperfect road bonding, which reduce effective load-bearing cross-section and produce anisotropic behaviour where strength varies with loading direction relative to the print (Ngo et al., 2018; Popescu et al., 2018). Rankouhi et al. (2016) demonstrated through fractographic analysis that tensile failure in FDM specimens preferentially initiates at interlayer boundaries, with fracture mode strongly dependent on
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Scientific Research in School Volume 8 Issue 1 2026 layer thickness and build orientation; Perez et al. (2021) similarly found that brittle fracture occurs at these interfaces along a single plane, representing the primary mechanism for reduced z-direction strength. That interlayer bonding governs tensile outcome is further confirmed by Gajjar et al. (2025), who found that tensile strength of FDM-printed PLA improved by up to 10% with increasing nozzle temperature, attributing this directly to improved interlayer fusion. According to Griffith fracture theory, the presence of surface defects and voids acts to concentrate stress at crack tips, lowering the effective fracture strength of the material below its theoretical maximum (see Figure 3 and 4) (McGinty, 2014; van Otterloo et al., 2015). The smaller and fewer the surface defects, the higher the stress required to propagate a crack (Ashby, 2011; Hiremath et al., 2024). Wickramasinghe et al. (2020) confirmed this directly in FDM specimens, noting that voids between filament roads act as stress concentrators and rapid crack initiation sites under tensile loading (see Figure 5). Because epoxy coating acts on the outer surface only, any improvement in tensile performance must arise from surface-level mechanisms rather than bulk reinforcement.
Figure 3: Stress field lines concentrating around a surface crack under tensile loading. (Adapted from McGinty 2014)
Figure 4: Progressive crack propagation through three stages (Source: van Otterloo et al., 2015)
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Figure 5: Crack propagation through uncoated PLA and inhibition of propagation in coated PLA (Adapted from Hiremath et al., 2024)
Post-processing strategies for FDM PLA reinforcement Several post-processing strategies address FDM's mechanical limitations. Thermal annealing promotes polymer chain diffusion across interlayer interfaces and increases crystallinity, improving tensile and flexural properties by up to 15–20%, though it risks dimensional distortion in thin geometries (Kahya et al., 2025; Wickramasinghe et al., 2020). Acetonebased chemical smoothing is effective for ABS, but PLA resists most common solvents at room temperature, limiting this approach (Baran & Erbil, 2019; Valerga et al., 2019). Surface coating with a stiff polymer offers a different mechanism: rather than modifying the internal structure, it adds an external load-bearing shell and may seal surface defects without thermal exposure (see Figure 5). Epoxy resin coatings cure at room temperature without thermally deforming the substrate, require no specialist equipment, and have well-characterised mechanical properties, making epoxy a practical candidate for surface reinforcement of FDM parts. Epoxy as a reinforcing coating and the PLA–epoxy interface Epoxy resins cure through a cross-linking reaction between a resin and hardener, forming a rigid thermoset network with tensile strength typically 55– 80 MPa and elastic moduli of 2–4 GPa (Farooq et al., 2020; May, 1988). West System 105/205 epoxy has a manufacturer-reported tensile strength of 54.09 MPa and tensile modulus of 2.81 GPa (Wessex Systems & Adhesives, 2023), placing it within the lower bound of structural epoxy systems. Once applied, the system forms a core-shell composite in which the printed PLA is the load-bearing core and the cured epoxy constitutes a thin external shell (see Figure 6). For the shell to contribute to tensile performance, stress must transfer across the PLA–epoxy interface; weak adhesion would increase mass without a linear strength gain (Ashby, 2011). Critically, composite theory suggests the shell's contribution depends on its volume fraction relative to the core: a thin single coat may produce negligible reinforcement while progressively thicker coatings could yield increasing
Scientific Research in School Volume 8 Issue 1 2026 returns, though with diminishing effect as shell volume fraction grows (Ashby, 2011; Chawla, 2012).
Figure 6: Composite shell illustration
Interface quality is influenced by PLA's surface condition. FDM-printed PLA is hydrophobic with low surface energy, exhibiting a static water contact angle typically in the range of 75–85°, which can reduce epoxy wettability and limit bonding completeness (see Figure 7) (Baran & Erbil, 2019; Kariž et al., 2021; Yang et al., 2020). Žigon et al. (2020) found that coating adhesion to 3D-printed PLA depended on substrate porosity and chemical compatibility, with surface characteristics directly affecting film uniformity.
Figure 7: Water droplet on a hydrophobic PLA surface (Source: Yang et al., 2020)
Mechanical roughening prior to coating increases surface area and promotes mechanical interlocking (see Figure 8) (Dachev et al., 2025; Parodo et al., 2025). Hiremath et al. (2024) found that a single epoxy coat reduced surface roughness of FDM PLA by up to 95% but produced a coated ultimate tensile strength of approximately 15 MPa, below that of the best uncoated specimens, likely because poor surface preparation reduced interface adhesion. This suggests that the relationship between coating quantity and ultimate tensile strength improvement may not be straightforward, and that the greatest mechanical benefit may occur in the initial coating applications rather than accumulating linearly with additional layers.
Figure 8: Microscopy analysis of 3D-printed PLA plates’ surface after different surface treatments: (a) untreated (UT); (b) isopropyl alcohol-cleaned (AL-C); (c) mechanical abrasion (MA); (d) acetone-cleaned (AC-C). (Source: Parodo et al., 2025)
Cross-sectional area, ultimate tensile strength and a gap in the literature Ultimate tensile strength (UTS) is the maximum engineering stress a material sustains before failure, calculated as the peak force recorded during tensile loading divided by the original cross-sectional area of the specimen (Ashby, 2011). When comparing tensile performance across coated and uncoated FDM specimens, the cross-sectional area used in stress calculations is significant. Studies calculating UTS for FDM polymers, including coated specimens, routinely use the nominal printed cross-section (Hiremath et al., 2024; Popescu et al., 2018; Rankouhi et al., 2016). This is acceptable when coating is negligible, but as layers build, neglecting the epoxy shell increasingly overestimates the calculated stress. No prior study has investigated the effect of increasing epoxy coating layers on FDM-printed PLA, nor accounted for progressive cross-sectional growth when calculating stress across coating levels, representing a gap with direct implications for how stress is interpreted in multi-layer coated specimens.
Scientific Research Question To what extent does the number of epoxy resin coating layers affect peak force and ultimate tensile strength in FDM PLA tensile specimens?
Scientific Hypothesis Hypothesis 1 As resin coating increases in FDM PLA tensile specimens, peak force will increase linearly. Because each coat also adds cross-sectional area, ultimate tensile strength is investigated separately to relate the findings to material performance:
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Scientific Research in School Volume 8 Issue 1 2026 Hypothesis 2: Ultimate tensile strength will initially increase as coating levels increase and then plateau
Methodology Tensile specimens were designed in SolidWorks to the dimensions specified in ASTM D638 Type IV (Figure 9) and 3D printed using a Bambu Lab X1 Carbon fused deposition modelling (FDM) printer. All specimens were printed flat in polylactic acid (PLA) using identical printing conditions outlined in Table 1 (Chacón et al., 2017; Popescu et al., 2018). Fifty specimens were divided into five batches of 10: B0 as the uncoated control and B1–B4 receiving one to four epoxy coats respectively.
Figure 9: Engineering drawing of ASTM D638 Type IV tensile specimen designed in SolidWorks, showing gauge section dimensions of 6.0 mm width and 3.2 mm thickness Table 1: FDM printing parameters applied uniformly across all 50 specimens printed on the Bambu Lab X1 Carbon Variable Polymer Nozzle Diameter (mm) Layer Height (mm) Nozzle Temperature (°C) Bed Temperature (°C) Infill (%) Infill Pattern Raster Angle (º) Max. Volumetric Speed (mm3/s)
Value PLA 0.4 0.2 220 55 100 Aligned Rectilinear ±45 21
Prior to coating, the surface of each specimen in batches B1–B4 was mechanically roughened using 600-grit silicon carbide sandpaper to increase surface area and promote mechanical interlocking with the epoxy coating (Dachev et al., 2025; Parodo et al., 2025). Five consistent strokes were applied along the entire surface of each specimen. Specimens were then cleaned using isopropyl alcohol and allowed to air dry completely to improve bond strength (see Parodo et al., 2025). The initial mass of each specimen was recorded using a digital balance with a precision of ±0.005 g. Epoxy coating mixtures were prepared using West System 105 Epoxy Resin combined with West System 205 Fast Hardener in a 5:1 ratio following the procedure specified by the manufacturer. For batch B1, a thin and uniform epoxy coating was applied to each specimen using a foam brush.
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Immediately after coating, specimens were suspended vertically from a curing rack using binder clips. Excess resin accumulating at the lower edge of specimens during curing was periodically removed using paper towels to minimise droplet formation and maintain coating uniformity. The coating process for batches B2–B4 was repeated to produce multi-layer coatings. An observational curing interval was used between intermediate coats when the surface was tacky but left no residue on a glove to avoid amine blush (which inhibits intercoat adhesion) (May, 1988). This interval was 4 hours and 15 minutes for all batches in this setup. All coated specimens were allowed to cure for at least 24 hours following the final coating application. Final specimen masses were then recorded. The mass increase of each coated specimen was divided by the manufacturer-reported cured density (1.1417 g/cm³; Wessex Systems & Adhesives, 2023) to calculate the volume of epoxy deposited. Mean coating thickness (t) was then estimated gravimetrically consistent with ASTM B767 (ASTM International, 2021), distributing epoxy volume uniformly across the total nominal specimen surface area derived from SolidWorks (3,887 mm²): 𝑡𝑡𝑡𝑡 =
∆𝑚𝑚𝑚𝑚
𝜌𝜌𝜌𝜌𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸
×
1000 𝐴𝐴𝐴𝐴𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆
The modified cross-sectional area of the gauge section for each specimen was calculated individually, incorporating corner rounding observed on coated specimens where the epoxy bridges sharp PLA corners with radius approximately equal to t: 𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐶𝐶𝐶𝐶𝑆𝑆𝑆𝑆𝐶𝐶𝐶𝐶 = (6.0 + 2𝑡𝑡𝑡𝑡)(3.2 + 2𝑡𝑡𝑡𝑡) − (4 − 𝜋𝜋𝜋𝜋)𝑡𝑡𝑡𝑡²
where 6.0 mm and 3.2 mm are the nominal gauge width and thickness respectively, and the term (4 − π)t² corrects for the four corners of the rectangular cross-section, which were observed to round off with radius approximately equal to t as the epoxy bridged the sharp PLA edges rather than building up around them (Figure 10).
Scientific Research in School Volume 8 Issue 1 2026 dividing peak force by the cross-sectional area of the gauge section.
Figure 10: Cross-sectional view of a B4 Specimen illustrating corner rounding of epoxy
Specimen density was calculated as a weighted blend of PLA volume from SolidWorks and individual epoxy volume, using densities of 1.24 g/cm³ for PLA and 1.1417 g/cm³ for cured epoxy (Wessex Systems & Adhesives, 2023). Tensile testing was conducted using the Bridge Buster 3000, a custom-made compression and tensile strength testing machine lent for use, fitted with a custom clamping jig (see Figure 11) produced for this specific investigation. The jig was machined from 6061 aluminium alloy using a CNC, consistent with the testing platform used in prior Barker College tensile investigations (Moon, 2025; Wilson, 2022). The jig was designed to reduce variability from manual clamping (which prior investigators found time-consuming and inconsistent) and to ensure specimens could reach failure (J. Wilson, personal communication, May 2026; J. Moon, personal communication, May 2026). Each specimen was individually secured within the apparatus and loaded at the minimum separation speed allowed (5mm/s; Kong, 2024) until fracture occurred, consistent with quasi-static tensile testing practice for polymer specimens (Chacón et al., 2017). The peak force was recorded in kilograms and converted to Newtons (N) by multiplying by 9.81. UTS was calculated by
Figure 11: Bridge Buster 3000 fitted with a custom aluminium mounting jig
Mean peak force and UTS values for each batch were calculated and plotted against epoxy mass gain and coating level. Statistical significance between experimental groups was evaluated using a one-way ANOVA with a post hoc Tukey HSD test at a significance level of α = 0.05, conducted separately for peak force and UTS across all five batches.
Results Mass gain increased progressively across batches B1 to B4, with mean values of 0.395 g, 0.825 g, 1.324 g and 1.689 g respectively. This corresponded to estimated mean coating thicknesses of 0.089 mm, 0.186 mm, 0.298 mm and 0.381 mm, and mean gauge cross-sectional areas of 20.86 mm², 22.73 mm², 24.97 mm² and 26.66 mm², representing a 38.9% increase in cross-sectional area relative to the uncoated control (19.20 mm²). These values are summarised in Table 2.
Table 2: Mean batch data including coating build-up parameters, peak force and UTS across all five experimental batches. SD = standard deviation of peak force within each batch. Batch B0 (no coat) B1 (one coat) B2 (two coats) B3 (three coats) B4 (four coats)
Mean initial Mean final Mean mass mass (g) mass (g) gain (g)
Mean coating thickness (mm)
Mean crossMean peak sectional force (N) area (mm2)
SD (N)
Mean UTS (MPa)
SD (MPa)
5.566
5.566
0
0
19.20
938.4
31.4
48.88
1.64
5.558
5.953
0.395
0.089
20.86
1112.6
17.5
53.34
1.07
5.580
6.405
0.825
0.186
22.73
1221.4
22.9
53.73
0.73
5.583
6.907
1.324
0.298
24.97
1341.3
25.2
53.73
1.40
5.561
7.250
1.689
0.381
26.66
1423.5
37.9
53.40
1.49
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Scientific Research in School Volume 8 Issue 1 2026 Effect of coatings on peak force Mean peak force increased consistently from B0 to B4, rising from 938.4 N in the uncoated control to 1112.6 N (B1), 1221.4 N (B2), 1341.3 N (B3) and 1423.5 N (B4), representing a cumulative increase of 51.7% across the coating range (Figure 12). Within-batch standard deviations ranged from 17.5 N in B1 to 37.9 N in B4, indicating low within-batch variability relative to the between-batch differences observed. A linear trend line fitted to mean peak force values across coating levels yielded R² = 0.9834 (Figure 12), consistent with the strong linear relationship observed between individual specimen peak force and epoxy mass gain across all 50 specimens (R² = 0.949, Figure 13). A one-way ANOVA found a statistically significant difference in peak force between batches (p < 0.001). A post hoc Tukey HSD test found that all ten pairwise batch comparisons were significant, each returning the minimum p-value reportable by the analysis software, and were thus reported as p < 0.01, indicating that each additional coating layer produced a statistically distinguishable increase in peak force (Table 3).
Figure 12: Line graph of mean peak force (N) per coating level with standard deviation error bars across batches B0–B4, with linear trend line (R² = 0.9834)
Figure 13: Scatter plot of individual specimen peak force (N) against mass gain (g) for all 50 specimens, coloured by batch
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Scientific Research in School Volume 8 Issue 1 2026 Table 3: Tukey HSD post hoc test results for peak force across all pairwise batch comparisons. All comparisons significant at p < 0.01. Batch Pair B0 vs B1 B0 vs B2 B0 vs B3 B0 vs B4 B1 vs B2 B1 vs B3 B1 vs B4 B2 vs B3 B2 vs B4 B3 vs B4
Q Statistic 18.73 30.45 43.34 52.19 11.71 24.61 33.45 12.90 21.74 8.84
p-value < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01 < 0.01
Inference Significant Significant Significant Significant Significant Significant Significant Significant Significant Significant
Effect of coatings on ultimate tensile strength Mean UTS increased from 48.88 MPa in B0 to 53.34 MPa in B1, representing an increase of 9.1%. Beyond B1, UTS remained approximately constant across B2 (53.73 MPa), B3 (53.73 MPa) and B4 (53.40 MPa), with differences between coated batches of less than 0.4 MPa (Figure 14). Against individual specimen mass gain, coated UTS values clustered narrowly regardless of mass gain, with no upward trend observable beyond the initial increase from B0 (Figure 15). A one-way ANOVA found a statistically significant difference in UTS between batches (p < 0.001). A post hoc Tukey HSD test found that only comparisons involving B0 were significant (p < 0.01), while no significant difference was detected between any two coated batches, each returning the maximum p-value reported by the software (p ≈ 0.90), indicating that UTS increased significantly upon application of the first coat and plateaued thereafter (Table 4).
Figure 15:Line graph of mean ultimate tensile strength (MPa) per coating level with standard deviation error bars across batches B0–B4, with linear trend line fitted to coated batches only (B1–B4), showing a sharp increase from B0 to B1 followed by a plateau across B1–B4
Figure 14: Scatter plot of individual specimen ultimate tensile strength (MPa) against epoxy mass gain (g) for all 50 specimens, coloured by batch, with linear trend line fitted to coated specimens only (B1–B4), illustrating the absence of an upward trend beyond the initial increase from B0.
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Scientific Research in School Volume 8 Issue 1 2026 Table 2: Tukey HSD post hoc test results for UTS across all pairwise batch comparisons. Green = significant (p < 0.01); red = insignificant (p ≈ 0.90). Batch Pair B0 vs B1 B0 vs B2 B0 vs B3 B0 vs B4 B1 vs B2 B1 vs B3 B1 vs B4 B2 vs B3 B2 vs B4 B3 vs B4
Q Statistic 10.23 11.14 11.13 10.38 0.91 0.89 0.14 0.01 0.76 0.75
p-value < 0.01 < 0.01 < 0.01 < 0.01 0.90 0.90 0.90 0.90 0.90 0.90
Inference Significant Significant Significant Significant Insignificant Insignificant Insignificant Insignificant Insignificant Insignificant
Discussion The results of this investigation provide full support for both hypotheses. Hypothesis 1 predicted that peak force would increase linearly with coating, and this was fully supported in the sense that peak force increased consistently and significantly across all five batches, with a strong linear relationship observed (R² = 0.9834). Hypothesis 2 predicted that UTS would initially increase then plateau, and this was fully supported. UTS increased significantly from B0 (48.88 MPa) to B1 (53.34 MPa), representing a 9.1% increase, but remained statistically indistinguishable across B1 through B4, with all coated batch pairings returning p ≈ 0.90 on Tukey HSD testing. The consistent increase in peak force across all batches is explained by the progressive addition of epoxy shell volume. As each coating layer is applied, the epoxy shell contributes additional load-bearing material to the gauge section. Since the cured epoxy has a reported tensile strength of 54.09 MPa (Wessex Systems & Adhesives, 2023), each additional layer is mechanically capable of carrying tensile load. This is consistent with core-shell composite theory, in which the shell's total contribution to load capacity scales with its volume fraction relative to the core (Ashby, 2011; Chawla, 2012). As coating thickness increased from 0.089 mm in B1 to 0.381 mm in B4, the growing epoxy shell volume produced the observed linear increase in peak force. The plateau in UTS across B1 to B4 is the most significant finding of this investigation and is fully consistent with Hypothesis 2. While peak force increased with each additional coat, the crosssectional area of the gauge section also increased as the epoxy shell added material to all four faces. The two effects approximately cancel, producing a plateau in stress. This indicates that the epoxy shell and PLA core contribute to similar load per unit area, so intrinsic composite strength does not change significantly with increasing coating thickness once the first coat establishes the shell. This is consistent with composite theory, which predicts that stress
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depends on the relative mechanical properties of core and shell rather than their absolute volumes (Ashby, 2011; Chawla, 2012). The significant jump in UTS from B0 to B1 warrants specific explanation. According to Griffith fracture theory, surface defects and voids act as stress concentrators that lower the effective fracture strength of a material below its theoretical maximum by reducing the critical stress required for crack propagation (Ashby, 2011). Perez et al. (2021) and Rankouhi et al. (2016) demonstrated that tensile failure in FDM specimens preferentially initiates at interlayer boundaries and surface stress concentrations in the outermost filament roads, and Wickramasinghe et al. (2020) confirmed that voids between filament roads act as rapid crack initiation sites under tensile loading. The first epoxy coat fills these surface voids and seals the outermost layer of filament roads, reducing effective crack length at the surface and increasing the critical stress required for crack propagation. This is further supported by Hiremath et al. (2024), who found that a single epoxy coat reduced surface roughness of FDM PLA by up to 95%, suggesting that the majority of defect sealing occurs in the first coating application, with subsequent layers contributing primarily to shell thickness rather than further surface remediation. Several limitations should be acknowledged. The gravimetric method used to estimate coating thickness assumes uniform epoxy distribution across the entire specimen surface, whereas in practice epoxy may pool at lower edges during vertical curing. This introduces uncertainty in the calculated cross-sectional area for individual specimens, which propagates into UTS values, and could be reduced in future through direct measurement using cross-sectional microscopy or profilometry. Additionally, the custom aluminium jig used with the Bridge Buster 3000, while improving on earlier iterations of the apparatus (Moon, 2025; Wilson, 2022), cannot fully eliminate eccentric loading, reducing confidence in the absolute stress values reported. Future research should prioritise replication using a calibrated universal testing machine such as the Instron 5960, which applies pure uniaxial tension through self-aligning grips, to confirm whether the UTS plateau is a genuine material phenomenon or partially an artefact of the non-ideal loading geometry. Testing beyond four coating layers would determine whether the plateau continues or UTS eventually declines as the epoxy shell becomes thick enough to introduce its own failure modes, while lower infill specimens would clarify whether the surface sealing
Scientific Research in School Volume 8 Issue 1 2026 mechanism driving the B0-to-B1 improvement is more pronounced where internal voids are prevalent. Print orientation also warrants investigation, as FDMprinted PLA exhibits significant anisotropy (Chacón et al., 2017) and it remains unknown whether epoxy coating differentially reinforces orientations where interlayer weakness is greatest. Finally, a pre-sizing procedure that prints specimens progressively undersized to achieve a controlled gauge dimension after coating would standardise cross-sectional area across batches, eliminating the divergence between peak force and UTS and enabling a cleaner comparison of intrinsic material strength across coating amounts.
Conclusion This investigation examined the effect of increasing numbers of epoxy resin coating layers on the peak force and ultimate tensile strength (UTS) of FDMprinted PLA specimens. Peak force increased consistently and significantly across all five batches, rising from 938.4 N in the uncoated control to 1423.5 N in B4, representing a cumulative increase of 51.7% and a strong linear relationship with coating level (R² = 0.9834). UTS increased significantly from 48.88 MPa in B0 to 53.34 MPa in B1, representing a 9.1% improvement, but remained statistically indistinguishable across all coated batches (p ≈ 0.90), providing full support for Hypothesis 2. These findings demonstrate that multi-layer epoxy coating is an effective and practical post-processing strategy for increasing the peak load capacity of FDM-printed PLA parts, requiring no specialist equipment and curing at room temperature without dimensional distortion.
Declaration of use of AI Artificial intelligence (AI) was used in several capacities throughout this investigation. During the literature review phase, OpenAI’s ChatGPT and Anthropic’s Claude AI were used to identify and locate peer-reviewed sources relevant to the research question, including suggesting search terms and recommending specific papers covering FDM mechanical limitations, epoxy coating properties, composite theory and surface adhesion. All references were verified by myself and imported into Zotero independently. Claude also assisted in structuring and drafting the literature review text, with myself providing content direction, approving all claims, and making final editorial decisions. During the methodology phase, ChatGPT assisted in drafting the methodology from a numbered procedural
list developed by myself, ensuring appropriate academic language and structure. It recommended the West System 105/205 epoxy system as a suitable coating material based on its well-characterised mechanical properties, room-temperature curing behaviour, and local availability, with the final selection confirmed by myself. ChatGPT further assisted in deriving and verifying the mathematical model used to estimate mean coating thickness and modified cross-sectional area from gravimetric data, including the corner-rounding correction term (4 − π)t² and the simplified Excel formula for ACoated. The logical basis for each formula was discussed and confirmed by myself before implementation in the data spreadsheet. During the writing phase, Claude assisted in drafting the results, discussion and conclusion sections based on data provided by myself. All scientific interpretations, hypothesis evaluations and mechanistic explanations were developed by myself, with Claude used to proofread, restructure and adjust the language to an appropriate academic register. AI was not used to generate raw data, conduct experiments, or make independent scientific judgements. All final decisions regarding content, interpretation and structure were made by myself.
Acknowledgements I would like to thank Dr Matthew Hill for research supervision, report guidance, and invaluable mentorship throughout this investigation. I am grateful to Jack Wilson and Jack Moon for personal communication regarding their prior research reports and for generously sharing methodological advice. Barker Robotics provided essential access to 3D printing, aluminium material stock, and CNC machining facilities without which specimen fabrication would not have been possible. I also acknowledge the Science Extension coordinators who facilitated access to laboratory facilities and equipment.
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Appendices Appendix A: Individual Specimen Raw Data (All 50 Specimens) All 50 specimens across batches B0–B4 with individual mass, coating thickness, cross-sectional area, peak force, UTS and density values. Table A1: Individual specimen data for all 50 specimens across batches B0–B4. Batch means shown at bottom. Final Mass Mass Gain (g) (g)
Specimen ID
Initial Mass (g)
B0-01 B0-02 B0-03 B0-04 B0-05 B0-06 B0-07 B0-08 B0-09 B0-10
5.64 5.48 5.42 5.64 5.47 5.48 5.65 5.60 5.62 5.66
5.64 5.48 5.42 5.64 5.47 5.48 5.65 5.60 5.62 5.66
B1-01 B1-02 B1-03 B1-04 B1-05 B1-06 B1-07 B1-08 B1-09 B1-10
5.61 5.66 5.66 5.49 5.49 5.46 5.61 5.64 5.49 5.47
6.01 5.85 6.09 6.05 5.91 5.92 5.89 5.98 6.02 5.81
B2-01 B2-02 B2-03 B2-04 B2-05 B2-06 B2-07 B2-08 B2-09 B2-10
5.66 5.61 5.61 5.65 5.45 5.62 5.61 5.64 5.47 5.48
6.48 6.38 6.49 6.43 6.26 6.42 6.42 6.47 6.28 6.42
B3-01 B3-02 B3-03 B3-04 B3-05 B3-06 B3-07
5.41 5.67 5.61 5.61 5.59 5.62 5.62
6.71 6.99 6.92 6.93 7.04 6.90 6.95
Coating Thickness (mm)
CrossSectional Area Peak Force (N) UTS (MPa) (mm²)
Batch B0: Uncoated Control 0 19.20 0 19.20 0 19.20 0 19.20 0 19.20 0 19.20 0 19.20 0 19.20 0 19.20 0 19.20 Batch B1: 1 Coat 0.400 0.0901 20.88 0.190 0.0428 19.99 0.430 0.0969 21.01 0.560 0.1262 21.57 0.420 0.0947 20.97 0.460 0.1037 21.14 0.280 0.0631 20.37 0.340 0.0766 20.63 0.530 0.1194 21.44 0.340 0.0766 20.63 Batch B2: 2 Coats 0.820 0.1848 22.71 0.770 0.1735 22.49 0.880 0.1983 22.97 0.780 0.1758 22.53 0.810 0.1825 22.66 0.800 0.1803 22.62 0.810 0.1825 22.66 0.830 0.1871 22.75 0.810 0.1825 22.66 0.940 0.2118 23.24 Batch B3: 3 Coats 1.300 0.2930 24.86 1.320 0.2975 24.95 1.310 0.2952 24.91 1.320 0.2975 24.95 1.450 0.3268 25.55 1.280 0.2885 24.77 1.330 0.2997 25.00 0 0 0 0 0 0 0 0 0 0
Density (g/cm³)
942.7 988.8 877.0 923.1 924.1 974.1 917.2 919.2 949.6 968.2
49.10 51.50 45.68 48.08 48.13 50.74 47.77 47.87 49.46 50.43
1.240 1.240 1.240 1.240 1.240 1.240 1.240 1.240 1.240 1.240
1117.4 1111.5 1137.0 1138.9 1109.5 1127.2 1103.6 1098.7 1103.6 1078.1
53.50 55.59 54.11 52.80 52.91 53.32 54.17 53.26 51.47 52.26
1.233 1.237 1.233 1.230 1.232 1.232 1.235 1.234 1.231 1.234
1226.3 1200.7 1265.5 1216.4 1205.6 1191.9 1195.8 1224.3 1240.0 1247.8
54.00 53.40 55.09 53.99 53.20 52.69 52.76 53.81 54.71 53.70
1.227 1.227 1.226 1.227 1.226 1.227 1.227 1.226 1.226 1.225
1321.4 1329.3 1335.1 1317.5 1327.3 1315.5 1339.1
53.15 53.27 53.61 52.80 51.95 53.11 53.57
1.220 1.220 1.220 1.220 1.218 1.220 1.220
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Scientific Research in School Volume 8 Issue 1 2026 B3-08 B3-09 B3-10
5.65 5.47 5.58
6.98 6.91 6.74
1.330 1.440 1.160
B4-01 B4-02 B4-03 B4-04 B4-05 B4-06 B4-07 B4-08 B4-09 B4-10
5.61 5.63 5.47 5.67 5.40 5.61 5.49 5.67 5.49 5.57
7.29 7.31 7.03 7.37 7.16 7.31 7.24 7.45 7.13 7.21
1.680 1.680 1.560 1.700 1.760 1.700 1.750 1.780 1.640 1.640
B0 Mean B1 Mean B2 Mean B3 Mean B4 Mean
5.566 5.558 5.580 5.583 5.561
5.566 5.953 6.405 6.907 7.250
0 0.395 0.825 1.324 1.689
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0.2997 25.00 0.3245 25.50 0.2614 24.22 Batch B4: 4 Coats 0.3786 26.62 0.3786 26.62 0.3516 26.06 0.3831 26.71 0.3966 26.99 0.3831 26.71 0.3944 26.95 0.4011 27.09 0.3696 26.43 0.3696 26.43 Batch Means 0 19.20 0.089 20.86 0.186 22.73 0.298 24.97 0.381 26.66
1355.7 1379.3 1393.0
54.24 54.09 57.50
1.220 1.218 1.222
1423.4 1395.0 1428.3 1467.6 1474.4 1387.1 1382.2 1434.2 1475.4 1367.5
53.48 52.41 54.82 54.94 54.62 51.93 51.30 52.95 55.82 51.74
1.216 1.216 1.217 1.216 1.214 1.216 1.215 1.215 1.216 1.216
938.4 1112.6 1221.4 1341.3 1423.5
48.88 53.34 53.73 53.73 53.40
1.240 1.233 1.226 1.220 1.216
Scientific Research in School Volume 8 Issue 1 2026
Validating FD-z as a biomarker for Preclinical Alzheimer’s Disease Ahan Sharma Barker College Alzheimer’s Disease (AD) is a common and lethal neurological disease that results in severe cognitive loss and eventual death due to this neurodegeneration. Preclinical AD lacks cost-effective, non-invasive diagnostic screening tools capable of identifying the precursors of AD before cognitive effects take place. Fractal Dimension (FD) analysis offers a geometric framework to quantify subtle changes in cortical complexity, yet its efficacy as an early structural biomarker remains unverified. This study evaluated whether baseline age- and sex-standardized FD scores (FD-z) could distinguish amyloid status (presence of abnormal amyloid-beta depositions which can occur decades prior to symptoms) and predict longitudinal cognitive decline (through analysis using functional cognitive scores and conversion from cognitively normal to AD) Utilizing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), baseline MRI scans (n=98) were processed using the fractalbrain toolkit to calculate FD scores across specific regions reflecting the preclinical progression of AD. A normative model was applied to these raw FD scores, accounting for the effect of age and sex, in order to create FD-z scores. The objective was then assessed utilizing a Linear Mixed Effects Model, Kaplan Meier Survival Analysis, a Cox Hazards test, Receiver Operating Characteristic Analysis, and violin plots. Statistical analyses revealed that baseline FD-z scores did not significantly predict the rate of longitudinal cognitive decline (beta = -0.041, p = 0.702) or time to clinical conversion (HR = 1.1, p = 0.80). Secondary analyses confirmed that FDz possessed no capacity to discriminate between CN A- and CN A+ cohorts (AUC = 0.49).
Literature Review Alzheimer’s Disease Alzheimer’s Disease (AD) is an enigmatic and lethal disease, indicated by the presence of amyloid-beta plaques and neurofibrillary tau tangles (Masters et al., 2015). AD causes wide-ranging cerebral atrophy (Figure 1) through cascading neuroinflammation and neuronal starvation, resulting in severe cognitive loss and eventual death secondary to this neurodegeneration (Sheppard & Coleman, 2020). As such, AD was classified as the leading cause of death in Australia in 2024 (Australian Bureau of Statistics, 2024) and is predicted to triple in prevalence worldwide by 2050 (Scheltens, 2021). Functional severity of AD can be measured clinically and accurately through a Clinical Dementia Rating - Sum of Boxes (CDR-SB) test, which assesses memory, orientation, judgement, community involvement, home and hobbies, and personal care. This score is widely used in testing the efficacy of medical interventions, namely in clinical trials (Morris, 1993). Though there is no current cure or fully established pathogenesis for AD (Sheppard & Coleman, 2020), biomarkers remain important in risk stratification namely, enlisting for potential preventative clinical trials and planning for carer and lifestyle strategies.
Figure 1: Comparing a normal brain to a brain that has experienced cerebral atrophy (Source: The University of Queensland, 2019)
Fractal Dimension - an Introduction Fractal Dimension (FD) in a purely geometrical sense quantifies the self-similarity and complexity of objects across spatial scales. For example, the mandelbrot set (Figure 2) has a high fractal dimension as it consistently repeats across arbitrarily small spatial scales (Fig 2.). FD has been applied to a large range of fields, from geography to neuroscience (Moaveninejad et al., 2025), to elucidate non-linear relationships across complex boundaries - such as that across the cortical plane or the coast of Britain (Figure 3). In physical disciplines, FD can be estimated
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Scientific Research in School Volume 8 Issue 1 2026 through Equation 1 essentially quantifying the increase in complexity as the side length of nonoverlapping boxes approaches 0 (Bouda et al., 2016): Equation 1: Equation 1: An estimation of Fractal Dimension
Where: D = Fractal Dimension Score S = Side length of boxes Ns = Number of boxes
stimulation – a treatment associated with mixed outcomes. Thus, it may act as a preclinical biomarker for degeneration rate in AD years before any symptoms occur. Other MRI-derived structural biomarkers, such as hippocampal thickness, are correlated with predicting AD progression though at a much later stage that may inhibit the necessary interventions (Frisoni et al., 2010). Validation of this technique may allow for cost-efficient triaging and screening of patients with risk-factors, which could allow for greater preventative clinical trials in the future. The Problem of FD in AD Early Alzheimer’s follows two distinct progressions tracking amyloid-beta deposition and neurofibrillary tau tangles - Thal and Braak staging respectively. The progressions are described below and illustrated in Figure 4.
Figure 2: A visualization of the infinitely repeating and self-similar nature of the mandelbrot set, showcasing the upper bound of fractal dimension for the 2d plane. (Source: Danielkwalsh, 2022)
Thal Staging: Thal Staging concerns the spatial spread of amyloid plaques. Amyloid plaques - abnormal clumps of misfolded amyloid-beta protein present in interneural spaces as a hallmark of AD - appears first in the neocortex and other default-mode regions, such as the precuneus and posterior cingulate, then spreads to deeper structures over successive phases (Thal, 2006). Braak Staging: Braak Staging instead concerns the spread of tau tangles. Tau tangles - which follow the deposition of amyloid plaques, instead abnormal twisted clumps of tau protein that accumulate inside neurons and cause necrosis - initially accumulate in the entorhinal cortex and then move to the limbic/medial-temporal regions, and reach the wider neocortex later (Braak et al., 2006). Tau is better associated with atrophy and cognitive systems compared to Alzheimer’s Disease (Malpetti et al., 2022).
Figure 3: using box-counting to measure the fractal dimension of Britain's coastline. Essentially, as it decreases the number of boxes to cover the coast, the amount of detail captured is greater. Thus, as it divides over the scale, the gradient showcases how the complexity increases over magnification. (Source: Mahieu, 2014)
FD in AD Recently, Fractal Dimension scores have been used to detect microstructural changes before traditional observable changes in other neurological disorders as well as predicting progression rates as demonstrated by its successful use by Schoen et. al. (2025) in stratifying cognitively normal and Parkinson’s patients as well as predicting response to deep brain
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Figure 4: Diagram illustrating the differences in Braak and Thal staging (Source: Swarbrick et al., 2019)
Typically, both processes are at work in those with AD, however amyloid plaques can occur years before tau accumulation (Young et al., 2025), however, it is
Scientific Research in School Volume 8 Issue 1 2026 not associated with structural change and instead changes in the neural connections of the default mode network. As FD is a structural biomarker, it will likely only be able to reliably detect atrophy caused by the neurofibrillary tau tangles, thus unable to take advantage of this earlier preclinical window and detection. -- essentially it is just stating that FD may not be useful in that early preclinical window.
study, cognitively normal amyloid positive individuals will be denoted as CN A+, and amyloid negative individuals CN A-. Amyloid positive individuals do not have AD, instead they have preclinical cursory amyloid plaques that substantially increase future risk for AD.
Study Aim This study explores the application of FD scores in Braak Staging regions to predict preclinical Alzheimer’s disease and predict yearly functional degradation pre-symptomatically.
(1) Do the baseline FD-z scores in cognitively normal amyloid-positive individuals predict functional cognitive decline and (2) can baseline FD-z detect and sort an individual as CN A- or CN A+?
To standardize FD scores against age and gender, FDz scores were used which is a measure of how different the FD of an individual is from the expected FD score of the typical brain of someone that age and gender. Only the first MRI of each patient was analysed to extract their baseline FD score (and calculate their baseline FD-z score) to test the possibility of this biomarker in a mass screening capability. This study will primarily use cognitively normal (not afflicted with Alzheimer’s Disease) amyloid positive individuals – which exceed a predetermined threshold point in containing amyloid plaques – as they are deemed significantly more at risk than amyloid negative and thus contribute AD progression. Inclusion of amyloid negative individuals may dilute the dataset with non-progressing individuals. In this
Scientific Research Question
Scientific Hypothesis H1: In amyloid-positive cognitively normal individuals, lower baseline FD-z scores are associated with significantly greater cognitive decline (indicated by greater increasing CDR-SB scores – the standard clinical test for functional cognition) and significantly higher conversion probabilities to AD. H2: In cognitively normal individuals, FD-z scores can be used to significantly differentiate between amyloid negativity and positivity (indicated by the Receiver Operating Characteristic curve and violin plots explained below), i.e. A significantly high AUC and significantly different distributions between FD-z stratified CN A- and CN A+ cohorts
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Methodology and Results Table 1: Table summarizing all the commonly used terms throughout the study Variable
Description
Frequency
Baseline Amyloid Status (CN A+/-)
Binary classification of a patient based on amyloid plaque density
Once per patient
Raw Baseline FD
A geometric metric quantifying the structural complexity of the apriori region (entorhinal, posterior cingulate and precuneus) via a 3D box-counting algorithm.
Once per patient
Baseline FD-z
An age- and sex-standardized score measuring an individual’s structural complexity deviation compared to the expected FD of someone that age and gender.
Source
Use CN A- individuals are used to determine the expected FD scores for Calculated from provided not-at-risk-of-AD patients based on Standardised Uptake age and gender. Value Ratio (SUVR) CN A+ individuals have their FD-z scores in ADNI following scores (calculated by comparing with literature precedent CN A- distributions), which can then be compared to their cognitive decline and survival probabilities. This research used ADNI post-processed images of the brain and used a Used to calculate Baseline FD-z computer program known as the “fractalbrain toolkit” to calculate the FD score. Mathematically calculated by comparing to the DF scores of the CN A- cohort using LOOCV (as the CN This is the primary independent A- cohort is considered to variable. be representative of expected FD scores of otherwise healthy individuals
A regognised clinical cognitive CDR-SB at staging score assessing As often as recorded in This forms the first dependent various performance across 6 domains the ADNI clinical Extracted directly from the variable, allowing comparison times after memory, orientation, judgement, database (Minimum of 3 longitudinal ADNI clinical between FD-z scores and rate of Baseline community, home/hobbies, assessments over >24 database cognitive decline. FD-z was personal care - ranging from 0 to months) determined 18. Time until conversion The state of converting from Extracted directly from This forms the second dependent Once per patient (at after cognitively normal to Alzheimer’s ADNI’s longitudinal variable, providing an insight on how conversion point) baseline Disease clinical database FD-z affects the time to conversion. MRI
Acquisition and Pre-processing of Data Dataset The research utilized relevant medical data - notably post-processed FreeSurfer surfaces, medical history, and longitudinally reported CDR-SB data - acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). ADNI is a highly reliable multimillion-dollar project funded by the American National Institute of Aging. ADNI data has been used by over 5,500 research publications, is easily accessible, and has rigorous quality control and preprocessing to standardize all MRI data (ADNI, 2026). Crucially, the surface reconstructions were directly downloaded from post-processed ADNI data - specifically UCSF-processed cross-sectional FreeSurfer recon-all surfaces - which employ highly valid and standardized computational methods to process them (Hartig et al., 2014). To obtain access to the ADNI database, an official application must be approved by ADNI’s Data Sharing and Publications Committee with project details, qualifications, and an agreement to applicable ethical guidelines to ensure
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ethical use of the dataset. If access is obtained, the data used in this report is readily available and reproducible. Selection Criteria All participants were selected from their adherence to the following criteria: 1. Cognitively Normal at first available scan 2. Amyloid Status available 3 .>=3 CDR-SB results over >24 months 4. Baseline MRI and USCF Freesurfer processing available
Scientific Research in School Volume 8 Issue 1 2026 Defining Amyloid Status
Figure 5: Flowchart on patient selection for participation in the study
described in Braak staging. These included the entorhinal, posterior cingulate and precuneus regions. (Braak et al., 2006). Note that amyloid plaques form earlier but are weakly associated with the structural change that FD aims to capture. While an extension to this present research, the same analysis was also performed on three regions effected during Thal staging to determine if, in fact, there are tiny microstructural changes associated with amyloid plaques that a fractal analysis can uncover over traditional volumetric analysis. the precuneus, medial orbitofrontal, and posterior cingulate cortices which are associated with the earliest accumulation of amyloid-beta fibrules (Palmqvist et al., 2017). Interestingly, the results were comparable to the regions associated with Braak staging. Relevant analysis is included in the Appendix.
Fundamentally, Amyloid PET scans measure the amount of radio tracer that has stuck on amyloid plaques through a Standardized Uptake Value Ratio (SUVR), i.e. a SUVR of 1.0 is reference. Using the precedent of Royse et al. (2021), the cutoffs for CN A+ are as follows: florbetapir SUVR > 1.11 and a florbetaben SUVR > 1.08. The SUVR value utilized in this study was ADNI’s post-processing value, normalized to the entire cerebellum. Fractal Dimension Extraction (FD) Raw Fractal Dimension scores were calculated using the fractalbrain toolkit (Marzi et al., 2020), which directly processes FreeSurfer outputs through a 3D box-counting algorithm. Only one fractal dimension score was extracted per patient, calculated from their first available scan.
Quality Control The ADNI database states whether a scan passes or fails internal quality checks (the specifics are irrelevant to this report) and so any scans flagged by ADNI/USCF FreeSurfer’s internal quality checks were excluded. FD values were screened for extreme outliers (beyond 2.5 SD) and flagged cases were manually inspected. While one scan was excluded due to a failed ADNI internal quality check, zero were manually excluded as an outlier FD score. Harmonization ComBat – a statistical data harmonization method used to reduce unwanted variations (i.e. between different models of scanners) while ensuring that effects due to biology (age, sex, amyloid status) are kept unchanged. This is a level of pre-processing before conversion to FD scores to ensure that nonbiological differences do not impact the validity of the analysis (Orlhac et al., 2021). Summary of Patient Trajectories:
The exact box sizes ranged from 1-16mm, which spans the spatial scales over which cortical complexity can be measured - bounded by the resolution of the image (1 mm voxels) and the scale at which gyral folding is no longer resolvable. The fractal dimension is the slope of the log-log relationship between box count and box size across this range, i.e. Equation 1. In order to provide a FD score the whole brain was not used, rather FD scores were calculated and averaged across the three regions relevant to the progression
Figure 6: (CDR-SB trajectories of all participants: each line is the CDR-SB trajectory of one patient
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Scientific Research in School Volume 8 Issue 1 2026 Determining FD-z scores: Normative (FD-z score) Curve The raw FD scores of the three aforementioned regions is vague in a clinical context; instead, a model fits and compares an individual's score against their expected score for their age and sex - which was created using the CN A- individuals in the study - in terms of standard deviations. No further covariates were added to mitigate the risk of regressing abnormalities of which the score is meant to detect. The model was fitted with only CN A- individuals using a Leave One Out Cross Validation (LOOCV) system (Stone, 1974), wherein n-1 individuals are used to train the model, then the one left out is tested on the model and this is repeated n times to ensure every observation contributes to both the testing and validation, which was especially useful in this study due to the low sample size (CN A- = 60)
This was calculated for each patient using the baseline MRI-derived FD score and applied through the following analyses. Assessing H1: The relationship between FD-z and Cognitive Decline A Linear Mixed Effects (LME) model estimates the longitudinal trajectory of each individual, allowing each individual their own intercept and rate of change while estimating the average pattern across the group (Bates et al., 2015). The model combines effects shared across the cohort (i.e. the influence of age) with effects unique to each individual (i.e. their slope) to create a unified model. This was used to isolate and measure the effect of FDz on the CDR-SB trajectory and thus whether it can be used to predict further cognitive decline.
Normative Graph, the age- and sex-adjusted curve for typical CN A- individuals) This curve shows a significant (p=0.044) weak correlation between baseline cortical FD and age and a significant difference (p=0.006) between sex – the two covariates the normative curve adjusts for.
Figure 8: A linear mixed effects model comparing the average CDR-SB trajectories as a function of years from baseline, the fixed effect coefficient of FD-w x time can be extracted as to analyze H1 Figure 7: Normative Graph, the age- and sex-adjusted curve for typical CN A- individuals) This curve shows a significant (p=0.044) weak correlation between baseline cortical FD and age and a significant difference (p=0.006) between sex – the two covariates the normative curve adjusts for.
With this curve, FD-z scores can be calculated using the following equation: Equation 2
Where: Z = FD-z score X = raw FD score of an individual μ = population mean σ = standard deviations from the population mean Where the population mean is age and sex adjusted.
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The above graph depicts the average CDR-SB trajectory across the cohort, with the fixed effect of the FD-z x Time with a weak negative coefficient (beta=0.041), i.e. lower FD-z scores were generally correlated with a greater rate of decline. However, likely due to the low sample size (n=38), the results were non-significant (p=0.702). As the percentile lines showcase, the lower FD-z scores are associated with a faster decline. Assessing H1: The relationship between FD-z and time to conversion from CN to AD Both a Kaplan-Meier Survival analysis and a Cox Hazards Ratio was performed to test the predictive abilities of FD-z on conversion from cognitively normal to Alzheimer’s disease, determined by a clinical diagnosis. The Kaplan Meier curve measures the number of conversions (from cognitively normal to AD) over a
Scientific Research in School Volume 8 Issue 1 2026 period of time, excluding any individuals which were unable to provide a measurement at a later time (Kaplan & Meier, 1958). Due to the low sample size, this censorship ability was very important in this analysis.
Figure 10: Power analysis comparing various Hazard Ratios against sample size and statistical power
Figure 9: Kaplan Meier Curves comparing the distribution of converters from CN to AD over time between high and low FD-w – stratified by the median
There was no significant difference between high FDz and low FD-z in terms of Kaplan Meier distributions (p=0.82). Note that high and low FD-z were stratified based on position from the median. A Hazards Ratio (HR) test was also run, which essentially calculates the risk of conversion at a particular time point and compares it between the two groups. For example, a HR of 2 means one group is twice as likely to convert at a particular time point compared to another group (Cox, 1972; Therneau & Grambsch, 2000) The HR between high and low FDz was 1.1 - i.e. a low FD-z would convert from CN to AD at 1.1x the likelihood compared to a higher FD-z – but at a non-significant p value of 0.80. When the same sample set was stratified based on amyloid status, the HR was 8.92, with a p value of 0.0001. Thus, amyloid status has a large effect on time to conversion despite the low sample size, whereas FD-z may have a possible small to no effect on conversion. Secondary Analyses Post-hoc Power Analysis A power analysis determines what size of effect a study of this size was capable of detecting (Cohen, 1988; Schoenfeld, 1983). With a limited number of participants, only relatively large effects can be reliably found whereas smaller, subtler effects can go undetected, as illustrated below. The threshold of 0.8 power is the arbitrary threshold for most other fields, however, it has been disputed as too high in the surgical sciences (Bababekov et al., 2019
The power of the current analysis, at n=38 CN A+ participants, is only at 6%. The large HR demonstrated with amyloid positivity is significant due to its large effect. Ultimately, due to the insignificant results of both the linear mixed effects model and Kaplan Meier curve, there is not enough evidence to reject the null hypothesis for H1. Validation of FD-z as a biomarker A Receiver Operating Characteristic (ROC) curve and violin plots were used to demonstrate significant differences between CN A- and CN A+ individuals with FD-z as the predictor. ROC curve The ROC curve measures the false positive rate against the true positive rate - i.e. how often does the model’s prediction, through FD-z, of amyloid status align with the reported value - across a continuous spectrum of thresholds for sorting, where the Area Under the Curve (AUC) indicates a higher probability that an amyloid-positive individual will receive a higher FD-z score than an amyloid-negative individual. Mathematically, the AUC can be described as the average of the true positive rate over all possible values of the false positive rate. As such a high AUC score indicates that the model maintains a high true positive rate on average, even when strictness thresholds are altered to minimize false positives (Robin et al., 2011; Collins et al., 2015).
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Figure 12: Violin Plots comparing distributions of FD-w across both the CN A- and CN A+ cohort
Figure 11: : A ROC curve comparing false positive rate and true positive rate to determine an AUC score
The AUC of 0.49 shows that FD-z has no real effect on stratifying CN A+ and A- individuals (i.e. it is almost a coin flip when the model uses FD-z to sort between CN A+ and A-). Violin Plot The Violin Plots visualize the distribution of FD-z scores within the CN A- and CN A+ groups as a combination of a simple box plot with the width at a certain point demonstrating the proportion of data points with that score. A t-test is performed to determine whether the difference between the average FD-z scores between the two groups are statistically significant and Cohen's d score measures the difference between the plots in terms of standard deviations, i.e. Equation 3
There was no significant difference in the distributions between the CN A- and CN A+ groups. Ultimately, due to the insignificant results of the violin plot and ROC curve, the null hypothesis for H2 cannot be rejected.
Discussion Across every analysis, the effect of FD-z was insignificant on predicting cognitive decline in CN A+ individuals nor in predicting conversion from CN to AD, i.e. both H1 and H2 are not supported by the evidence. The Cox HR when comparing CN A- and CN A+ groups yielded a ratio of 8.92 despite the low sample size, indicating a large difference that aligns with the literature. Thus, the null result was likely credible rather than a methodological failure. The power analysis yielding 6% demonstrates that the current analysis was too underpowered to yield any significant results, thus requiring a much larger sample size to validate completely. While the initial hypothesis was firmly grounded in existing evidence that links early neurodegenerative changes to altered complexity, this expectation may have overlooked the specific nuances of early-stage pathology. FD fundamentally targets structural changes in the brain architecture of the brain. In the first stages of preclinical AD, a structural change is not prominent - instead amyloid concentrates in the default mode network (associated with introspection, and detachment from external stimuli) and initiates hyperconnectivity (increased neural activity) rather than atrophy (Palmqvist et al., 2017). Therefore, a preclinical analysis with little to no atrophy may not produce significant changes in FD between the groups
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Scientific Research in School Volume 8 Issue 1 2026 - explaining the null result of the analysis with a priori amyloid-based regions. The tau tangles, which the primary analysis focused on, occurs years to decades following the accumulation of amyloid beta, and at this point of low atrophy, inherent complexity variance between individuals may overcome any systematic effect of these tangles on complexity. This can be seen through the low correlation of the normative graph, and the relatively high number of CN A+ (which are predicted to systematically sit below the curve) above the regression. Due to this, FD-z may not have a valuable place in preclinical analysis of AD but may have more applicable results in Parkinson’s Disease as Shoen et. al uncovered. Though the largest reason for the null result was likely the underpowered statistical analysis, it is important to note that the ADNI dataset has a large problem with lack of diversity which may limit generalizability of results, increase health inequalities - specifically minority access to clinical trials - and lead to overfitting as samples are shared between different Alzheimer’s initiatives. ADNI1, which constitutes all 98 of this study’s participants, was composed of 90.4% non-Hispanic white participants - a significant racial bias (Lim et al., 2023; Bøttern et al., 2023) Ethical concerns regarding access to ADNI human data were mitigated by (1) their internal and sweeping securing of patient confidentiality and (2) by obtaining formal access approval from the institution’s internal ethical committee and thus meeting their ethical standards. Throughout the study, no personally identifying information was accessible or utilized nor terms of the ADNI use agreement violated. Limitations The largest limitation was sample size, with 98 total fitting the criteria, and only 38 CN A+ individuals the primary sample for analysis. Thus, while amyloid status has been validated as having a strong effect on predicting AD progression, smaller effects - i.e. FD-z - may not be a significant biomarker without a larger sample. Furthermore, there is a CDR-SB floor in CN individuals which may affect its linear coefficient and significance. A GAMs model (Wood & Augustin, 2002) - of which a suitable non-linear curve is drawn to fit the general trend of data - was performed but was, due to the low sample size, incoherent (artifact in appendix). There may also be a large ComBat instability due to the low number of subjects per scanner and therefore low amount to generalize to with a model which may promote overfitting.
preclinical AD. The comparable results between the Thal and Braak staging analysis - particularly the small coefficient of Time x CDR-SB can align with tiny microstructural changes that the analysis picked up. However, due to its insignificance, it cannot be whether this small effect is due to random chance or a real interaction. Future Directions The analysis should be repeated with the large number of participants excluded for no provided UCSF FreeSurfer downloads (n=474) r. This would require a unified FreeSurfer method across the entire selected cohort and would substantially improve the power of the analysis and provide a clearer consensus on the preclinical use of FD-z in AD. However, even with the full 179 CN A+ cohort, the power will only 14% if the effect size did not change. There is still a large unmet clinical need for cheap and accessible pre-clinical biomarkers to reliably stratify those at risk for AD and apply prophylactic lifestyle changes or preventative clinical trials if deemed ethical. Amyloid PET - a reliable stratifier - was deemed to cost from $1346 to $2964 in out-of-pocket expenses to the average American citizen, which inhibits screening and wide application as a low-cost biomarker (Canestaro et al., 2024). Due to these inconclusive findings, structural MRI-based biomarkers may not be the best solution to detect preclinical AD - though more research with a larger sample size may be able to dispute this. Along with this, more research should be conducted on blood plasma biomarkers as their rapid improvement could create a cost-effective and accessible test which can allow for intervention and mass screening – enabling more possibilities for prophylactic clinical trials (Teunissen & van Harten, 2026).
Conclusion This study evaluated the clinical utility of Fractal Dimension Z scores as a novel, structural MRIderived biomarker to detect preclinical Alzheimer’s disease and predict functional cognitive and conversion from cognitively normal to AD. This research utilized standardized data from the ADNI database to test whether structural complexity alterations within target Braak and Thal staging regions could distinguish between CN A- and CN A+ individuals and map their subsequent clinical trajectories via LME and survival models. Ultimately, across all primary and secondary statistical analyses, FD-z did not demonstrate a significant capacity to differentiate amyloid status or predict longitudinal degradation, failing to support hypotheses H1 and H2.
Overall, it is unclear whether the null result arose from lack of data or the lack of brain degradation in
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Acknowledgements The author is deeply grateful for the continuous expert support and guidance of Dr Matthew Hill as both a research advisor and mentor throughout the project.
Data use permissions
Figure 13: Summary Forest Plot Figure, the dots are the reported value, and the bars are the 95% CI, with the dashed lines representing the null value, all were insignificant
These null findings indicate that either the biochemical deposition of amyloid-beta plaques did not produce structural changes or that, as a weak effect, the low sample size meant the analysis was underpowered (with only 6% statistical power). As the global prevalence of AD climbs, the clinical mandate for cheap, non-invasive diagnostic tools remains vital. Future efforts should focus on scaling this analysis across wider cohorts to definitively overcome power constraints, while concurrently integrating emerging blood plasma biomarkers.
Declaration of use of AI AI was used throughout the project in multiple capacities; the specifics are listed below: • AI was used to create the methodological protocol - namely, the selection and sequencing of tests and analyses. • AI created all the R code for the analysis, it is included in the Appendix • AI was used to socratically teach the more complex statistical methods for further understanding • All writing and explanations in this study were not created by AI, though AI was used to proofread and increase readability once the first draft was completed • AI may have been unintentionally called due to the inbuilt AI overview when using google search The model used throughout this project was Claude Opus 4.7-4.8. Although AI was utilized throughout this project, it never - at any point - had access to participant level data to mitigate the potential risk of data sharing.
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Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_a pply/ADNI_Acknowledgement_List.pdf.
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Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12(1). https://doi.org/10.1186/14712105-12-77 Royse, S. K., Minhas, D. S., Lopresti, B. J., Murphy, A., Ward, T., Koeppe, R. A., Bullich, S., DeSanti, S., Jagust, W. J., & Landau, S. M. (2021). Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach. Alzheimer’s Research & Therapy, 13(1). https://doi.org/10.1186/s13195-021-00836-1 Scheltens, P. (2021). Alzheimer’s disease. The Lancet, 397(10284), 1577–1590. https://doi.org/10.1016/S01406736(20)32205-4 Schoen, D., Deutsch, S., Mehta, J., Wang, S., Kornak, J., Starr, P. A., Wang, D. D., Ostrem, J. L., Bledsoe, I. O., & Morrison, M. A. (2025). Boundary complexity of cortical and subcortical areas predicts deep brain stimulation outcomes in Parkinson’s disease. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-60695-4 Schoenfeld, D. A. (1983). Sample-Size Formula for the Proportional-Hazards Regression Model. Biometrics, 39(2), 499. https://doi.org/10.2307/2531021 Sheppard, O., & Coleman, M. (2020, December 18). Alzheimer’s Disease: Etiology, Neuropathology and PubMed; Exon Publications. Pathogenesis. https://www.ncbi.nlm.nih.gov/books/NBK566126/
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Appendix
Thal, D. R. (2006). The Development of Amyloid beta Protein Deposits in the Aged Brain. Science of Aging Knowledge Environment, 2006(6), re1–re1. https://doi.org/10.1126/sageke.2006.6.re1 The University of Queensland. (2019, August 8). What causes dementia? Uq.edu.au. https://qbi.uq.edu.au/dementia/dementia-causes-andtreatment Therneau, T. M., & Grambsch, P. M. (2000). Modeling survival data : extending the Cox model. Springer. Wood, S. N., & Augustin, N. H. (2002). GAMs with integrated model selection using penalized regression splines and applications to environmental modelling.
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Investigation into recursive Equations and their Generalised Functional Counterparts Eric Hayman Barker College Many of the processes that are used in mathematics are the general form of a function that has been iterated many times and generalized: Addition is repeated succession, multiplication is repeated addition and exponentiation is repeated multiplication. However careful analysis of other real functions reveals that they can also be represented by iterating functions. This paper has determined that the solution for finding the iterative function is 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(1 + 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥)), when the general function is 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥). This relationship makes certain predictions about the properties and the relationship between 𝑓𝑓𝑓𝑓 and 𝑔𝑔𝑔𝑔 that can help to find the general function which can have real benefits in science. The goal of this paper is to investigate methods from iterative function to find connections between these two forms of the same function. Understanding the restrictions of finding iterative functions will yield further benefit.
Literature Review In science, sometimes data is collected that relates to functions, but functions that are less useful for presentation and analysis. This can occur due to lack of information or frequency of data collection. One solution is to treat the data as contributing to an iterative function, and should a corresponding general function be able to be determined, the general function may be a more useful way of presenting and analysing the data. Therefore, finding more robust relationships between iterative and general functions (the purpose of this paper), has the potential of benefitting various fields of quantitative science. The concept of iteration is the repeated application of a function to its own output. It represents a foundational bridge between static algebraic structures and the complexities of fractals hidden within number theory. In pure mathematics, an iterative function is defined as a self-mapping 𝑔𝑔𝑔𝑔: 𝑋𝑋𝑋𝑋 → 𝑋𝑋𝑋𝑋 on a set 𝑋𝑋𝑋𝑋, where the 𝑛𝑛𝑛𝑛-th iterate is typically described as 𝑓𝑓𝑓𝑓 𝑛𝑛𝑛𝑛 (𝑥𝑥𝑥𝑥) and describes the system’s state after 𝑛𝑛𝑛𝑛 steps. Unlike applied dynamical systems that model physical processes with noise or parameter drift, purely mathematical iterative functions operate in a deterministic, abstract realm though they do often give rise to real phenomenon as will be shown in this paper. This literature review contains key developments in the study of these functions, such as fixed-point theories, convergence, the arithmetic of integer iterations, and the profound computational limits revealed by iterated recursive functions. Early 20th-century analysis, particularly the work of Brouwer and Stefan Banach (Norwegian University of
Science and Technology [NTNU], n.d.), established the rigorous foundation for understanding iteration through fixed-point theorems (“Banach’s Fixed Point Theorem…,” n.d.). Banach’s fixed-point theorem states that a contraction mapping on a complete metric space has a unique fixed point. What this means is that if the result of iterating a function leaves it in a smaller range, then at least one point remains the same and that the iteration of the function from any starting point converges to that fixed point. This result provided a constructive method for solving equations by transforming them into iterative functions 𝑥𝑥𝑥𝑥𝑛𝑛𝑛𝑛+1 = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥𝑛𝑛𝑛𝑛 ) and remains a pillar of numerical analysis. However, iteration is not limited to convergence. Beyond continuous analysis, discrete iterative functions on integers reveal deep number-theoretic mysteries. Perhaps the most famous open problem in this domain is the Collatz conjecture (Becker, 2025). Define 𝑇𝑇𝑇𝑇: ℕ → ℕ by 𝑇𝑇𝑇𝑇(𝑛𝑛𝑛𝑛) = 𝑛𝑛𝑛𝑛/2 if 𝑛𝑛𝑛𝑛 even, nd 𝑇𝑇𝑇𝑇(𝑛𝑛𝑛𝑛) = 3𝑛𝑛𝑛𝑛 + 1 if 𝑛𝑛𝑛𝑛 odd (Becker, 2025). The conjecture posits that iteration from any positive integer eventually reaches the cycle 1 → 4 → 2→ 1. Despite extensive computational verification for numbers up to 268 (Roosendaal, 2021), no proof exists. This exemplifies a key role of iterative functions: they define simple rules that generate profound unsolved problems, testing the limits of number theory and logic. Another remarkable family that spawns from the ideas of iterating functions that this paper may be useful for is the Goodstein sequences. These functions are interesting for the purposes of our paper as they demonstrate the growth that can occur and reveal the
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Scientific Research in School Volume 8 Issue 1 2026 somewhat chaotic results that we can see, which are based on iterating a function that writes a number in hereditary base-2 notation, replaces all base-2 with base-3, then subtracts 1 (Kirby & Paris, 1982). While these sequences appear to grow incomputable rate, Goodstein proved that every such sequence eventually reaches 0. Crucially, Kirby and Paris showed that the theorem cannot be proved within Peano arithmetic. This established iterative integer functions as a tool for demonstrating logical incompleteness. The function’s purely mathematical definition yields behaviours unprovable by standard arithmetic, an insight into the nature of proof and computation. Kleene would then go on to introduce the 𝜇𝜇𝜇𝜇-operator, representing unbounded search (iteration until a condition holds), to define the full set of general recursive functions (Kleene, 1952). This coincided with Turing computable functions that also followed an iterative process (Introduction to Algorithms Notes on Turing Machines, 2012). Thus, the notion of “algorithm” is mathematically captured as an iterative process over natural numbers. The Church-Turing thesis asserts that every effectively calculable function is recursive (David Deutsch, 1985). Consequently, iterative functions are not just mathematical objects; they are the formal definition of what it means to compute. Moreover, recursive function theory reveals incomputable iterative functions. The Busy Beaver function Σ(𝑛𝑛𝑛𝑛), defined as the maximum number of 1’s a halting Turing machine with 𝑛𝑛𝑛𝑛 states can produce, grows faster than any computable function (Harland, 2005). For any computable function 𝑓𝑓𝑓𝑓, there exists 𝑁𝑁𝑁𝑁 such that Σ(𝑛𝑛𝑛𝑛) > 𝑓𝑓𝑓𝑓(𝑛𝑛𝑛𝑛) for all 𝑛𝑛𝑛𝑛 ≥ 𝑁𝑁𝑁𝑁. This uncomputability arises from the halting problem, but the function’s definition is purely mathematical: iterate a Turing machine’s transition rules from the blank tape. Thus, iteration can define functions that are mathematically well-specified but algorithmically impossible to compute in full. In holomorphic dynamics which is the field of study of dynamic systems obtained by iterating a complex analytic function (Complex Dynamics, 2019), iteration of rational maps on the Riemann sphere leads to the study of Julia sets and the famous/infamous Mandelbrot set, fractals of infinite complexity derived from the simple function 𝑓𝑓𝑓𝑓(𝑧𝑧𝑧𝑧) = 𝑧𝑧𝑧𝑧 2 + 𝑐𝑐𝑐𝑐 where c is a complex number and the set is defined as the area in which it does not diverge to infinity after being iterated infinitely (Campuzano, 2019). In arithmetic dynamics, which is a growing field, researchers study iteration of polynomial maps over finite fields and number fields, seeking Diophantine analogs of
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classical dynamical properties (Silverman, 2007). Meanwhile, the search for a resolution to the Collatz conjecture drives novel approaches in ergodic theory and fractal analysis. Purely mathematical iterative functions thus remain a fertile ground for foundational research, reminding us that simple iterative processes can reveal the deepest truths.
Scientific Research Question What relationships exist between general functions and their iterative counterparts so that either can be more easily found with the end goal of finding a way to turn iterative functions into general functions?
Scientific Hypothesis That some simple rules can be determined to find corresponding iterative and general functions.
Methodology The method to complete the project was as follows: Part A: By finding and inspecting a variety of known general and iterative function pairs so that patterns and similarities were identified that allowed identification of some new general and iterative function pairs. Part B: A relationship was proposed from some of these new examples which could readily support finding iterative functions from general functions: Theorem 1: 𝒈𝒈𝒈𝒈(𝒙𝒙𝒙𝒙) = 𝒇𝒇𝒇𝒇(𝒇𝒇𝒇𝒇−𝟏𝟏𝟏𝟏 (𝒙𝒙𝒙𝒙) + 𝟏𝟏𝟏𝟏).
(It will be discussed how difficulty prevented this being done for the reverse, finding general functions from iterative functions). Part C: Theorem 1 was proven as a relationship that describes related iterative and general functions (given certain conditions). Part D: Theorem 1 was then used to find corresponding iterative functions for some more complicated general functions than were explored in Parts 1 and 2.
Results Part A: Investigation of known general and iterative function pairs Most of the functions that we see in everyday life are simply the general function of other iterative functions. For example, addition is iterated succession, multiplication is repeated addition and
Scientific Research in School Volume 8 Issue 1 2026 exponentiation, it is possible to go even further with tetration still. Within the convention that this paper sets out each would be written as so. Succession to Addition. Let succession of 𝑥𝑥𝑥𝑥 ≡ 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥 (𝑦𝑦𝑦𝑦) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) Where 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑦𝑦𝑦𝑦 + 𝑥𝑥𝑥𝑥 Let addition of 𝑦𝑦𝑦𝑦 to x ≡ 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥 (0) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) Where 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑦𝑦𝑦𝑦𝑥𝑥𝑥𝑥
Let multiplication of 𝑦𝑦𝑦𝑦 to 𝑥𝑥𝑥𝑥 ≡ 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥 (1) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) Where 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑦𝑦𝑦𝑦 𝑥𝑥𝑥𝑥
However, the next logical step of combining multiplication and addition was still massively more complicated. Let 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑛𝑛𝑛𝑛𝑥𝑥𝑥𝑥 + 𝑚𝑚𝑚𝑚 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥 (𝑦𝑦𝑦𝑦) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥)
Then 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑛𝑛𝑛𝑛 𝑥𝑥𝑥𝑥 𝑧𝑧𝑧𝑧 +
𝑚𝑚𝑚𝑚(𝑛𝑛𝑛𝑛𝑥𝑥𝑥𝑥 −1) 𝑛𝑛𝑛𝑛−1
.
This is massively more complicated than before. Part B: Theorem 1 discovery It was attempted to solve for the iterative function for triangular numbers. Triangular numbers can be defined as follows: 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 − 1) + 𝑥𝑥𝑥𝑥 In order to turn this into an iterative function all that I had to do was represent the right hand of the function using only 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 − 1), I realised that since I knew the inverse function for 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) I could get away with this: 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 − 1) + 𝑓𝑓𝑓𝑓 −1 �𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 − 1)� + 1 Giving 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) as 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑥𝑥𝑥𝑥 + 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) + 1
Using the Idea that any value of 𝑥𝑥𝑥𝑥 was now possible by representing it as 𝑓𝑓𝑓𝑓 −1 �𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 − 1)� + 1 we could represent any function as 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) → 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓 −1 �𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 − 1)� + 1) finally turning this into the general form was easy: 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) + 1).
Part C: Theorem 1 proof and formal definitions. It is necessary to mathematically analyse these pairs going forward. That they are proved mathematically. This, thus, is my proof of theorem 1. Let 𝑓𝑓𝑓𝑓𝑥𝑥𝑥𝑥 (𝑎𝑎𝑎𝑎) represent the function 𝑓𝑓𝑓𝑓 iterated upon itself 𝑥𝑥𝑥𝑥 times starting at the value 𝒂𝒂𝒂𝒂, such that
𝑓𝑓𝑓𝑓0 (𝑥𝑥𝑥𝑥) = 𝑥𝑥𝑥𝑥 𝑓𝑓𝑓𝑓1 (𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) 𝑓𝑓𝑓𝑓𝑎𝑎𝑎𝑎 (𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓𝑎𝑎𝑎𝑎−1 (𝑥𝑥𝑥𝑥))
(In this paper both 𝑓𝑓𝑓𝑓−1 (𝑥𝑥𝑥𝑥) and 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) will be the notation for the inverse of a function due to the fact that 𝑓𝑓𝑓𝑓−1 (𝑥𝑥𝑥𝑥) is consistent with my defined notation, but 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) is more formal.)
Iterative functions and general functions describe the relationship between 2 specific functions, where after defining 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) as the iterative function the general function would be defined as 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥 (𝑎𝑎𝑎𝑎).
Restrictions It is necessary for this proof that 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) is invertible. A function is invertible if it is bijective, bijectivity requires two subcomponents: Subjectivity and Injectivity.
Surjective For a function 𝑓𝑓𝑓𝑓 to be surjective it means that for all possible solutions allowed by the functions mapping, there must be a solution allowed by the mapping of 𝑋𝑋𝑋𝑋.
For example: For the function 𝑓𝑓𝑓𝑓: ℝ → ℝ defined as 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑥𝑥𝑥𝑥 2 the equation is not surjective as the negative elements of the real numbers are not possible to be reached from the real numbers of the inputs. To make the function 𝑓𝑓𝑓𝑓 surjective, it must be defined as: 𝑓𝑓𝑓𝑓: ℝ → ℝ+ : 𝑥𝑥𝑥𝑥 ↦ 𝑥𝑥𝑥𝑥 2 . This function however is still not inversible because of the second condition: Injective. Injective For a function 𝑓𝑓𝑓𝑓 to be injective if the result of 2 functions having the same then the inputs must be identical: ∀𝑥𝑥𝑥𝑥1 , 𝑥𝑥𝑥𝑥2 ∈ 𝑋𝑋𝑋𝑋, 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥1 ) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥2 ) ⇒ 𝑥𝑥𝑥𝑥1 = 𝑥𝑥𝑥𝑥2 , if this does not hold then we can get multiple results leading to multiple answers.
For the surjective function given earlier, 𝑓𝑓𝑓𝑓: ℝ → ℝ+ : 𝑥𝑥𝑥𝑥 ↦ 𝑥𝑥𝑥𝑥 2 , this function is still not injective as for inputs 𝑎𝑎𝑎𝑎 and −𝑎𝑎𝑎𝑎 , 𝑓𝑓𝑓𝑓(𝑎𝑎𝑎𝑎) = 𝑓𝑓𝑓𝑓(−𝑎𝑎𝑎𝑎) yet 𝑎𝑎𝑎𝑎 ≠ −𝑎𝑎𝑎𝑎 (assuming 𝑎𝑎𝑎𝑎 ≠ 0), to fix this we must restrict the domain so that the function is ‘one-to-one’. To make the function injective it could be defined as 𝑓𝑓𝑓𝑓: ℝ+ → ℝ+ : 𝑥𝑥𝑥𝑥 ↦ 𝑥𝑥𝑥𝑥 2 . Now the function has been properly restricted to be made inversible. Axioms This section gives simple rules that are true for all cases, and are useful for different proofs 𝑔𝑔𝑔𝑔𝑎𝑎𝑎𝑎 (𝑥𝑥𝑥𝑥) ∘ 𝑔𝑔𝑔𝑔𝑏𝑏𝑏𝑏 (𝑥𝑥𝑥𝑥) = 𝑔𝑔𝑔𝑔𝑎𝑎𝑎𝑎+𝑏𝑏𝑏𝑏 (𝑥𝑥𝑥𝑥) 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 + 1) = 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥+1 (𝑓𝑓𝑓𝑓(0)) 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 + 1) = 𝑔𝑔𝑔𝑔(𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥)) Science Extension Journal • 151
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Intuitively all this expression means is that when assuming 𝑢𝑢𝑢𝑢 to be the 𝑋𝑋𝑋𝑋 𝑡𝑡𝑡𝑡ℎ iterate. By taking the inverse 𝑓𝑓𝑓𝑓 −1 will give the value of 𝑋𝑋𝑋𝑋 before adding an additional one and putting it into the function to give the 𝑋𝑋𝑋𝑋 + 1𝑡𝑡𝑡𝑡ℎ iterate and 𝑔𝑔𝑔𝑔 is defined to give the next iterate of the 𝑋𝑋𝑋𝑋 𝑡𝑡𝑡𝑡ℎ iterate.
This is important as it gives us grounds on how we would expect the relationship between these functions to behave, and what functions to test to get unusual and unexpected results. Giving unique insight into the workings of said functions. This proof makes assertions about what 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) should be, and the equation itself asserts that an inverse to the general function exists, which isn’t the case. It does mean that the iterated form won’t be a function.
Part D: More complicated relationships Letting 𝑓𝑓𝑓𝑓: [0, ∞) → ℝ+ : 𝑥𝑥𝑥𝑥 ↦ 𝑥𝑥𝑥𝑥 2 , and 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥 �𝑓𝑓𝑓𝑓(0)� ≔ 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥), This formatting can be understood as taking a restricted mapping of the function, in order to fit the restrictions that we said needed to be followed. The ≔ simply means ‘is defined as’, this is needed because it defines the relationship between the two functions.
Table 1: Demonstrating how theorem 1 proves 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑥𝑥𝑥𝑥 + 2√𝑥𝑥𝑥𝑥 + 1. 𝒇𝒇𝒇𝒇(𝒙𝒙𝒙𝒙)
𝒈𝒈𝒈𝒈𝒙𝒙𝒙𝒙 (… )
Fully expanded form
𝑓𝑓𝑓𝑓(0) 𝑓𝑓𝑓𝑓(1)
𝑓𝑓𝑓𝑓(0) 𝑔𝑔𝑔𝑔(𝑓𝑓𝑓𝑓(0))
0 (√0 + 1)2
𝑓𝑓𝑓𝑓(2) 𝑓𝑓𝑓𝑓(3) 𝑓𝑓𝑓𝑓(4)
𝑔𝑔𝑔𝑔(𝑔𝑔𝑔𝑔(𝑓𝑓𝑓𝑓(0)))
𝑔𝑔𝑔𝑔(𝑔𝑔𝑔𝑔(𝑔𝑔𝑔𝑔(𝑓𝑓𝑓𝑓(0))))
𝑔𝑔𝑔𝑔(𝑔𝑔𝑔𝑔(𝑔𝑔𝑔𝑔(𝑔𝑔𝑔𝑔�𝑓𝑓𝑓𝑓(0)�)))
(�(√0 + 1)2 + 1)2
(�(�(√0 + 1)2 + 1)2 + 1)2 (�(�(�(√0 + 1)2 + 1)2 + 1)2 + 1)2
Solution
Basic proof of theorem Let 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥)be a bijective function, let 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥 �𝑓𝑓𝑓𝑓(0)� ≔ 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑔𝑔𝑔𝑔𝑥𝑥𝑥𝑥 �𝑓𝑓𝑓𝑓(0)� (By definition) 𝑥𝑥𝑥𝑥 + 1 = 𝑥𝑥𝑥𝑥 + 1 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 + 1) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 + 1) 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓 −1 (𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥)) + 1) = 𝑔𝑔𝑔𝑔(𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥)) 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑢𝑢𝑢𝑢 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓 −1 (𝑢𝑢𝑢𝑢) + 1) = 𝑔𝑔𝑔𝑔(𝑢𝑢𝑢𝑢)
0 1 4 9
16
While it can be useful to know the iterative for 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) it is more useful to know the general form 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥). This allows a person to do interesting things like applying the function to itself only a fraction of a time. 𝑔𝑔𝑔𝑔1 (𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥)) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥 + 0.5) 2
The theorem always gives g as valid function. It is therefore important to ask what happens when 𝑔𝑔𝑔𝑔 is not a function: Such that, if an experiment exists that does have a valid function 𝑓𝑓𝑓𝑓 but doesn’t have a valid function for g, (the function has multiple outputs for a single input) then it can be concluded that 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) is not bijective and 𝑓𝑓𝑓𝑓 −1 is not a function. What does this mean in practice? Let there be the non-bijective function depicted in Figure 1.
It can be found from table 1 it should be hopefully more clear about how this formula works, the formula proves that 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑥𝑥𝑥𝑥 + 2√𝑥𝑥𝑥𝑥 + 1. This is shown to be true in table 1.
Figure 1: 𝑥𝑥𝑥𝑥 2 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑙𝑙𝑙𝑙𝑛𝑛𝑛𝑛𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑙𝑙𝑙𝑙ℎ𝑎𝑎𝑎𝑎𝑙𝑙𝑙𝑙 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑙𝑙𝑙𝑙 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑖𝑖𝑖𝑖𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑖𝑖𝑖𝑖𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙
This function is not injective, and so it can not be inverted.
152 • Science Extension Journal
Scientific Research in School Volume 8 Issue 1 2026
However, 𝑓𝑓𝑓𝑓: ℝ → ℝ+ : 𝑥𝑥𝑥𝑥 ↦ 𝑥𝑥𝑥𝑥 2 can be represented as 𝐺𝐺𝐺𝐺(𝑥𝑥𝑥𝑥) ℝ+ ∪ 0 → ℝ+ : 𝑥𝑥𝑥𝑥 ↦ 𝑥𝑥𝑥𝑥 2 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝐺𝐺𝐺𝐺(𝑥𝑥𝑥𝑥) ∪ 𝐹𝐹𝐹𝐹(𝑥𝑥𝑥𝑥) � 𝐹𝐹𝐹𝐹(𝑥𝑥𝑥𝑥) ℝ− → ℝ+ : 𝑥𝑥𝑥𝑥 ↦ 𝑥𝑥𝑥𝑥 2 By splitting the function in two parts Thus we can find 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝐺𝐺𝐺𝐺 −1 (𝑥𝑥𝑥𝑥) + 1) ∪ 𝑓𝑓𝑓𝑓(𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥) + 1) 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(√𝑥𝑥𝑥𝑥 + 1) ∪ 𝑓𝑓𝑓𝑓(−√𝑥𝑥𝑥𝑥 + 1) 2
𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = �±√𝑥𝑥𝑥𝑥 + 1�
𝑔𝑔𝑔𝑔 is not a function but made up of two functions.
While it can be clearly seen from the equation form of 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) and the graph given by the equation in Figure 2 that there are two solutions for all positive x values, it is also possible to see this clearly from the properties of 𝑓𝑓𝑓𝑓:
Figure 2: iterative form of 𝑥𝑥𝑥𝑥 2
Noticing that 𝑓𝑓𝑓𝑓(𝑢𝑢𝑢𝑢) = 𝑓𝑓𝑓𝑓(−𝑢𝑢𝑢𝑢) 𝑔𝑔𝑔𝑔�𝑓𝑓𝑓𝑓(𝑢𝑢𝑢𝑢)� must give 𝑓𝑓𝑓𝑓(𝑢𝑢𝑢𝑢 + 1) But 𝑔𝑔𝑔𝑔�𝑓𝑓𝑓𝑓(−𝑢𝑢𝑢𝑢)� must give 𝑓𝑓𝑓𝑓(−𝑢𝑢𝑢𝑢 + 1) So 𝑔𝑔𝑔𝑔�𝑓𝑓𝑓𝑓(𝑢𝑢𝑢𝑢)� must give 𝑓𝑓𝑓𝑓(−𝑢𝑢𝑢𝑢 + 1) Despite 𝑓𝑓𝑓𝑓(𝑢𝑢𝑢𝑢 + 1) ≠ 𝑓𝑓𝑓𝑓(−𝑢𝑢𝑢𝑢 + 1)
Thus 𝑔𝑔𝑔𝑔(𝑓𝑓𝑓𝑓(𝑢𝑢𝑢𝑢)) must give 2 solutions where one solution is due to 𝑓𝑓𝑓𝑓(𝑢𝑢𝑢𝑢), and the other part is due to 𝑓𝑓𝑓𝑓(−𝑢𝑢𝑢𝑢).
These properties hold true for all even functions. Where the iterated forms of all non-constant functions will all give at least two solutions. While it often seems that the iterated forms are often more complicated than the general form. As has been demonstrated, this isn’t always the case. As can be seen by looking at the iterated form of the general
function 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = 𝑛𝑛𝑛𝑛 𝑥𝑥𝑥𝑥 𝑎𝑎𝑎𝑎 +
𝑚𝑚𝑚𝑚(𝑛𝑛𝑛𝑛𝑥𝑥𝑥𝑥 −1) 𝑛𝑛𝑛𝑛−1
. The iterated function
takes the form: 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑛𝑛𝑛𝑛𝑥𝑥𝑥𝑥 + 𝑚𝑚𝑚𝑚. In the iterated function the 𝑛𝑛𝑛𝑛 𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛𝑎𝑎𝑎𝑎 𝑚𝑚𝑚𝑚 are constants, and the general function has an additional term ‘𝑎𝑎𝑎𝑎’ to describe 𝑓𝑓𝑓𝑓(0) or the starting value for the general function. One of the new functions that was found through this equation was the iterative function of sin 𝑥𝑥𝑥𝑥. Letting 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) = sin 𝑥𝑥𝑥𝑥 and the 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) = sin−1 𝑥𝑥𝑥𝑥 (note: in this case the inverse sine function refers to the entire domain and range rather that how it is normally restricted) 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = sin(sin−1 (𝑥𝑥𝑥𝑥) + 1) 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = 𝑥𝑥𝑥𝑥 cos 1 ± sin 1 �1 − 𝑥𝑥𝑥𝑥 2
This results in a neat oval.
Discussion Rules and patterns The following is a series of rules and patterns that can be used when comparing similar function pairs. First the vertical scaling, let 𝐹𝐹𝐹𝐹(𝑥𝑥𝑥𝑥) be the general function and G(x) be the accompanying iterative function. When we let 𝑛𝑛𝑛𝑛𝐹𝐹𝐹𝐹(𝑥𝑥𝑥𝑥) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) when we try to find the accompanying iterative function 𝑔𝑔𝑔𝑔. 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) = 𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥⁄𝑛𝑛𝑛𝑛) 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) + 1) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑛𝑛𝑛𝑛𝐹𝐹𝐹𝐹(𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥⁄𝑛𝑛𝑛𝑛) + 1) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥𝑛𝑛𝑛𝑛) 𝐹𝐹𝐹𝐹(𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥) + 1) = = 𝐺𝐺𝐺𝐺(𝑥𝑥𝑥𝑥) 𝑛𝑛𝑛𝑛 𝑛𝑛𝑛𝑛𝐺𝐺𝐺𝐺(𝑥𝑥𝑥𝑥⁄𝑛𝑛𝑛𝑛) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) Now the horizontal scaling, similar assumptions to the last time except we let 𝐹𝐹𝐹𝐹(𝑥𝑥𝑥𝑥⁄𝑛𝑛𝑛𝑛) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) and we still try to find 𝑔𝑔𝑔𝑔. 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) = 𝑛𝑛𝑛𝑛𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥) 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) + 1) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑛𝑛𝑛𝑛𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥) + 1 𝐹𝐹𝐹𝐹 � � = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑛𝑛𝑛𝑛 1 𝐹𝐹𝐹𝐹 �𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥) + � = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑛𝑛𝑛𝑛
Horizontal dilation, 𝐹𝐹𝐹𝐹(𝑥𝑥𝑥𝑥 − 𝑛𝑛𝑛𝑛) = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) now finding 𝑔𝑔𝑔𝑔 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) = 𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥) + 𝑛𝑛𝑛𝑛 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) + 1) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝐹𝐹𝐹𝐹(𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) + 1 − 𝑛𝑛𝑛𝑛) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝐹𝐹𝐹𝐹(𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥) + 𝑛𝑛𝑛𝑛 − 𝑛𝑛𝑛𝑛 + 1) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝐹𝐹𝐹𝐹(𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥) + 1) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝐺𝐺𝐺𝐺(𝑥𝑥𝑥𝑥) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥)
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Scientific Research in School Volume 8 Issue 1 2026 Vertical translation, 𝐹𝐹𝐹𝐹(𝑥𝑥𝑥𝑥) + 𝑛𝑛𝑛𝑛 = 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) 𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) = 𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥 − 𝑛𝑛𝑛𝑛) 𝑓𝑓𝑓𝑓(𝑓𝑓𝑓𝑓 −1 (𝑥𝑥𝑥𝑥) + 1) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝑓𝑓𝑓𝑓(𝐹𝐹𝐹𝐹 −1 (𝑥𝑥𝑥𝑥 − 𝑛𝑛𝑛𝑛) + 1) = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝐹𝐹𝐹𝐹(𝐹𝐹𝐹𝐹(𝑥𝑥𝑥𝑥 − 𝑛𝑛𝑛𝑛) + 1) + 𝑛𝑛𝑛𝑛 = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) 𝐺𝐺𝐺𝐺(𝑥𝑥𝑥𝑥 − 𝑛𝑛𝑛𝑛) + 𝑛𝑛𝑛𝑛 = 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥)
These results can be used to help describe the results that we saw in the case of the iterate form of the sine of 𝑥𝑥𝑥𝑥, First we shouldn’t expect the iterate to be a function since it is not injective, second of all, from what we saw about horizontal translations the long arms of the function shouldn’t add anything new, since we can translate it 2𝜋𝜋𝜋𝜋 radians and come up with the same function. Additionally, we wouldn’t expect any holes or discontinuities since both the inverse function and the function itself don’t contain any, finally we would expect the iterate form to have a domain and range of -1 to 1 since that is the range of the general function. Unfortunately certain functions like the Mandelbrot set display infinite complexity from even quite simple iterative functions that cast doubt onto the possibility of finding general function based off of iterative functions. In general, how well the equation works is determined by the inverse of the function. When the general function has an elementary inverse, or in other words, those which are obtained in a finite number of steps by performing algebraic operations and taking exponentials and logarithms (Ritt, 1925). In these situations, an iterative equation can be found and applied easily. The next problem comes from when the general function is not injective, in these cases the inverse general function will loop back on itself, and the iterative function will also double back on itself. This means that when analysing the iterative function special care must be taken as the iterative function may give 2 possible answers when only 1 is possible. The equation loses a lot of its usefulness when the inverse of the general function is not possible to find. For example, 𝑥𝑥𝑥𝑥𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖(𝑥𝑥𝑥𝑥). An iterative solution could still be found but it would require finding the results point by point rather than by using graphing. Overall, the hypothesis has been shown to be partially true. Overall finding 1: No simple rules (at this stage) of finding iterative functions from general functions. It is likely that none are possible.
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Overall finding 2: There exists a theorem (Theorem 1), first presented in this report, that is proven to be mathematically correct, that demonstrates usefulness in finding general functions from iterative functions (given certain conditions) Overall finding 3: It has not been able to find general functions from all iterative functions, even within certain conditions, so further research needs to be done to investigate the limitations.
Conclusion Further research should be put into finding the general function based off of the iterative function, as well as putting more research into the effects and growth of certain functions. For example, one of the iterative functions of the Collatz conjecture introduced back in the literature review is: 𝑔𝑔𝑔𝑔(𝑥𝑥𝑥𝑥) = − �
5𝑥𝑥𝑥𝑥 1 7𝑥𝑥𝑥𝑥 1 + � cos(𝜋𝜋𝜋𝜋𝑥𝑥𝑥𝑥) + + 4 2 4 2
Finding the general form of this function and finding the solution as 𝑓𝑓𝑓𝑓(𝑥𝑥𝑥𝑥) approaches infinity would be especially useful for proving the formula to be true or not.
Declaration of use of AI I used minimal artificial intelligence to help me write the literature review. this made the best use of generative AI to my knowledge. DeepSeek helped me to find parts of the research that I had previously overlooked because they weren’t tied to the rest of the paper directly. After the AI had created a rough sketch of the items of interest for a paper based around iterative processes, I had to manually change it so that it contained more relative information about the history and progress of iteration in relation to my paper. For example, I had to remove quite a lot of the AI’s recommendations about software iteration because it was not relevant to me. At this point I did further research into the history of relevant functions and wrote my own literature review based on the structure the AI provided.
Acknowledgements Thank you to Dr Matthew Hill and the other science extension staff for the assistance in creating this paper. Thank you also to Barker College for providing me with wonderful teachers and researchers that assisted me in this journey.
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References Becker, Ralf. “Collatz Conjecture: Unveiling Fractal Patterns.” Medium, 31 May 2025, https://medium.com/@ratwolf/collatz-conjecture-patternsin-a-simple-rule-38688455f640. Campuzano, Juan Carlos Ponce. “The Mandelbrot Set.” Complex Analysis, 26 Jan. 2019, https://complexanalysis.com/content/mandelbrot_set.html. “Complex Dynamics.” Wikipedia, 11 May https://en.wikipedia.org/wiki/Complex_dynamics.
2019,
Deutsch, David. “Quantum Theory, the Church–Turing Principle and the Universal Quantum Computer.” Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, vol. 400, no. 1818, 8 July 1985, pp. 97–117, 10.1098/rspa.1985.0070. Accessed 30 July 2026. Harland, James. “Busy Beaver.” Rmit.Edu.Au, School of CS&IT, 16 Aug. 2005, https://titan.csit.rmit.edu.au/~e24991/busybeaver/seminarA ug05.html. Accessed 9 June 2026.
Introduction to Algorithms Notes on Turing Machines. Cornell University, 2012. Kirby, Laurie, and Jeff Paris. “Accessible Independence Results for Peano Arithmetic.” Bulletin of the London Mathematical Society, vol. 14, no. 4, July 1982, pp. 285– 293, 10.1112/blms/14.4.285. Accessed 13 Dec. 2021. Kleene, Stephen Cole. Introduction to Metamathematics. 1952. Ritt, J. F. “Elementary Functions and Their Inverses.” Transactions of the American Mathematical Society, vol. 27, no. 1, 1925, pp. 68–90, 10.1090/s0002-9947-19251501299-9. Accessed 15 June 2026. Norwegian University of Science and Technology. (n.d.). Banach’s fixed point theorem and applications. NTNU. https://wiki.math.ntnu.no/_media/tma4145/2020h/banach.p df Wikipedia Contributors. “Church–Turing Thesis.” Wikipedia, Wikimedia Foundation, 19 Jan. 2020, https://en.wikipedia.org/wiki/Church%E2%80%93Turing_t hesis.
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Study and Optimization of Piezoelectric performance of Rochelle salt filled 3D printed Honeycomb Composites Tong Zhu Barker College Piezoelectric structures are widely used in mechanical, aerospace, biomedical and structural engineering. Rochelle Salt (RS) is a biodegradable piezoelectric material whose brittleness and unreliable performance limit its practical application. This research investigated whether the geometry of the 3d printed supporting matrix can control the piezoelectric performance of RS composites. Four hexagonal PLA honeycomb inspired matrices with increasing cell counts fabricated through FDM 3D printing process, RS crystals were then grown within their cavities. The voltage responses were studied under compression load from 98N to 980N. Low force sensitivity decreased monotonically with cell count, correlating with individual cavity area, indicating that performance is influenced by cell size and continuity. The results also indicate that increasing the cell count would enhance the low force sensitivity of the composite whilst high cell count would lead to a more stable increase in voltage per applied force.
Literature Review The piezoelectric effect occurs when force is applied to a non-centrosymmetric crystal to generate a measurable surface charge. The relationship is empirically expressed as Q = d₃₃S, where Q is the surface charge, d₃₃ is the piezoelectric charge coefficient measured parallel to the polar axis, and S is the applied stress (Zhao et al., 2024). Materials including lead zirconate titanate (PZT) and barium titanate (BaTiO₃) are most prevalent within industry use as these are inorganic ferroelectric perovskites with high d₃₃ values but contain heavy metals, toxic elements or require energy-intensive high temperature manufacturing. This would limit their environmental sustainability and applications (Lemaire et al., 2021) Rochelle salt (RS,potassium sodium tartrate, KNaC₄H₄O₆) was the first material where piezoelectricity was discovered and remains viable as a sustainable transducer because its food grade ingredient, biodegradable and synthesisable from lowcost chemicals at room temperature. Experimentally recorded piezoelectric coefficients for RS vary between 30 to 80 pC/N in pure crystal form (Khan et al., 2020), with composite films reaching as high as 119 pC/N, which exceeds commercial polyvinylidene fluoride trifluoroethylene (PVDF-TrFE) in thin films of comparable thickness (Lemaire et al., 2020). However, RS is ferroelectric only between approximately 255 K and 297 K, with paraelectric phases outside this Curie window where the orthorhombic lattice depolarises and net spontaneous polarisation falls to zero, leading to minimal voltage output (Görbitz & Sagstuen, 2008).
However, the simple shape and fragile property of Rochelle salt crystal limit its applications as the crystal growth is randomly if just leave it in a container. Recently, how to manufacture and accurately control the shape of Rochelle salt crystal and use it as wearable sensors, bioimplants and energy harvesting devices are great challenges. Additive Manufacturing (3D printing) provides us with a good solution by introducing the accurately controlled complexity structure fabrication. Here we report a bioinspired piezoelectric composite by growing the Rochelle salt crystal into the 3D printed honeycomb matrix. This study is focused on the understanding and optimization of the relationship of piezoelectric performance with the number of holes of the 3D printed matrix. Honeycomb Matrix The honeycomb matrix act as a structural scaffold for the RS crystals growth, and increase mechanical durability to induce a higher piezoelectric effect. Honeycomb structures distribute stress more evenly across material within the cavities of the honeycomb when compared to reference geometries (without the use of a 3d printed honeycomb matrix), concentrating higher absolute stress in well-defined regions rather than unpredictably (Köllner et al., 2023). Furthermore, composite matrix structures reduce stress concentration, resulting in more uniform global stress distributions that effectively protect the entire structure from total failure (Liu et al., 2023). Increasing the percentage of holes in the sample, this would lead to an increased crystal surface exposure and exposure along the sides of the samples internally, meaning more material participates in
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Scientific Research in School Volume 8 Issue 1 2026 electromechanical transduction and this would increase the total voltage output of the crystal (Pelayo Garcia et al., 2024). Therefore, it can be hypothesized that the honeycomb structure serves a dual function: it promotes a more uniform stress distribution, enabling the application of greater forces and thereby enhancing voltage output, while also increasing the effective surface area through both external and internal sidewall exposure, further contributing to improved output performance. This effect is analogous to enhancing stress transfer through modifications to the matrix geometry. Numerical simulations have shown that when a composite lacks an efficient pathway for transmitting strain and polarization to its piezoelectric fillers, the resulting electric field is significantly reduced, leading to diminished piezoelectric performance (Safari, 2023). These findings highlight the critical role of effective stress transfer in maximizing voltage output while also increasing the maximum strain that the samples can sustain before failure. Research gap and rationale Despite increase interest in RS, significant gaps remain in how systematically varying the geometry of the supporting matrix affects its electrical output. Current research has established that growing RS crystals in a 3d printed matrix improves mechanical stability, with PLA matrices reinforcing the RS and allowing higher consistency of crystal growth and voltage output (Lemaire et al., 2021). Further work has been done within mimicking the structure of a pomelo fruit peel, created by using a SLA printer reinforcing RS crystals increasing the durability compared to unsupported samples (Kassab et al., 2025). These studies, however, employ fixed or biomimetic geometries that do not systematically isolate the effect of increasing hole count on piezoelectric voltage output. Furthermore, honeycomb structures have been shown to produce better mechanical and piezoelectric outputs than conventional ceramics, yet, the relationship between structure and performance is an area that requires optimisation (Köllner et al., 2023). This study directly addresses these gaps by fabricating four hexagonal PLA honeycomb matrices with systematically increasing hole counts (9, 19, 37, and 61 holes), growing RS crystals uniformly across each sample, and measuring the resulting voltage output, providing the first controlled comparison of how honeycomb porosity level governs the piezoelectric response of RS-based composites.
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Scientific Research Question How does the changing the size of the holes for growth of Rochelle salt crystal within a PLA 3d printed honeycomb matrix influence the piezoelectric performance?
Scientific Hypothesis We hypothesize that increasing the density and spatial uniformity of Rochelle salt crystal-filled hexagonal architectures within a PLA framework will enhance load transfer efficiency and structural stiffness, thereby reducing both local and global deformation under compressive loading. Furthermore, the Rochelle salt crystal regions are expected to exhibit lower deformation than the surrounding PLA frame due to their greater resistance to elastic deformation.
Methodology All 3d printed structures were designed in “Fusion 360” software, then sliced as layers in “Bambu Suite” to generate 2d patterns. To print the honeycombs, a “Bambu Lab A1 Mini” printer was used with Bambu Lab Basic PLA 3D filament. FDM Printing and its use in creating a matrix for RS Fused Deposition Modelling (FDM) was selected for the honeycomb matrices due to its biodegradability, accessibility, and precision. FDM builds 3D structures through layer-by-layer melt extrusion of plastic filament with a digital design (Fusion 360) sliced in Bambu and then executed by the printer (Vyavahare et al., 2022), offering cost efficiency, reliability, and affordability (Alavi et al., 2024). PLA was chosen for several reasons. It degrades naturally while retaining mechanical strength and wear resistance, making it suitable for FDM (Wang et al., 2024), and its biodegradability aligns with the ecofriendly rationale of using Rochelle salt. FDM provides strong stress transfer that improves the RS response, and the rigid PLA walls apply a compressive force into the crystals grown within each hexagonal cavity. FDM's parametric control also allows hexagon side lengths to be scaled by a factor of 1.5 while maintaining consistent print settings, isolating geometry as the independent variable. The 37-cell sample had hexagon side lengths of 2 mm; each increase or decrease in hole count corresponds to a 1.5× decrease or increase in side length respectively (Figure 1). Four samples of each cell dimension were originally printed.
Scientific Research in School Volume 8 Issue 1 2026 aluminium foil was placed on both sides, yet carefully so the two opposite electrodes do not contact
Figure 1: Honeycomb CAD designs, from 7 cells to 61 cells, 3D printed honeycomb scaffold and after the growth of Rochelle salt crystal in the scaffold.
Measurement: A Rigol Digital oscilloscope was used to measure the signals of output voltage under different loading of compressive forces. The 1X probe was attached to one side and the grounding wire on the other side through a wire in between the sample and electrode as seen in figure 2.
Each sample was printed with the following settings. Table 1: FDM 3D printer settings Property Layer Height Wall Loops Infill Density (%) Support Brim Outer wall speed Layer Height
Value 0.08 2 15 0 Auto 200 0.08
All samples were printed horizontally flat on the bed. Preparation of Rochelle Salt (RS) Sodium Carbonate anhydrous powder and Potassium bitartrate powder (Cream of Tartar) were used as raw materials. To prepare the Solution, a large beaker was used to firstly hold 250 grams of Potassium Bitartrate, and then 312.5 ml Distilled water was added in the container. This solution was mixed with the stirring pill on a hot plate maintaining the solution at 75 degrees Celsius. Then, 1 gram at a time, the sodium carbonate is added in, until gas stops releasing from the solution. This means that the chemical reaction is complete and the solution is saturated. The solution is then filtered through a filter paper to get rid of unreacted sodium carbonate and other impurities. Then, 100ml beakers were used, filled with 25ml of the filtered solution. The 3d printed sample is then added into the beaker and crystal formation is observed during the cooling to room temperature. When the solution becomes unsaturated, observed when the crystal stops growing, the print and attached RS crystals are removed from the solution and the remaining RS crystals are dissolved by heating on a hot plate. The sample is then placed in again after the solution is cooled (~40°C) to prevent RS on the structure from dissolving. Repeat this step until crystal coverage is fully around the sample. Then, a Dremel is used to shape the crystal off until there is an even flat surface on both sides ±2mm from the surface of the 3d print. Then, silver paste and
Figure 2: Illustration of the tested 3D-printed-PLA sample electrodes.
Force was applied through the “Bridge Buster” a pneumatic tool that applies a displayed force from 0 to 9800N over approximately 10 seconds. A video recording of the display of applied mass was analysed and stopped at the frames from 0 to 100 kg in increments of 10 simultaneously including the oscilloscope display and the corresponding voltages were recorded. Table 2: Force applied VS voltage output of each cells Force Applied (N) 0 98 196 294 392 490 588 686 784 882 980
Voltage (mV) Trial 1 7 Cells 0 979 1109 1133 1147 1164 1219 1225 1249 1292 1319
19 Cells 0 639 807 868 907 920 940 981 998 1011 1037
37 Cells 0 389 543 555 645 708 778 1043 1070 1209 1243
61 Cells 0 310 451 632 767 832 887 895 921 952 987
Voltage (mV) Trial 2 19 37 Cells Cells 3490 5012 1463 4261 266 2617 216 352 273 342 216 192 152 104 196 60 190 201 102 256 104 301
Results in table 2 show a consistent monotonically increasing trend within the first 4 samples, with varying baseline voltages. This was an exciting result which was then followed by a series of inconsistent measurements on the oscilloscope when every other sample was tested (only the first two of those tests are included in the results table in the two right-hand columns). Multiple steps were taken to restore
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Scientific Research in School Volume 8 Issue 1 2026 consistency in the oscilloscope readings including consultation with laboratory technicians, however the initial readings and subsequent readings for each of these tests were so peculiar that the difficult decision was made to focus this investigation on the first four readings which includes one clear result for each category of specimen. It is encouraged that further research should validate these results. Statistical tests of R^2 correlation test was utilised in order to measure the strength of correlation between the voltage outputted and cell count, as seen in figure 4 and figure 5. A normalised response curve was also graphed (Figure 6). Normalising means rescaling each sample's results so that its largest voltage counts as 100%, and every smaller reading is expressed as a percentage of that maximum. Doing this allows for a fair comparison of samples, as it removes the differences in their raw voltage sizes and instead reveals the pattern of how each one responds. In other words, rather than displaying how much voltage a sample produced, the curve shows it climbs toward its own ceiling as more force is applied. This makes the shape of each response easy to read at a glance, clearly showing both the steep rise at low forces and the plateau in voltage output at higher forces.
Figure 3: A scatter-line graph of the force applied versus the baseline-adjusted voltage, deducting original baseline voltage of each sample
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Figure 4: A scatter-line graph of the cell count versus the voltage output at 98N of force
Figure 5: A scatter-line graph of Rochelle Salt Cavity surface area versus the voltage output at low force application (98N). R^2 value of 0.9873
Figure 6: Normalised response curve of the amount of force applied to achieve a certain percentage of voltage through the 4 samples
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Discussion The results from figure 3 support the first hypothesis as there is a clear trend between cell count and voltage output, especially in the low force region (98N), as seen in figure 4. This is because there is an R^2 value of 0.825 between cell count and voltage and further seen within figure 5 where there is an R^2 value of 0.9873 between surface area of the individual cavities and voltage, meaning that there is a strong negative correlation between the amount of holes and the voltage output at 98N of force and a strong positive correlation between surface area of each individual cavity. Additionally, figure 5 depicts that a near perfect linear correlation between the amount of RS within the cavities and the voltage output and since the relationship between surface area of individual cavities and voltage has a Pearson’s correlation value of 0.006 which is smaller then 0.05, this signals a statistically significant relationship between the cavity area and sensitivity. A FEA (Finite Element Analysis, a quantitative computer simulation) was conducted in COMSOL Multiphysics to investigate the trends found in the experiment, generated by Dr Yang Yang (San Diego State University), with CAD files (figure 1) created and supplied by the author. This simulation allows for analysis of variables experimentally unmeasurable such as charge density, deformation, electric potential and stress, as seen in figure 7 and figure 8 and how these factors vary across honeycomb structures and its influence on voltage output.
The finite element analysis (FEA) in figure 7 shows the distribution of piezoelectric charge density generated within the Rochelle salt crystal regions when a compressive load of 100 N is applied to the structure. The red and yellow regions represent areas of high positive charge density, while the blue regions indicate negative charge density. Since PLA is nonpiezoelectric, charge generation occurs only within the Rochelle salt crystal infill. The results show that the increase in cell numbers leads to a decreased overall charge density (max value decreases from 82.6 to 68.7 μC/m2) as seen in the “Piezoelectric (FEA Results)”. This reduction occurs because the applied load is distributed among a larger number of piezoelectric cells, resulting in lower stress and polarization within each individual crystal. Consequently, each crystal generates a lower local charge density. The charge density is directly related to the electric displacement produced by the piezoelectric effect and is an indicator of the voltage generation capability of the material. Since the output voltage is proportional to the generated charge density for a given material and thickness, the structures with fewer holes are expected to produce higher local voltages than those with a larger number of holes. This is because the simulation assumes crystals of ideal orientation in every cavity, this discrepancy suggests a second, compounding mechanism not captured by the model which are the quality of the crystals themselves. Larger cavities allow for the growth of large, continuous single crystals with consistent orientation (Bauer et al., 2021), but smaller cavities yield fragmented, randomly oriented RS that
Figure 7: Piezoelectric FEA (Finite Element Analysis) performed by Yang Yang (San Diego State University), CAD geometries provided by the author, simulates Rochelle salt piezoelectric voltage output and charge density with 100N of force applied perpendicular to the surface
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Scientific Research in School Volume 8 Issue 1 2026 are subject to the mutual cancellation of charge due to differing orientations, as seen in the differing sizes and build up many different crystals on the beforeprocessed 61 cell sample in figure 8 below compared to several large crystals on the 7 cell sample. The crystals on the 61 cell sample has multiple different orientations of small crystals nucleating from its cells potentially limiting voltage output through cancellation due to differing polarisations throughout the composite. However, The 7-cell sample's larger crystals are likely to share a more consistent orientation, because in a larger cavity competitive growth tends to favour a few dominant crystals rather than many small, randomly nucleated ones. With more aligned polarisations, their charges add together rather than cancel, producing a higher voltage. The experimental effect therefore likely reflects two consequences of cavity size: greater force transfer throughout the structure due to a more compliant structure (captured by the FEA), and crystal continuity (not captured).
dE(ΔL/L₀), where E = Young's modulus, ΔL is the deformation, L₀ is the original sample thickness, and d is a constant, so charge density is proportional to deformation. Because d, E and the sample thickness L₀ are the same for every sample, this can be written simply as σ = C·ΔL,(where C is a constant) meaning as deformation increases total charge increases and hence voltage would increase due to the aforementioned 𝑉𝑉𝑉𝑉 =𝑘𝑘𝑘𝑘𝑘𝑘𝑘𝑘. The experimental results correspond well with the simulation results shown in Fig.7 showing that the samples with smaller number of holes and larger RS crystals had the largest deformation, charge density and hence voltage. As the hole number increased from 7 to 61, the total deformation decreased because the applied 100 N compressive load was distributed over a larger number of RSt-filled cells, increasing the effective stiffness of the composite. Since piezoelectric charge density is governed by the strain transferred into the Rochelle salt phase, the decrease in deformation indicates reduced charge density in each crystal, this would lead to a lower voltage, aligning with experimental data. However, this stiffening could also shield the crystals from fracture, with peak stresses confined to the PLA which has a yield strength of 55MPa, which is consistent with all samples surviving experimental loads of 981 N.
Figure 8: 61 Cell sample made up of many individual crystals on the left, and 7 Cell sample made up of large crystals on the right
Figure 9 shows the FEA simulation of mechanical deformation under 100N compressive loading for all of the 4 samples. The results show that the total deformation decreases from 0.178 mm to 0.024mm with the increment of number of holes and filled crystals. The output charge density is related to this deformation: charge density is given by σ =
The rate of the sample’s voltage output exposes further insight into the influence of the geometry. The max voltages at full load (981N) showed minimal variation between voltages ( 987 m- 1319mV), showing there is no significant relationship of max voltage versus geometry with a p value of 0.38 which is greater then 0.05, and as a result is consistent with figure 6. However, the difference comes from how quick the samples reach their maximum voltage. The normalised response curve in figure 6, shows that the 7 cell sample delivered 74% of its gain within the first
Figure 9: FEA (Finite Element Analysis) performed by Yang Yang (San Diego State University), CAD geometries provided by the author, simulates total deformation and Von Mises Stress with 100N of force applied perpendicular to the surface of samples.
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Scientific Research in School Volume 8 Issue 1 2026 98N of applied force, whereas the 37 and 61 cell samples only delivered 31%, requiring several hundred more newtons to reach comparable fractions of total output. The connection can therefore be made that cell count governs how rapidly the response develops but not the max size. A trade off between sensitivity and measuring range is well documented in piezoelectric force sensors. (US Patent 8,365,615, n.d.)is seen to have respond quickly to light loads but consequently saturate quickly, meaning it reaches its max voltage faster. Additionally, the sensors covering a wider force range often trade low force sensitivity (Niu et al.2022). This shows that matrix geometry can tune the trade off between voltage saturation speed and the range of sensing. Despite this, there were several limitations to qualify these findings. Most importantly each geometry was tested as a single sample, so sample to sample variation could not be properly measured. Because RS is strongly anisotropic meaning the transduction mainly occurs along the B axis (Similar to a singular axis in the 3d coordinate system (x,y,z)) ,[MH15.1] meaning its alignment influence the voltage outcome heavily. The perfect rank order makes coincidence unlikely but replication 3 times for each geometry would be required to establish a trend with statistical confidence. Measurement limitations were also faced, firstly in non zero baseline voltages varying up to 209mV between samples, but also within the 37 cell sample which exhibited an outlier like rise in between 600 – 900 newtons, possibly reflecting odd crystal deformation and orientation. Additionally, environmental conditions could have also played a role, since RS is hygroscopic, it dehydrates above 35 degrees Celsius and loses ferroelectricity above its 24 degree Celsius. Most surprisingly, following the 4 samples tested, the next samples resulted in varying degrees of poor results, such as high voltages of up to 10V with limited force applied, or no variation in voltage with 981N applied. After replacing electrodes and conductive wire and another oscilloscope, the results remained poor. Furthermore, the samples tested after the 4 were placed under the same conditions, saturation of RS solution, temperature, testing conditions and processing, signalling that there is perhaps variability within the natural growth of crystals as seen in figure 10, yet, this remains implausible as well, since the same crystals tested that achieved results failed to achieve the same results or any when repeated during debugging. Furthermore, as seen in table 3, the left sample had crystal growth concentrated more on the
surface, rather than nucleating outwards like the composite on the right in figure 10. Although sanding removes the protruding crystal, it does not equalise how much RS remains inside the cavities or how it is oriented, and these internal differences persist afterwards, contributing to the inconsistent outputs.
Figure 10: 2 different samples of the same cell count, with variations in crystal growth.
Further research The immediate priority would be replication and testing the same geometry at least 3 times to observe whether the trends hold with statistical confidence. Xray diffraction should also be utilised to determine the orientation of crystals within each cavity. Additionally, definite stable environmental conditions of below 24 degrees and remove humidity could lead to clearer results. To minimise issues, crystal growth should be contained so that perhaps a single seed was tied onto each sample with a piece of thin string so crystal growth could be more uniformly nucleated in order to achieve higher crystal orientation success. It would also be recommended to utilise either more solution in the beaker the crystals are growing in or discardable paper cups to make the extraction process from the bottom of the beaker more efficient and minimise risk of damaging the composite in the process instead of using beakers. Implication The central practical implication is that the RS composite displays that the piezoelectric response characteristics can be tuned through the geometry of the sample. This firstly lowers the cost of entry for sensor development as a consumer grade 3D printer was used (Bambu lab A1 Mini). Composites with less cells in the hexagonal shape could be utilised due to their low force sensitivity advantage, potentially used in wearable surfaces light vibration detection or footstep detection. Composites more cells could be used to preserve a more equal discrimination of force for load measurement and impact characterisations.
Conclusion This research investigated how the geometry of a 3D printed PLA honeycomb matrix influences the piezoelectric performance of the Rochelle salt
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Scientific Research in School Volume 8 Issue 1 2026 composites, by growing crystals within four hexagon matrices systematically increased cell count (7,19,37 and 61) and voltage response under compressive load of up to 981N was measured. The results demonstrate that matrix geometry influences the voltage output of the composite. Low force sensitivity decreased monotonically with every increase in the cell count where the 7 cells sample produced over 3 times the response of the 61 cells sample at 98N of force, correlating almost with the cross-sectional area of each individual cavity (r = 0.994, p = 0.006). Furthermore, less cells displayed higher sensitivity, applicable in light touch sensors while samples with more cells lead to a higher distribution of their force across a range. This highlights that eco-friendly piezoelectric sensors can be finely tuned through geometry.
Declaration of use of AI AI was utilised for generation of figure 2, to create a visual supplement for the measurement process. Additionally, AI was used within the experimental method in the form of trouble shooting, with earlier crystal growth not fully covering the sample, and concluded it was due to a lack of purity in the solution leading to crystals nucleating off the debris at the bottom of the beaker instead of the matrix. This leads to a simpler fix of re-saturating the solution by dissolving the bottom of the beaker and passing it through a filter paper. AI was further used for excels help in graphing data especially to create the normalised response curve.
Acknowledgements I would also like to thank Mr Rodriguez from the science lab faculty of Barker College who provided knowledge in operating the oscilloscope, equipment provision and electrode choice. Lastly, I also acknowledge Professor Yang from San Diego State University for generating a FEA on my cad files and guiding me to increase saturation of RS in the solution to grow better crystals on the sample.
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