Science Research Journal, Vol 1, 2021

2021

SCIENCE RESEARCH JOURNAL

Volume 1

2021

SCIENCE RESEARCH JOURNAL

Volume 1

‘Challenging the Status Quo in Science’

was our Science goal for 2021. In all our Science lessons we encourage students to be active learners; by asking questions, as questions are often more important than the answer. How best do we prepare our students for an uncertain future? How do we teach the skills needed to solve problems that we don’t even know are problems yet? What does it really mean to equip our students with 21st century skills?

The events of 2021 have more than ever shown us the importance of Science, everyone needs Science. We need our students to be scientifically literate, to think critically, to be creative, to ask questions, to solve problems, to be curious and to know that Science transforms lives. This year like no other we have been able to witness the importance and value of scientific research. Scientific research uses the scientific method to prove a hypothesis, claim or observation, it develops and promotes knowledge and information, it drives innovation and allows us all to live better and longer lives.

A number of our IB, HSC and Yearr 10 Science students this year have conducted some exceptional scientific research. They developed their own question, conducted some background information, carefully planned and conducted their valid and reliable methodology to test their research, collected their data and analysed it before drawing their conclusions. I am pleased and proud to say that our students have amazed me with the quality of their scientific reports, they have conducted some real world genuine scientific research and in the process have learnt a lot about their world and themselves as learners and become more scientifically literate in the process.

I would like to congratulate each student for their efforts and contributions and I am happy that they have learnt what it means to be a research scientist and enjoyed the challenges that this journey allowed them to experience.

I hope the Santa community enjoys reading the students’ scientific research papers as much as I did.

MOIRA DE DOMENEGHI Head of Science

Contents Investigation of the Relationship Between the pH of a Methanoic Acid Buffer Solution and the Rate Constant on the Inversion of Sucrose

Anastasia Gikas, Year 12 IB Chemistry HL......................................................................1 Investigation of the Effect of Impedance on the Power Factor of an RLC Circuit

Anastasia Gikas, Year 12 IB Physics HL........................................................................16 Comparing the viscosity of four different liquids

Rosanna Cartwright, Year 10 Science............................................................................31 The effect of sugar concentration on fermentation

Sophie Rigon, Year 10 Science.......................................................................................55 Investigating the relationship between different colour noises and the amount of deep sleep they stimulate

Maree Sialepis, Year 10 Science.....................................................................................65 An invesitgation into oxygen exposure and ethanoic acid formation in red wine

Hannah Svoboda, Year 12 IB Chemistry SL...................................................................73 Measuring Coral Replantation Efficacy

Eliose Struthers, Year 12 HSC Science Extension........................................................86 Investigating how the Height from which Water falls impacts the amount of Hydropower produced

Isabella Azzopardi, Year 12 IB Physics SL...................................................................100 Detremination of Vitamin C Mass by Titration in Different Fruits

Maya Lee, Year 12 IB Biology HL.................................................................................. 114 Investigating the Effect of Leaf Foliage Density on Radiant Heat

Rachel Frecker, Year 12 IB Biology SL.........................................................................127 Mona Talks Corona: The Automated Chatbot for Adolescents

Joanna Benedict, Sonya Jayatatillake, Victoria Kim, Lordeana Leonard, Sophia

Witting, Winners of the Biotech Future 2021 competition..........................................139

Investigation of the Relationship Between the 𝐩𝐇 of a Methanoic Acid Buffer Solution and the Rate Constant of the Inversion of Sucrose Exploration Research Question What is the relationship between the pH (2.00, 4.00, 6.00 / ± 0.01) of a methanoic acid buffer solution and the rate constant 𝑘 (mol–1 dm3 s–1) of the inversion of sucrose, calculated from the angle of rotation 𝛼(𝑡) + using the formula 𝛼(𝑡) = 𝛼0 𝑒 𝑘([C12H22O11]0−[H ]0)𝑡 ?

Introduction and Rationale Sucrose (C12H22O11), also known as saccharose, decomposes into a mixture of glucose and fructose called “invert sugar” when dissolved in an acidic solution. This reaction is known as the inversion of sucrose because it causes a rotation of the plane of polarisation; the sucrose solution is dextrorotatory (D, rotation towards the right), while the mixture of glucose and fructose is levorotatory (L, rotation towards the left) (Physical Chemistry Laboratory, 2018). As this reaction occurs, the taste of sucrose becomes increasingly sweeter and less likely to be spoiled by numerous microorganisms than other types of sweeteners (Sale & Skinner, 1922).

In addition, it prevents the crystallisation and moisture loss from the culinary products which contain it.

Monitoring the rate and extent of sucrose inversion is important in order to retain flavour and texture in processed foods such as soft drinks, sweets and other products involving sweeteners. Since the presence of a Brønsted-Lowry acid (H +) increases the rate of inversion, consideration of the acidity of a solution of sucrose is imperative in manufacturing, cooking, baking and food sciences.

When baking with my grandparents, I was exposed to various traditional desserts which involved different ways of sweetening. I found that using a different sugar or sweetener made a large difference to the overall taste, and became curious as to why, probing my inquiry into reactions with different sugars. Additionally, I was immediately intrigued when I started reading into organic chemistry, particularly stereoisomerism.

Following further research into the applications of polarimetry, I discovered that my interest in saccharide chemistry could be paired with polarimetry to design an investigation which enables me to delve into both interests. As aforementioned, this also has many industrial and individual applications which interested me, and finding an ideal acidic pH for a quick rate of reaction which is commercially favourable will allow for efficient, large-scale production.

Background Methanoic or formic acid, HCOOH, is a weak acid with dissociation constant 𝐾𝑎 = 1.77 × 10−4 at 25.0ºC (Harned & Embree, 1934). In an aqueous solution, methanoic acid dissociates according to the equation: HCOOH (aq) + H2O (l) ⇌ H3O+ (aq) + HCOO– (aq) with 𝐾𝑎 being defined as the ratio of the equilibrium concentration of aqueous products to aqueous reactants: [H3 O+ ][HCOO− ] 𝐾𝑎 = [HCOOH] A solution where a weak acid and its conjugate base (or a weak base and its conjugate acid) exist in non-zero concentrations is called a buffer solution. Buffer solutions are represented by reversible reactions, where the concentration of either the acid, base, conjugate acid or conjugate base of a weak acid may change. The distinctive characteristics of buffer solutions is that they resist pH change if small amount of an acid or a base is added. Hence, they are important in regulating natural systems such as the pH of blood, the osmotic balance of cells and the biochemical composition of various foods (Brown & Ford, 2014).

The buffer system resulting from the dissociation of HCOOH involves the acid-base pair HCOOH/HCOO–.

Hence, according to Le Chatelier’s principle, if the concentration of the conjugate base (HCOO–) is increased, the position of the equilibrium will favour the reactants. The concentration of the conjugate base may be increased by dissolving a methanoate salt, such as sodium methanoate (HCOONa).

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Following the addition of HCOONa, a new equilibrium is established, and the pH of the solution is expected to be higher as there will be a lower concentration of H3O+ (typically, the hydronium ion is written as H +).

This phenomenon is known as the common ion effect (Clark et al., 2019). The [H + ] affects the rate constant 𝑘 (and rate of reaction) in any reaction in which it is a reactant, such as the inversion of sucrose. The chemical reaction for sucrose inversion, or hydrolysis, can be seen in Figure 1.

C12H22O11 (aq) + H2O (l) + H+ (aq) → C6H12O6 (aq) + C6H12O6 (aq)

Figure 1: Reaction for the inversion of sucrose.

Sucrose, glucose and fructose are all optically active organic compounds, meaning that when plane polarised light, or light in which the electric field vector oscillates only in one direction, passes through a solution of them, they cause it to rotate by a particular angle (Anton Paar GmbH, 2021). Optical activity arises due to the presence of chiral centres or carbon atoms bonded to four different atoms or groups, making them asymmetrical; the chirality of these saccharides is shown in Figure 2 below.

sucrose

glucose

fructose

Figure 2: Chiral centres of sucrose, glucose and fructose shown in red in their structural formulas.

The device used to quantify optical activity is a polarimeter, which consists of a light source, a solution tube and two polarising filters along with a light detector. The monochromatic wave emitted is plane polarised, travelling in one orientation, and is then passed through the solution in the polarimeter’s tube (Brown & Ford, 2014). The plane is rotated by the chiral mixture and an analyser is used to determine rotation, then electrically processed by the photocell and displayed on the screen.

The polarimeter measures the angle of rotation 𝛼 of the mixture in the tube, also known as the observed rotation. This is done by displaying the change in angle of the plane of polarised light as it passes through the sample in the solution tube. The angle of rotation 𝛼 depends on the following factors: length of the polarimeter 𝑙, concentration of the solution in the tube 𝑐 and the specific rotation [𝛼]𝛵𝜆 . Specific rotation ([𝛼]𝛵𝜆 ) is a constant physical property of an optically-active compound that shows the magnitude and direction of the light’s plane rotation at a particular temperature 𝑇 and for a specific wavelength 𝜆. If 𝑇, 𝜆 and 𝑙 are kept constant, the angle of rotation will be directly proportional to the concentration of the sample in the tube, as shown below: 𝛼 = 𝑙𝑐 [𝛼]𝛵𝜆 𝛼∝𝑐 In the inversion of sucrose, the rate law can be written as the product of the reactants’ concentrations raised to the power of their reaction order, with 𝑘 ′ as the rate constant: rate = 𝑘′[C12 H22 O11 ]𝑚 [H2 O]𝑛 [H + ]𝑝 Assuming that the concentration of water is excessively large in relation to the other reactants, it does not significantly change over the course of the reaction. It is therefore considered a constant. Furthermore, assuming that sucrose and H + are first order reactants (making the overall reaction second order), and this is the only, rate-determining step, the differential form of the rate law may be simplified to: rate = 𝑘[C12 H22O11 ][H + ] where 𝑘 is the rate constant with the given assumptions.

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However, in a reaction where the concentrations of the two first order reactants are unequal (such as the second order reaction for sucrose inversion in this experiment), the integrated rate law must be utilised instead. The integrated rate law is an expression derived from calculus and considers the changes in reactant concentration over time. It can be expressed as (Boundless Learning, n.d.): ln (

[C12 H22O11 ][H+ ]0 ) = 𝑘([C12 H22 O11 ]0 − [H + ]0)𝑡 [H+ ][C12 H22O11 ]0

where 𝑡 is the time after the reaction begins (in seconds), [C12 H22O11 ]0 is the initial concentration of sucrose, and [H + ]0 is the initial concentration of hydrogen ions.

In this experiment, because the concentration of H + is altered by changing the pH of the methanoic acid solution, the reactant concentrations are unequal and hence it is not possible to calculate the rate constant from the differential form of the rate law; thus, the integrated rate law will be used.

In this second order reaction, it is impossible to isolate the concentration of a single reactant, as they both change in parallel. Rather, the ratio of the concentrations is considered.

[C12 H22 O11 ] Let = 𝑐(𝑡) [H + ] [H + ]0 ln 𝑐(𝑡) + ln = 𝑘([C12 H22 O11]0 − [H + ]0 )𝑡 [C12 H22 O11 ]0 [H + ]0 ln 𝑐(𝑡) = 𝑘([C12 H22O11 ]0 − [H + ]0)𝑡 − ln [C12 H22 O11]0 [H+ ]0 12 H22 O11 ]0

𝑘([C12 H22 O11 ]0 −[H+ ]0 )𝑡−ln[C

𝑐(𝑡) =

𝑒

𝑐(𝑡) = 𝑒 + 𝑒 𝑘([C12H22 O11]0−[H ]0)𝑡 [C12 H22 O11 ]0 𝑘([C H O ] −[H+] )𝑡 12 22 11 0 0 = = 𝑒 [H+ ]0 [H + ]0 [H + ]0 ln[C 𝑒 12 H22O11]0 [C12 H22 O11 ]0

𝑘([C12 H22 O11 ]0 −[H+ ]0 )𝑡

As aforementioned, the angle of rotation 𝛼 is directly proportional to the concentration 𝑐 of a substance in a solution (Bora et al., 2018). It follows that if the concentration of the optically active sample changes over time, the angle of rotation will also be time-varying. Therefore, the angle of rotation and concentration of the sample in the tube will be treated as functions of time, 𝛼(𝑡) and 𝑐(𝑡) respectively.

The concentrations of multiple optically active reactants and/or products are changing in the sample in the polarimeter tube, so the measured angle of rotation is the sum of the mole fraction of each species. For the inversion of sucrose, the measured angle of rotation over time can be calculated using: 𝑐𝑔𝑙𝑢𝑐𝑜𝑠𝑒 (𝑡) 𝑐𝑓𝑟𝑢𝑐𝑡𝑜𝑠𝑒 (𝑡) 𝑐𝑠𝑢𝑐𝑟𝑜𝑠𝑒 (𝑡) 𝛼(𝑡) = × 𝛼𝑠𝑢𝑐𝑟𝑜𝑠𝑒 + × 𝛼𝑔𝑙𝑢𝑐𝑜𝑠𝑒 + × 𝛼𝑓𝑟𝑢𝑐𝑡𝑜𝑠𝑒 𝑐𝑡𝑜𝑡𝑎𝑙 (𝑡) 𝑐𝑡𝑜𝑡𝑎𝑙 (𝑡) 𝑐𝑡𝑜𝑡𝑎𝑙 (𝑡) As sucrose is being hydrolysed into glucose and fructose, the angle of rotation should gradually change.

Initially, the (overall) angle of rotation should be equal to the angle of rotation of sucrose, as there are no products in the mixture. As the inversion progresses, more glucose and fructose are produced in a molar ratio of 1:1. Considering the initial angle of rotation 𝛼0, and measuring how 𝛼(𝑡) changes over time as more sucrose is being hydrolysed, it is possible to determine the change in concentration ratio of the reactants 𝑐(𝑡), where 𝛾 acts as the proportionality constant. 𝛼(𝑡) ∝ 𝑐(𝑡) [C12 H22 O11]0 Let 𝛼0 = 𝛾 [H + ]0 𝒌([𝐂𝟏𝟐 𝐇𝟐𝟐 𝐎𝟏𝟏 ]𝟎 −[𝐇+ ]𝟎 )𝒕 𝜶(𝒕) = 𝜶𝟎 𝒆 The above (bolded) function will thus be used to determine the value of the rate constant 𝑘. The values of 𝛼0 and 𝛼(𝑡) will be measured using a polarimeter, whilst the [C12 H22O11 ]0 value is constant in all trials. The [H + ]0 is reliant on the pH of the buffer solution, and will have a different value which will be substituted into the equation at pH 2.00, 4.00 and 6.00.

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Hypothesis Increasing the pH of a 0.570 mol dm–3 methanoic acid buffer solution by adding HCOONa will increase the magnitude of the rate constant 𝑘, yielding a less negative value. This is expected due to the common ion effect, where increasing the concentration of a product leads to an equilibrium position favouring the reactants. This results in a lower [H + ] and a higher pH. As the rate constant of the inversion of sucrose is increased in an acidic environment, higher pH should lead to a lower rate constant. The angle of rotation will be used to determine the rate of the inversion reaction as both sucrose and the products are optically active compounds.

Methodology Variables Table 1 details the independent variable in this experiment.

Table 1: Independent variable.

Independent variable

Variation method

Intervals (± 0.01)

[𝐇 + ]𝟎

pH of the methanoic acid solution

The pH is changed by altering the concentration of HCOO– in the reaction mixture, which affects the initial concentration of H + according to Le Chatelier’s principle. The [H + ]0 is determined from [H + ]0 = 10−pH, as seen in Appendix A.

2.00

0.0100

4.00

1.00 × 10−4

6.00

1.00 × 10−6

The selection of methanoic acid is justified for multiple reasons. Firstly, it is a weak acid; strong acids have a constant pH independent of the conjugate base concentration, given they completely dissociate. Additionally, HCOOH is a liquid at room temperature (25.0ºC) and it inexpensive, readily available and relatively safe compared to more corrosive acids.

The dependent variable is elaborated on in Table 2.

Table 2: Dependent variable.

Dependent variable

Measurement method

Justification

Units

Rate constant of the inversion of sucrose

Calculated from curve fitting; the equation + 𝛼(𝑡) = 𝛼0 𝑒 𝑘([C12H22O11]0−[H ]0)𝑡 will be used with 𝑡, 𝛼0 and 𝛼(𝑡) being the experimental measurements. This process will be repeated for the three pH levels.

By observing how the angle of rotation of the mixture changes over time, the rate of change of sucrose concentration can be determined. The pH affects the magnitude of the rate constant in the inversion.

mol–1 dm3 s–1 as calculated in the data processing

Table 3 explains the controlled variables.

Table 3: Controlled variables.

Controlled variable Stirring process.

Same polarimeter, electronic balance and pH probe used.

Temperature of the solution and surroundings.

Control method Be consistent in the vigorousness and duration of stirring the solutions for all trials.

Use the Atago Polax D polarimeter, A&D Mercury FX-400 electronic balance and EZDO 7200 pH probe.

All trials will be carried out in the same, air-conditioned laboratory at approximately 25.0ºC. All solutions used should be kept at the same location. The polarimeter ensures a thermostable environment in the tube during the reaction.

Justification This ensures that the concentrations are uniform throughout the solution.

Errors in calibration or faulty readings may exacerbate systematic errors.

The temperature 𝑇 affects the specific rotation, which in turn affects the angle of rotation, as seen in: 𝛼 = 𝑙𝑐 [𝛼]𝛵𝜆 .

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Polarimeter tube length.

The length of the tube 𝑙 affects the angle of rotation as per: 𝛼 = 𝑙𝑐 [𝛼]𝛵𝜆 .

The wavelength 𝜆 affects the specific Set the polarimeter’s light source to rotation, which in turn affects the angle of emit monochromatic light of 589 nm. rotation, as seen in: 𝛼 = 𝑙𝑐 [𝛼]𝛵𝜆 .

Different acids have different 𝐾𝑎 values, HCOOH is used in all trials. which would affect the rate of reaction.

The initial concentrations affect the angle of rotation of the mixture, as per: Prepare the solutions so that equal 𝑘([C12 H22 O11 ]0 −[H+ ]0 )𝑡 𝛼(𝑡) = 𝛼 𝑒 0 concentrations are used in each trial, This ensures that only the concentration of as detailed in the method.

H +, and hence the independent variable pH, is affecting the rate constant.

Use the same polarimeter tube of length 100 mm in all trials.

Wavelength of light.

Type of acid used.

Initial concentration of C12H22O11 and HCOOH.

Risk Assessment Table 4 details hazards, risks posed and mitigations to establish a safe experimental environment.

Table 4: Risk assessment.

Hazard

Risk Posed

Use of a glass beaker, measuring cylinders and a stirring rod.

Given that glass is very fragile, if dropped on any hard surface, it may shatter and cause punctures, cuts, and infections at the wound.

HCOOH is corrosive.

An acid spill may cause skin burns, eye irritation and/or damage to surroundings.

Dropping the polarimeter on the ground or on someone’s foot.

The polarimeter is heavy and may result in injuries if it falls on someone. It can also fall on the ground where someone may trip over it, or may cause structural damage.

Mitigation Confirm, prior to beginning the experiment, that none of the glassware is severely scratched or cracked. Wear a laboratory coat, safety glasses, latex gloves and enclosed shoes for added protection.

Handle all glassware with care. Have a first aid kit nearby. If the beakers or stirring rods happen to break, use a dustpan to remove shattered pieces.

Wear a laboratory coat, safety glasses, gloves and enclosed shoes for added protection. Handle acid with care. Ensure that a spill neutralisation reagent is available at the laboratory. Solutions may be disposed of ethically by simply pouring down the sink (NSW Education Standards Authority, n.d.).

Place the polarimeter away from the edge of the laboratory bench and ensure that only the experimenter is near the device. Neatly collect any loose hanging wires to mitigate trip hazard.

Equipment The required components for this experiment are shown in Table 5.

Table 5: Apparatus list.

Quantity 1 1 1 3 3 15 strips 1 1 1 1

Apparatus Polarimeter (Atago Polax D) 5 dm3 beaker 50 cm3 measuring cylinder 25 cm3 measuring cylinder 3 cm3 pipettes Universal indicator paper pH probe (EZDO 7200) iPhone 11 Pro Max stopwatch Glass stirring rod Three decimal place electronic balance (A&D Mercury FX-400)

Uncertainty ± 0.5º ± 0.0015 dm3 ± 0.5 cm3 ± 0.05 cm3 ± 0.01 cm3 – ± 0.01 ± 0.01 s – ± 0.001 g 5

Reagents The reagents used in this experiment are shown in Table 6.

Table 6: Reagent list.

Quantity 5.130 g 100 cm3 9.905 g 4.04 dm3

Reagent Pure sucrose Methanoic acid 90% w/w, 23.6 mol dm–3 stock (The Nest Group, Inc., 2016) Sodium methanoate powder ≥ 99% Distilled water

Method The pH calculations are shown in Appendix A, and all other relevant calculations used for the stoichiometric relationships in the method can be seen in Appendix B. 1. Calibrate the polarimeter according to the instructions.

2. Prepare a 4.14 dm3 solution of 0.570 mol dm–3 HCOOH by adding 4.04 dm3 of distilled water to the stock 23.6 mol dm–3 solution in the 5 dm3 beaker. To attain a precise volume, transfer 4.00 dm3 of distilled water via the large 5 dm3 beaker, and add 0.04 dm3 of distilled water using a 50 cm3 measuring cylinder.

Part A: pH = 2.00 3. Using a pipette, transfer 10 cm3 of the diluted HCOOH solution into a 25 cm3 measuring cylinder.

4. Add 0.342 g of C12H22O11 into the 25 cm3 measuring cylinder. Stir thoroughly using a stirring rod until fully dissolved.

5. Use 1 strip of the universal indicator paper to ensure that the pH has reached 2.00 (light red). Confirm the pH value using the pH probe.

6. Turn on the sodium lamp of the polarimeter and transfer the mixture into the polarimeter’s observation tube.

7. Record the angle of rotation as soon as the tube is inserted by adjusting the eyepiece lens until the field of view attains a uniform colour (yellow), and then every minute for 20 minutes.

8. Repeat steps 3-7 for four more trials.

Part B: pH = 4.00 9. Using a pipette, transfer 10 cm3 of the diluted HCOOH solution into another 25 cm3 measuring cylinder.

10. Add 0.476 g of HCOONa into the 25 cm3 measuring cylinder from step 9. Stir thoroughly until fully dissolved.

11. Repeat steps 5-7, instead ensuring that the pH has reached 4.00 (light orange).

12. Repeat steps 9-11 for four more trials.

Part C: pH = 6.00 13. Using a pipette, transfer 10 cm3 of the diluted HCOOH solution into another 25 cm3 measuring cylinder.

14. Add 1.505 g of HCOONa into the 25 cm3 measuring cylinder from step 13. Stir thoroughly until fully dissolved.

15. Repeat steps 5-7, instead ensuring that the pH has reached 6.00 (light green).

16. Repeat steps 9-11 for four more trials.

Results and Analysis Raw Data Qualitative Data For all trials at all pH levels, a strip of universal indicator paper was used to qualitatively confirm that the buffer solution had the predicted pH. It was observed that for the pH = 2.00 solutions, the indicator paper was light red; for the pH = 4.00 solutions, the indicator paper was light orange, and for the pH = 6.00 solutions, the indicator paper was light green. A pH probe was used to confirm the acidity levels of the HCOOH solutions, as well as to quantify the pH uncertainty. Furthermore, when the sucrose was added, the solution initially appeared cloudy, but after stirring, it became transparent. No further observations could be made when the reaction was in the polarimeter due to its opaque tube.

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Quantitative Data The collected raw, quantitative data is displayed in Table 7.

Table 7: Quantitative raw data.

Time 𝒕 (±0.01s) 0.00 60.00 120.00 180.00 240.00 300.00 360.00 420.00 480.00 540.00 600.00 660.00 720.00 780.00 840.00 900.00 960.00 1020.00 1080.00 1140.00 1200.00

T1 35.5 29.5 24.5 19.5 15.5 12.0 9.5 7.5 6.0 5.0 4.0 2.5 1.5 1.0 0.0 –0.5 –0.5 –1.5 –2.0 –2.5 –3.0

Instantaneous angle of rotation 𝜶(𝒕) / ±0.5º 𝐩𝐇 = 2.00 / ± 0.01 𝐩𝐇 = 4.00 / ± 0.01 𝐩𝐇 = 6.00 / ± 0.01 T2 T3 T4 T5 T1 T2 T3 T4 T5 T1 T2 T3 T4 37.0 36.0 35.5 35.5 35.5 36.0 35.5 36.0 37.0 35.5 35.0 35.0 36.0 30.5 30.0 29.5 29.0 31.0 31.5 31.0 31.0 31.0 32.0 32.5 32.0 32.5 25.5 25.0 25.0 25.0 28.0 28.5 28.0 28.5 29.0 29.0 29.5 28.0 29.5 20.0 20.0 19.5 19.5 25.0 25.0 25.0 25.0 25.0 27.0 27.5 27.0 26.5 15.5 16.0 16.0 15.5 22.5 22.0 22.5 22.0 22.5 24.0 24.5 24.0 23.5 12.5 12.0 12.0 12.0 18.5 19.0 19.0 19.0 18.5 22.0 22.5 22.5 22.0 10.0 9.5 9.0 10.0 17.0 17.5 17.0 17.0 17.0 20.0 20.0 20.5 20.0 7.5 7.0 6.5 7.0 15.0 14.5 15.0 15.0 14.5 18.5 18.5 19.0 18.0 6.0 5.5 5.5 6.0 12.5 13.0 13.0 13.0 12.5 16.0 16.0 17.0 16.5 5.0 5.0 4.5 5.0 11.0 11.0 11.5 11.0 11.5 14.5 14.5 15.5 15.0 4.0 4.0 4.0 4.0 9.5 9.5 9.5 9.0 9.0 13.5 13.5 14.0 13.5 3.0 3.0 3.0 2.5 8.0 7.5 8.0 8.0 7.5 12.5 12.5 12.5 12.0 1.5 2.0 1.5 1.5 7.0 6.5 6.5 7.0 6.0 11.5 11.0 11.0 11.0 1.0 1.0 1.0 0.5 6.5 6.0 6.0 6.0 5.0 10.5 10.0 10.0 10.0 0.0 0.5 0.0 0.0 6.0 5.5 5.5 5.0 5.0 9.5 9.0 9.0 9.0 –0.5 0.0 0.0 –0.5 5.5 5.0 4.5 4.5 4.5 8.0 8.5 8.5 8.5 –1.0 –1.0 –1.0 –1.0 5.0 4.5 4.0 4.5 4.0 7.5 7.0 7.0 7.5 –1.5 –1.0 –1.0 –1.5 4.0 4.0 3.5 4.0 3.5 7.5 6.5 6.5 7.0 –2.0 –1.5 –1.5 –2.0 3.5 3.5 3.0 3.5 3.0 7.0 6.0 6.0 6.5 –2.5 –2.5 –2.0 –3.0 3.5 3.0 3.0 3.0 3.0 6.5 6.0 6.0 6.0 –3.0 –3.0 –3.0 –3.5 3.0 3.0 3.0 3.0 3.0 6.0 5.5 5.5 6.0

T5 37.0 32.0 29.0 27.0 24.0 22.0 20.0 18.5 17.0 15.5 14.0 12.5 11.5 10.5 9.5 8.5 7.5 7.0 6.5 6.0 5.5

Data Processing The mean angle of rotation for each pH level and each point in the time series was calculated as follows: Mean angle of rotation for pH = 2.00 at 𝑡 = 0.00 s: 35.5 + 37.0 + 36.0 + 35.5 + 35.5 0.5 + 0.5 + 0.5 + 0.5 + 0.5 𝑎(0) = 𝛼0 = uncertainty = 5 5 𝑎(0) = 𝛼0 = 35.9° = ±0.5° After averaging the instantaneous angles of rotation, the rate constant 𝑘 was calculated for each time step by rearranging the bolded formula derived in the Background: + 𝜶(𝒕) = 𝜶𝟎 𝒆𝒌([𝐂𝟏𝟐𝐇𝟐𝟐𝐎𝟏𝟏]𝟎−[𝐇 ]𝟎)𝒕 𝛼(𝑡) ln ( 𝛼 ) 0 𝑘= ([C12 H22O11 ]0 − [H + ]0)𝑡 For example, see below for a sample calculation of 𝑘: Rate constant for pH = 2.00 at 𝑡 = 60.00 s: 𝛼(𝑡) 29.7 ln ( 𝛼 ) |ln ( )| 35.9 0 𝑘= = ([C11 H22 O11]0 − [H + ]0)𝑡 (0.1 − 0.01) × 60 ≈ −0.0351 mol−1 dm3 s−1

The units for 𝑘 are: dimensionless units for 𝑘 = mol dm−3 s −1 3 −1 = mol dm s

Sample calculations for the uncertainty of the initial concentrations of sucrose and H +, and the rate constant 𝑘, can be found in Appendix C. 7

Table 8 contains all processed data obtained from the quantitative raw data.

Table 8: Processed data. Red value indicates an outlier.

Time 𝒕/ ±0.01 s 0.00 60.00 120.00 180.00 240.00 300.00 360.00 420.00 480.00 540.00 600.00 660.00 720.00 780.00 840.00 900.00 960.00 1020.00 1080.00 1140.00 1200.00 Average

𝐩𝐇 = 2.00 / ± 0.01 Mean angle of rotation Rate constant 𝒌 / 𝜶(𝒕) / ± 0.5º mol–1 dm3 s–1 35.9 – 29.7 –0.0351 ± 0.0058 25.0 –0.0335 ± 0.0032 –0.0370 ± 0.0025 19.7 –0.0383 ± 0.0022 15.7 12.1 –0.0403 ± 0.0022 –0.0407 ± 0.0021 9.6 –0.0429 ± 0.0023 7.1 5.8 –0.0422 ± 0.0024 4.9 –0.0410 ± 0.0024 –0.0406 ± 0.0026 4.0 –0.0429 ± 0.0032 2.8 1.6 –0.0480 ± 0.0046 0.9 –0.0525 ± 0.0067 –0.0778 ± 0.0242 0.1 –0.3 – –0.9 – –1.3 – –1.8 – –2.5 – –3.1 – –0.0412 ± 0.0033

𝐩𝐇 = 4.00 / ± 0.01 Mean angle of rotation Rate constant 𝒌 / 𝜶(𝒕) / ± 0.5º mol–1 dm3 s–1 36.0 – 31.1 –0.0239 ± 0.0050 28.4 –0.0195 ± 0.0027 –0.0201 ± 0.0019 25.0 –0.0199 ± 0.0016 22.3 18.8 –0.0216 ± 0.0014 –0.0206 ± 0.0013 17.1 –0.0211 ± 0.0012 14.8 12.8 –0.0215 ± 0.0012 11.2 –0.0216 ± 0.0011 –0.0225 ± 0.0012 9.3 –0.0232 ± 0.0012 7.8 6.6 –0.0235 ± 0.0013 5.9 –0.0232 ± 0.0013 –0.0226 ± 0.0013 5.4 –0.0224 ± 0.0013 4.8 4.4 –0.0219 ± 0.0013 –0.0220 ± 0.0014 3.8 –0.0221 ± 0.0015 3.3 3.1 –0.0215 ± 0.0015 3.0 –0.0207 ± 0.0015 –0.0218± 0.0016

𝐩𝐇 = 6.00 / ± 0.01 Mean angle of rotation Rate constant 𝒌 / 𝜶(𝒕) / ± 0.5º mol–1 dm3 s–1 35.7 – 32.2 –0.0181 ± 0.0052 29.0 –0.0178± 0.0027 –0.0158 ± 0.0019 27.0 –0.0168 ± 0.0015 24.0 22.2 –0.0160 ± 0.0013 –0.0161 ± 0.0011 20.1 –0.0158 ± 0.0010 18.5 16.5 –0.0162 ± 0.0010 15.0 –0.0162 ± 0.0009 –0.0161 ± 0.0009 13.7 –0.0161 ± 0.0009 12.4 11.2 –0.0162 ± 0.0009 10.2 –0.0161 ± 0.0008 –0.0162 ± 0.0009 9.2 –0.0161 ± 0.0009 8.4 7.3 –0.0166 ± 0.0009 –0.0162 ± 0.0009 6.9 –0.0160 ± 0.0009 6.4 6.1 –0.0155 ± 0.0009 5.7 –0.0153 ± 0.0009 –0.0163 ± 0.0013

The rate constant for pH = 2.00 trials from times 900.00 to 1200.00 s was impossible to calculate as it involved a natural logarithm of a negative value (𝛼(𝑡)).

When 𝑡 = 0, the denominator becomes 0 and the calculation for 𝑘 again becomes invalid. The erroneous value of 𝑘 for 840.00 s in the pH = 2.00 mean angle of rotation is a result of the mathematical nature of the logarithmic function; as the angle of rotation approaches 0, the natural logarithm also approaches 0, which mathematically approximates to negative infinity. The value in red was hence considered an outlier and excluded from the average 𝑘 calculation. For pH = 2.00: −0.0351 − 0.0335 − ⋯ − 0.0525 0.0058 + 0.0032 + ⋯ + 0.0067 Average rate constant 𝑘 = = −0.0412 mol−1 dm3 s−1 uncertainty = = ±0.0033 13 13 8

Figure 3 illustrates the resulting angle of rotation curves from the processed data in Table 8.

Figure 3: Mean angle of rotation of the reaction mixture as a function of time for pH=2.00, 4.00 and 6.00.

It is evident in Figure 3 that regardless of the pH, the rotation angle of the mixture decays exponentially over time. This was expected as the angle of rotation is given by an exponential function, and all values of 𝑘 are negative in the equation below: + 𝜶(𝒕) = 𝜶𝟎 𝒆𝒌([𝐂𝟏𝟐𝐇𝟐𝟐𝐎𝟏𝟏]𝟎−[𝐇 ]𝟎)𝒕 Comparing the rate constants for the three pH levels, it was found that the pH = 6.00 trials had the least negative value of 𝑘, followed by the pH = 4.00 trials and ultimately the pH = 2.00 trials had the most negative constant. This trend is evidenced as the fastest change of the mean angle of rotation for pH = 2.00 and the slowest change for pH = 6.00. As all experiments started from approximately identical initial rotation angles, and the inversion of sucrose reaction involved the same amounts of reactants, it can be extrapolated that all curves should approach the same horizontal asymptote as 𝑡 → ∞; due to time constraints, this investigation only took observations up to 𝑡 = 1200.00 s.

Conclusion and Evaluation Conclusion In conclusion, the investigation hypothesis was that increasing the pH, by increasing the concentration of HCOONa, would result in a less negative rate constant 𝑘 for the inversion of sucrose. As evidenced in Figure 3, the reaction of sucrose in a pH = 2.00 buffer solution yielded the most negative rate constant, which in turn indicates the fastest rate of reaction. In contrast, the reaction of sucrose in a pH = 6.00 methanoic acid buffer solution yielded the least negative value of 𝑘, corresponding to the slowest rate of reaction. Hence, the experimental evidence suggests that the original hypothesis may be accepted. The pH = 2.00 buffer solution consisted of 0.570 mol dm–3 methanoic acid. Adding HCOONa to the pH = 2.00 buffer solution disturbs the position of the chemical equilibrium, as the concentration of the conjugate base (HCOO –) increases, and according to Le Chatelier’s principle, the position of the new equilibrium will be established to favour the reactants. By carefully calculating the amount of conjugate base that must be added, solutions with pH = 4.00 and 6.00 were obtained, both of which are less acidic than the original solution due to the common ion effect.

The concentration of the acid present directly affects the rate of reaction of the inversion of sucrose, as H + is 9

amongst the reactants in this reaction. As the hydrolysis reaction progresses, the dextrorotatory sucrose is turned into a levorotatory solution of glucose and fructose. In order to test the hypothesis, it was necessary to gauge the concentration changes over time for the three pH levels; thus, the angle of rotation was recorded over 20 minutes. As the reaction progressed, the angle observed in the polarimeter would gradually become more negative, as more products are generated.

It is common to use polarimetry in order to study reaction kinetics, including the rate of reaction for reactions where the reactant and product solutions demonstrate optical activity. The inversion of sucrose is in fact a well-studied reaction with polarimetry (Wienen & Shallenberger, 1988), (Makwakwa, 2014). Accurately modelling the rate of reaction of sucrose inversion is critical in the food industry as establishing the degree of hydrolysis affects the sweetness of sugar and the potential formation of syrup crystals (Sale & Skinner, 1922).

Evaluation Determining the validity of this investigation involves assessing the choice of the independent (pH), dependent (rate constant) and controlled variables, as well as the experimental methodology. Using pH as the independent variable is a valid choice as its magnitude affects the [H + ], which in turn affects the rate constant.

The chosen intervals of pH should produce significantly different values of 𝑘, and hence they were designed to be sufficiently apart from one another. The dependent variable (rate constant) is an equally valid choice as it allows to directly test the hypothesis. Since the rate constant depends on the rate of change of the concentrations of all species in the inversion reaction, and concentrations are determinant of the rotation angle of the solution, measuring the angle of rotation over time is a valid methodological process. Furthermore, for the experiment to be valid, appropriate measures were taken to eliminate the effect of the controlled variables on the rate constant, as shown in Table 3.

The assumptions for calculating 𝑘 with the integrated rate law were that the [H2 O] remains constant, and that the [C12 H22 O11 ] and [H + ] are considered first order and are unequal. In the context of this experiment, these are valid and necessary assumptions as otherwise, the mathematical complexity would be extremely high.

Additionally, when preparing the mixture of sucrose and the buffer, it was assumed that the inversion process does not start until the mixture is inserted into the polarimeter, and that the H + runs out some time after the measurements have stopped. Finally, the angle of rotation of the optically active mixture was calculated assuming that the products of the inversion reaction consisted of a single glucose and a single fructose stereoisomer, produced in a 1:1 molar ratio, which was an essential if not valid assumption.

The reliability of the results was high as the angles of rotation observed for different trials of the same pH were within a narrow range, as detailed in Table 7. For each of the five trials, a pH probe was used to confirm the [H + ] was constant, rendering the experiment reliable.

The glassware used for volumetric preparation and sampling of the reactants was as precise as possible in the context of a school laboratory. The electronic balance and pH meter were both highly precise, digital instruments, that could measure masses of 0.001 g and pH of 0.01 respectively. On the other hand, the polarimeter’s precision was lower (± 0.5°). This issue becomes more relevant as the sucrose inversion progresses and the rotation angle changes by a progressively smaller magnitude. Due to the cost and sophisticated nature of polarimeters, this experiment relied on the only polarimeter model that was available in this context. Regardless, the value of 𝑘 was calculated with a precision of 4 decimal places to illustrate its variability over time. Whilst the polarimeter precision could be higher, the overall precision is acceptable.

The results of an accurate experiment would be closely matching the theoretical results; however, the rate constant may not be obtained by simply using a deterministic formula and hence it is challenging to directly compare experimental and theoretical values. As this experiment is very original, the percentage error cannot be calculated, but the accuracy may still be assessed with the help of secondary sources. Similar experiments indicated that the rate constant of the sucrose inversion lied within the experimental range of –0.0100 to –0.0450 mol–1 dm3 s–1 and the reaction in a more acidic environment demonstrated a more negative 𝑘 (Jenkins & Williams, 1996). In light of the agreement of the experimental and secondary trends, the accuracy can be deemed fairly high.

10

Table 9 details all sources of error which may have affected the investigation results.

Table 9: Summary of error sources, their significance, and suggested improvements.

Source of error

Significance and impact of error Improvement to reduce error Systematic errors (which may affect accuracy) Obtain primary data for the orders If the real order of reactants is not 1, of the reactants by designing a Assuming that the order of the formula used to calculate 𝛼(𝑡) reaction kinetics experiment. sucrose and H + used to would change. The concentrations Conduct more comprehensive calculate the rate constant is 1 used to prepare the solutions of the secondary research to seek for incremental pH would also be for each reactant. more accurate order values and different. Highly significant. rate law formulas.

Delayed reaction time All values of 𝑡 in the experimental Add the necessary extra steps to between the point when the data were offset by a constant delay, the method that would allow reaction begins and when the which in turn would have an impact measuring the delay time and first polarimeter measurement on the parameters of the exponential using that as an offset added to the is taken (ie. for transferring decay used in finding 𝑘: recorded time intervals. Estimate the solution into the 𝒌([𝐂𝟏𝟐 𝐇𝟐𝟐 𝐎𝟏𝟏 ]𝟎 −[𝐇+ ]𝟎 )𝒕 the value of 𝛼0 by extrapolating 𝜶(𝒕) = 𝜶𝟎 𝒆 polarimeter and taking the the graph.

Relatively significant. initial measurement).

If H + is no longer present as a For every trial, ensure that the + The H may have run out reactant in the sucrose inversion, the sucrose concentration is equal to rate of the reaction would slow the H + concentration, so that both within the 1200.00 seconds that results were being taken. down, affecting the value and reactants are consumed at the accuracy of 𝑘. Relatively significant. same rate.

Obtain a more accurate prediction The actual types and ratios of Assuming that the glucose about the types and ratios of stereoisomers in the products affect and fructose in the products stereoisomers under specific horizontal asymptote on the 𝛼(𝑡) each consist of only one experimental conditions. Adjust stereoisomer, in the molar graph. This affects the value and the method so that the prescribed ratio 1:1. accuracy of 𝑘. Relatively significant. reaction conditions are met.

Random errors (which may affect precision) Observer error when handling the polarimeter to record the value of 𝛼(𝑡) at a specific Qualitatively assessing the colour by point in time. The manual eye is inherently random, which may Use a digital or automatic operation of the polarimeter yield erroneous values for the angle polarimeter with the capacity to induces random errors when of rotation. Highly significant. display on a screen and potentially discerning the point where extract the 𝛼(𝑡) values to a the field of view is uniformly spreadsheet. This would eliminate coloured yellow. the human error in determining The 𝛼(𝑡) values used to plot and the angle of rotation and lead to Inconsistent timing when analyse the data may have been more accurate and precise results. recording the angle of taken too early or too late in relation rotation as the manual to the expected time. Relatively process takes a few (2-5) insignificant; 𝛼(𝑡) does not notably seconds to complete. change within the delay duration.

The method relied on using specific Use more precise apparatus to masses and concentrations of various Preparation of reactants and prepare the reactants and use chemical species. Any random the prescribed concentration volumetric methods (such as deviation may alter results. value may be slightly off. titration) to confirm the exact Relatively insignificant due to the prescribed concentrations. high precision of the apparatus.

11

Investigation Extension and Improvements Given enough time, the experimental process could have been altered to test multiple pH levels in shorter intervals (for instance pH = 1.00, 1.50, 2.00, etc.). This would improve the evaluation of the hypothesis that the angle of rotation is affected by the pH levels. Furthermore, the modifications proposed in Table 9 could be implemented without changing the aim and overall direction of the experiment.

A possible extension of the experiment would be to introduce a range of new qualitative and quantitative experimental considerations or parameters, such as texture and taste of the invert sugar and the costs of preparation in an industrial setting. This would ensure that a more practical interpretation of the results could be made, as the angle of rotation itself has little relevance in the industrial preparation of food. Rather, the angle of rotation could be paired with these additional factors as an indicator of the optimal pH and time (of inversion) to produce the sweetest, commercially favourable invert sugar. This would require an extended methodology with further steps designed to determine the effects of changing pH on the effectiveness of the invert sugar as a sweetener and preservative, as well as the usefulness of polarimetry in commercial applications.

Reference List Anton Paar GmbH. (2021). Sugar inversion. Anton Paar. https://wiki.anton-paar.com/en/sugarinversion/#why-worry-about-sugar-inversion Bora, K. J., Goswami, D., Upadhyay, V., & Kalita, D. (2018). Optical method for measuring optical rotation angle and refractive index of chiral solution. Bulletin of Physics Projects, 3, 27–31. https://www.bbcphysics.in/images/pdfs/BPP-Vol-3_pdf/article-5.pdf Boundless Learning. (n.d.). The Integrated Rate Law. Lumen. Retrieved February 6, 2021, from https://courses.lumenlearning.com/introchem/chapter/the-integrated-rate-law/ Brown, C., & Ford, M. (2014). Higher Level Chemistry (2nd ed.). Pearson Education.

Clark, J., Danai, M., Tung, E., & Chieh, C. (2019). Common Ion Effect. Chemistry LibreTexts. https://chem.libretexts.org/Bookshelves/Physical_and_Theoretical_Chemistry_Textbook_Maps/Sup plemental_Modules_(Physical_and_Theoretical_Chemistry)/Equilibria/Solubilty/Common_Ion_Effe ct Harned, H. S., & Embree, N. D. (1934). The Ionization Constant of Formic Acid from 0 to 60°. Journal of the American Chemical Society, 56(5), 1042–1044. https://doi.org/10.1021/ja01320a010 Jenkins, B., & Williams, J. (1996, November 6). Inversion of sucrose; kinetics of a pseudo-first order reaction determined by polarimetry differential equations. http://people.uncw.edu/lugo/MCP/DIFF_EQ/deproj/sucrose/sucrose.htm Makwakwa, T. A. (2014). A Study of the Effect of Salt Solutions on the Kinetics of Sucrose Inversion as Monitored by Polarimetry. University of South Africa. http://uir.unisa.ac.za/bitstream/handle/10500/18902/dissertation_makwakwa_ta.pdf;sequence=3 NSW Education Standards Authority. (n.d.). Formic Acid. Chemical Safety in Schools. https://online.det.nsw.edu.au/ecmjsp/chemicals/?mode=viewchemares&chemalpha=F&chemid=795 #skipToContent Physical Chemistry Laboratory. (2018). Chemical Physics | Details. Tel Aviv University. https://www.tau.ac.il/~phchlab/exp-inversion-theory.html Sale, J. W., & Skinner, W. W. (1922). Relative Sweetness of Invert Sugar. Journal of Industrial & Engineering Chemistry, 14(6), 522–524. https://doi.org/10.1021/ie50150a019 The Nest Group, Inc. (2016, December 2). Molarity of Concentrated Acids & Bases. https://www.nestgrp.com/protocols/trng/molarity.shtml Wienen, W. J., & Shallenberger, R. S. (1988). Influence of acid and temperature on the rate of inversion of sucrose. Food Chemistry, 29(1), 51–55. https://doi.org/10.1016/0308-8146(88)90075-1 12

Appendices Appendix A: 𝐩𝐇 calculations (using ICE tables) Changing the concentration of HCOO– in the equilibrium mixture of the reaction leads to a change in pH. An ICE table was used with the concentrations of HCOOH, H + and HCOO– to change the independent variable.

Table 10 shows the calculation for a pH of 2.00 Table 10: ICE table for a pH of 2.00.

[HCOOH] 0.570 Initial 0.0100 Change Equilibrium 0.560 +] pH = − log[H = −log 0.01 = 2.00

[H+] 0.000 0.0100 0.0100

[HCOO–] 0.000 0.0100 0.0100

When the HCOONa is added to a solution of HCOOH at equilibrium, according to Le Chatelier’s principle, the reaction favours the reactants. In turn, this means that an amount of H+ and HCOO– ions will react and form methanoic acid. Letting the concentration of this amount be 𝑥 mol dm–3 (this is the amount in the “Change” row of Table 11), when equilibrium is established, the 𝐾𝑎 can be expressed as: [H + ][HCOO− ] 𝐾𝑎 = [HCOOH] (0.01 − 𝑥)(𝑤 − 𝑥) 1.77 × 10−4 = 0.560 + 𝑥 where 𝑤 = concentration of HCOONa required, in order to achieve pH of 4.00 in the buffer solution. The solution to this equation was approximated with the software Wolfram Alpha and it was found to be equal to: 𝑥 = 5 × 10−7 × (1000000𝑤 − √1000000000000𝑤 2 − 19646000000𝑤 + 500051329 + 10177) In order for the pH to be approximately 4.00, the [H + ] at equilibrium (0.01 − 𝑥) must be equal to 1.00 × 10−4. Hence, using the solver feature in Excel, it was calculated that 𝑤 must be 0.700 mol dm–3.

Table 11: ICE table for a pH of 4.00.

[HCOOH] 0.560 Initial Change 9.85 × 10−3 0.570 Equilibrium +] −4 ) pH = − log[H = −log(1.02 × 10 = 3.99 ≈ 4.00

[H+] 0.0100 9.85 × 10−3 1.02 × 10−4

[HCOO–] 0.700 9.85 × 10−3 0.690

The calculation for pH of 6.00 was conducted in a similar manner.

Appendix B: Stoichiometric calculations used in the method Other relevant calculations used for the stoichiometric relationships in the method are evident in Table 12.

Table 12: Stoichiometric calculations used in the method.

Calculation for the dilution of the methanoic acid solution (in step 2 of the method).

𝑐1 𝑉1 = 𝑐2 𝑉2 23.6 × 0.100 𝑉2 = = 4.1403508772 ≈ 4.14 dm3 0.570

Calculation for mass of sucrose used (in step 4 of the method).

𝑚 = 𝑛𝑀 𝑚 = 𝑐𝑉𝑀 The volume used is 0.0100 dm3, due to the maximum volume which can be input in the polarimeter’s observation tube. Arbitrarily assuming a concentration of C12H22O11 of 0.100 mol dm–3: 𝑚 = 0.100 × 0.0100 × (12 × 12.01 + 22 × 1.01 + 11 × 16.00) 𝑚 = 0.342 g 13

𝑚 = 𝑐𝑉𝑀 The volume used is 0.0100 dm3, due to the maximum volume which can be input in the polarimeter’s observation tube. According to Le Chatelier’s principle, a concentration of 0.710 mol dm–3 of HCOONa is required for an equilibrium pH of 4.00: 𝑚 = 0.700 × 0.0100 × (1.01 + 12.01 + 2 × 16.00 + 22.99) 𝑚 = 0.476 g 𝑚 = 𝑐𝑉𝑀 Calculation for The volume used is 0.0100 dm3, due to the maximum volume which can be input in amount of the polarimeter’s observation tube. According to Le Chatelier’s principle, a HCOONa added concentration of 2.223 mol dm–3 of HCOONa is required for an equilibrium pH of when pH = 6.00 6.00: (in step 14 of 𝑚 = 2.213 × 0.01 × (1.01 + 12.01 + 2 × 16.00 + 22.99) the method). 𝑚 = 1.505 g Calculation for amount of HCOONa added when pH = 4.00 (in step 10 of the method).

Appendix C: Sample calculations for the uncertainty of the initial concentrations of sucrose and H+, and the rate constant 𝒌 Table 13 includes the calculations for the uncertainty of [C12H22O11]0 for all trials, and a sample calculation for the uncertainty of [H+]0 for all pH = 2.00 trials.

Table 13: Sample calculations for initial concentrations uncertainties.

Uncertainty of [C12H22O11]0 for all trials: 𝑛 𝑚 𝑐= = 𝑉 𝑀𝑉 since 𝑀 is a constant, it has no uncertainty percentage uncertainty 0.001 0.05 = × 100 + × 100 0.342 10 = 0.79% uncertainty = mol dm−3

0.79 100

× 0.01 = ±7.9 × 10−5

Uncertainty of [H+]0 for all pH = 2.00 trials: To find this uncertainty, an indirect method was used as there is no direct calculation for the propagation of uncertainties of exponential functions. Namely, the extreme values of [H+]0 were calculated, and the uncertainty was found as: [H + ]0 = 10−pH [H + ]0lower boundary = 10−2.00−0.01 = 9.772 × 10−3 [H + ]0upper boundary = 10−2.00+0.01 = 1.023 × 10−2 uncertainty = max (|[H + ]0upper boundary − [H + ]0 | , |[H + ]0 lower boundary − [H + ]0 |) = max(2.3 × 10−4 , 2.28 × 10−4) = ±2.3 × 10−4 mol dm−3 Please note that the value of the uncertainty ends up being the same for all pH = 4.00 and 6.00 trials as well.

Finally, Table 14 explains the uncertainty calculation for the rate constant for pH = 2.00 at 𝑡 = 60.00 s.

Table 14: Sample uncertainty calculation for the rate constant.

Uncertainty for the rate constant for pH = 2.00 at 𝑡 = 60.00 s: 𝛼(𝑡) ln ( 𝛼 ) 0 𝑘= ([C12 H22 O11]0 − [H + ]0 )𝑡 For the numerator, a similar process was used as with the exponential function in Table 13.

[29.7 − 0.5] 𝛼(𝑡) ln ( ) = ln ( ) = −0.2204 [35.9 + 0.5] 𝛼0 lower boundary

For the denominator, the uncertainty for the concentration difference and the time had to both be found: uncertainty of concentration difference = 2.3 × 10−4 + 7.9 × 10−5 = 3.09 × 10−4 mol dm−3

14

ln (

[29.7 + 0.5] 𝛼(𝑡) ) = ln ( ) = −0.159 [35.9 − 0.5] 𝛼0 upper boundary

uncertainty = max (|ln (

𝛼(𝑡) 𝛼(𝑡) ) − ln ( ) |, 𝛼0 upper boundary 𝛼0

𝛼(𝑡) 𝛼(𝑡) |ln ( ) − ln ( ) |) 𝛼0 lower boundary 𝛼0

percentage uncertainty of concentration difference 3.09 × 10−4 = × 100 = 0.343% 0.1 − 0.01 percentage uncertainty of time 0.01 = × 100 = 0.017% 60

= max(0.031,0.031) = ±0.031 0.031 = 16.2% 29.7 ln ( ) 35.9 The final uncertainty of 𝑘 is hence: percentage uncertainty = 10.5% + 0.343% + 0.017% = ±16.56% 16.56 uncertainty = × 0.0351 = ±0.0058 mol−1 dm3 s−1 100 percentage uncertainty =

15

Investigation of the Effect of Impedance on the Power Factor of an RLC Circuit Exploration Research Question What is the relationship between the impedance (1.00, 2.00, 3.00, 4.00 Ω) and the power factor of an RLC AC circuit, considering capacitive and inductive effects?

Introduction and Rationale Power transmission and distribution networks deliver electricity from power plants to the consumer via alternating current (AC) circuits (Hamper, 2014). Furthermore, multiple electrical devices, such as motors and conveyor belts, use AC power. Due to the presence of electric and magnetic fields in capacitors and inductors respectively, the delivered power to the load of an AC circuit may be adversely affected. The concept of impedance, which is used to describe the aforementioned effects, is critical to investigate the delivered power in an AC circuit.

After studying capacitance in class in relatively simple circuits, my curiosity and interest in the topic enabled me to begin further research into more complex circuits involving capacitors, such as RLC circuits. Personally, I was extremely fascinated by the factors which affect power loss, seeing the great importance of AC power in numerous aspects of our daily lives. This investigation enabled me to understand the ways in which power loss can be minimised, which can in turn contribute to improved efficiency and sustainability in industrial and distribution settings. Electrical engineering is also a career path that I am considering after school, and hence this experiment acts as an indicator of what I can expect to study in the future.

Background Most AC circuits contain resistors, capacitors and inductors; they are referred to as RLC circuits. The angular frequency (𝜔) of an AC circuit characterises how rapidly the voltage and current change polarity and is proportional to the frequency as per 𝜔 = 2𝜋𝑓 (Ling et al., 2016). Both the voltage and the current in such a circuit change in a sinusoidal manner as seen in Figure 1, according to the equations: 𝑉(𝑡) = 𝑉0 sin 𝜔𝑡 𝐼(𝑡) = 𝐼0 sin(𝜔𝑡 − 𝜙)

Figure 1: The schematic of an RLC circuit (left) and the changes in voltage and current over time (right); (Ling et al., 2016).

There is a phase difference (𝜙), measured in radians, between the voltage and current curves which depends on the inductance (𝐿) and capacitance (𝐶) of the circuit. Capacitance is measured in Farad (F) and inductance is measured in Henry (H). Capacitors in RLC circuits charge and discharge continuously, which results in the presence of electric fields between the two metal plates. These electric fields oppose changes in current, an effect that is described with the quantity known as capacitive reactance (𝑋𝐶 ). Similarly, RLC circuits contain inductors, which demonstrate an opposition to the flow of current via the induction of magnetic fields (Hewes, 2007). This effect is described as inductive reactance (𝑋𝐿 ). Both reactances depend on 𝜔 and the respective parameters of the capacitor (𝐶) and inductor (𝐿), and are measured in Ohms (Ω).

1 𝜔𝐶 𝑋𝐿 = 𝜔𝐿

𝑋𝐶 =

16

Collectively, the effects of opposing the current due to the reactances and the effect of voltage drop as a result of resistance (𝑅) are described by the quantity known as impedance (𝑍). 𝑍 = √𝑅2 + (𝑋𝐿 − 𝑋𝐶 )2 While the resistance and the reactances have the same dimensions, they are not added algebraically, implying that they have a more complex geometrical relationship between them. In fact, they are all represented by vectors, where the 𝑅, 𝑋𝐿 and 𝑋𝐶 describe the magnitudes (Ling et al., 2016). However, each of the 𝑅, 𝑋𝐿 and 𝑋𝐶 have different effects on the phase angle 𝜙, specifically: • The resistance does not cause a phase difference (𝜙 = 0). 𝜋 • The capacitive reactance generates a phase difference of 𝜙 = − 2 . This results in the current leading 𝜋

•

the voltage by 2 .

𝜋

The inductive reactance generates a phase difference of 𝜙 = + . This results in the current lagging 𝜋

2

the voltage by 2 .

Depending on the relative magnitudes of the capacitive and inductive reactances, RLC circuits are classified as capacitive (when 𝑋𝐶 > 𝑋𝐿 ) or inductive (when 𝑋𝐶 < 𝑋𝐿 ). In a similar fashion to the reactances, there are three different components of the voltage in an RLC circuit: 𝑉𝑅 , 𝑉𝐿 and 𝑉𝐶 . The voltage components, along with the current, are typically drawn in phasor diagrams, where all vectors rotate about the origin and by convention the current vector is drawn parallel to 𝑉𝑅 . A phasor diagram for an RLC circuit is depicted in Figure 2.

The resistance (𝑅) and reactance (𝑋𝐿 and 𝑋𝐶 ) phasors are always in phase with their respective voltages. The phase angle 𝜙 may be calculated using trigonometry as seen in Figure 2. 𝑉𝐿 − 𝑉𝐶 𝑋𝐿 − 𝑋𝐶 𝜙 = arctan ( ) = arctan ( ) 𝑉𝑅 𝑅 It follows that if 𝑋𝐿 ≠ 𝑋𝐶 , then the phase angle 𝜙 ≠ 0. In this scenario, the power dissipated in an AC circuit has two natures: real power (𝑃) and reactive power (𝑄). The real power (𝑃) represents the actual amount of power being consumed in the circuit. As with DC circuits, it is measured in Watts (W). On the other hand, reactive power (𝑄) is the amount of power stored momentarily in electric and magnetic fields in the capacitors and inductors respectively. It is measured in Volt Amperes reactive (VAr), which is a unit that represents the same amount of power as 1 W (Ling et al., 2016).

Figure 2: Phasor diagram of an RLC circuit.

For inductors and capacitors, due to the phase difference between the voltage and the current, there are times where the voltage and the current have opposite signs. Power flowing in an inductor or capacitor is calculated as the product of voltage and current (𝑃(𝑡) = 𝑉(𝑡) 𝐼(𝑡)). Half of the time, 𝑉(𝑡) and 𝐼(𝑡) will have the same sign; hence, the power would be positive. Accordingly, for the other half of the time, the power would be negative as 𝑉(𝑡) and 𝐼(𝑡) have opposite signs. When the power is positive, the inductors and capacitors both store energy from the circuit; when the power is negative, they both release energy back into the circuit. Hence, on average, the capacitive and inductive devices have a net zero power dissipation in AC circuits.

An AC circuit must be able to handle both the real and reactive power at any point in time in order to avoid burning or damaging the circuit. The combined effect of real and reactive power is called apparent power (𝑆) and is calculated as the geometric sum of its components using Pythagoras’ theorem, as illustrated in Figure 3 (Kuphaldt, 2020). 𝑆 = √𝑃2 + 𝑄2 Figure 3: Geometric sum of the apparent power.

17

The SI unit for apparent power is Volt Amperes (VA). The equivalent AC calculation, derived from Ohm’s law, for the power dissipated in a DC circuit with resistors (𝑃 = 𝐼 2 𝑅) is: 𝑆 = 𝐼2𝑍 The ratio of real power to apparent power (or resistance to impedance) is equal to the power factor (𝑝𝑓): 𝑃 𝑅 𝑅 𝑝𝑓 = cos 𝜙 = = = 𝑆 𝑍 √𝑅 2 + (𝑋𝐿 − 𝑋𝐶 )2 The power factor is a very useful metric that represents the utilisation of power in an AC circuit. The closer it is to 1, the lower the reactive power, which in turn means the real power is transferred to the load more efficiently.

Hypothesis The maximum power factor of an RLC AC circuit will be observed when the inductive reactance is equal to the capacitive reactance, and thus the impedance is equal to the resistance. This is inferred from the fact that when 𝑋𝐿 = 𝑋𝐶 , 𝜙 = 0, and hence cos 𝜙 is at its maximum value of 1. Whenever 𝑋𝐿 > 𝑋𝐶 (circuit is inductive in nature), it should be that 𝜙 > 0 and whenever 𝑋𝐿 < 𝑋𝐶 (circuit is capacitive in nature), it should be that 𝜋 𝜋 𝜙 < 0. As long as the phase angle is between − 2 to 2 , the power factor (cos 𝜙) will be between 0 and 1. The power factor of the circuit pairs with equal magnitude impedance values is expected to be also equal.

Methodology Variables The independent variable is elaborated on in Table 1.

Table 1: Independent variable.

Independent variable

Variation method

Impedance (𝑍)

Use seven different RLC circuits, designed appropriately to ensure that each circuit gives a different value for the impedance.

Impedance increments and nature 4.00 Ω, capacitive 3.00 Ω, capacitive 2.00 Ω, capacitive 1.00 Ω, neither capacitive nor inductive 2.00 Ω, inductive 3.00 Ω, inductive 4.00 Ω, inductive

In this experiment, six circuits (three capacitive and three inductive) were tested along with another circuit which was neither capacitive nor inductive. The rationale behind this design decision was threefold. Firstly, it allows for testing the hypothesis, where the circuit which is neither capacitive nor inductive is expected to have the highest power factor. Moreover, investigating both capacitive and inductive RLC circuits to vary impedance is more pertinent to real-life engineering and physics systems (Qabazard, 2015). Finally, it allows for determining whether the power factor variation in capacitive or inductive RLC circuits is symmetrical.

The first step in the circuit design was the choice of the resistance value. The value of 𝑅 should be relatively small in order for the reactances to be of comparable magnitudes and to be able to purchase appropriate capacitors and inductors that are commonly available (which usually have a small capacitance and inductance respectively). The value of 𝑅 was arbitrarily chosen to be 1.00 Ω. Next, the angular frequency of the circuit was calculated as 𝜔 = 2𝜋𝑓 = 2𝜋 × 50 ≈ 314 rad. s −1. Beginning from the circuit with impedance 1.00 Ω, 1 1 which has 𝑋𝐿 = 𝑋𝐶 , it follows that 𝜔𝐿 = 𝜔𝐶, and hence 𝐶𝐿 = 𝜔2 ≈ 1.01 × 10−5. By arbitrarily choosing the value of 𝐶 = 1.00 × 10−3 F, it can be calculated that the value of 𝐿 must be approximately 1.00 × 10−2 H. Hence, the impedance of the circuit with a 1.00 Ω resistor, a 1.00 × 10−3 F capacitor and a 1.00 × 10−2 H inductor is: 𝑍 = √𝑅2 + (𝑋𝐿 − 𝑋𝐶 )2 = √𝑅2 + (𝜔𝐿 −

2 1 2 1 ) = √12 + (314 × 10−2 − ) ≈ 1.00 Ω 𝜔𝐶 314 × 10−3

18

Starting from this base circuit, three more capacitive and three more inductive RLC circuits were designed so that the impedance varied by 1.00 Ω each time. For the capacitive circuits, the inductance was arbitrarily chosen to be very small (1.00 × 10−6 H) so that 𝑋𝐿 < 𝑋𝐶 . Accordingly, for the inductive circuits, the capacitance was arbitrarily chosen to be relatively high (1.00 × 10−2 F) so that 𝑋𝐿 > 𝑋𝐶 . For each of these six circuits, the target impedance, angular frequency, resistance and one of 𝐿 or 𝐶 were known, and thus it was possible to calculate the necessary 𝐶 or 𝐿 value respectively. For example, in order to design the capacitive RLC circuit with an impedance 2.00 Ω: 𝑍=

√𝑅2

+ (𝑋𝐿 − 𝑋𝐶

)2

= √𝑅 2 + (𝜔𝐿 −

1 2 ) 𝜔𝐶

1

∴𝐶=|

| 𝜔 2 𝐿 − 𝜔√𝑍 2 − 𝑅2 1 𝐶=| | ≈ 1.80 × 10−3 F 2 −6 2 2 314 × 10 − 314√2 − 1 This process was repeated for the five additional circuits in order to achieve impedances of 2.00, 3.00 and 4.00 Ω in inductive and capacitive nature, as summarised in Table 2. The experimental impedances are slightly off from the originally planned 1.00 Ω intervals. This limitation was imposed due to the availability of specific values of capacitance and inductance in the market components.

Table 2: Circuit design and parametrisation summary.

Circuit Capacitance Inductance number /F /H 1 8.20 × 10−4 1.00 × 10−6 2 1.13 × 10−3 1.00 × 10−6 3 1.80 × 10−3 1.00 × 10−6 4 1.00 × 10−3 1.00 × 10−2 5 1.00 × 10−2 6.50 × 10−3 6 1.00 × 10−2 1.00 × 10−2 7 1.00 × 10−2 1.33 × 10−2

Capacitive reactance / Ω 3.88 2.82 1.77 3.18 0.32 0.32 0.32

Inductive reactance / Ω 3.14 × 10−4 3.14 × 10−4 3.14 × 10−4 3.14 2.04 3.14 4.18

Impedance /Ω 4.01 2.99 2.03 1.00 1.99 3.00 3.99

Circuit Nature Capacitive Capacitive Capacitive Neither Inductive Inductive Inductive

Table 3 explains the dependent variable.

Table 3: Dependent variable.

Dependent variable

Measurement method

Power factor (𝑝𝑓)

The power factor (𝑝𝑓) will be calculated as cos 𝜙, where 𝜙 is the phase angle obtained by comparing the source AC voltage as compared to the RLC alternating voltage curves.

Justification Using school equipment, the most direct way to obtaining the power factor is by determining the phase angle for each value of the impedance. The phase angle itself is directly related to impedance, according to: 𝑋𝐿 − 𝑋𝐶 ) 𝜙 = arctan ( 𝑅 √𝑍 2 − 𝑅2 ) 𝜙 = arctan ( 𝑅

Units

This is a dimensionless quantity (no units).

Table 4 details all of the controlled variables in this experiment.

Table 4: Controlled variables.

Controlled variable Ambient temperature kept at 25ºC (room temperature).

Control method Carry out all trials in the same airconditioned laboratory.

Justification The resistance, capacitance and inductance values are affected by temperature. If the temperature was not controlled, the impedance calculation would be invalid (Li & Yin, 2017). Minor fluctuations are negligible.

19

Same pieces of electrical equipment used in all trials of the same circuit.

Use the same type of resistors, capacitors, inductors, connecting wires and breadboards for each circuit.

Use the exact same oscilloscope for every Oscilloscope model trial of every circuit, and setting. configured in the same way.

The load resistor in all circuits is of equal Resistance 𝑅 = resistance (𝑅 = 1.00 1.00 Ω. Ω) and same model (same company).

2.00 V output used in all circuits.

Frequency maintained at 50.0 Hz.

Do not alter the voltage setting on the power pack for any trial; keep it at 2.00 V.

Use the same power pack in each circuit for all trials.

This warrants that the only factor affecting the dependent variable across the different trials for each circuit is the impedance. If different types of electrical equipment were to be used in each trial, there is a higher risk of the impedance value being affected due to discrepancies in the manufacturer’s tolerance.

To ensure the measurements for the phase angle and impedance are made with the same instrument (and same configuration).

This investigation aims to determine the effect of impedance, in capacitive and inductive RLC circuits, on the power factor. Varying resistance would affect the overall impedance (and hence the power factor), as well as the capacitive or inductive nature of each circuit.

The oscilloscope has a vertical scale of 2.00 mV to 10.0 V, meaning that 10.0 V is the maximum potential difference which can be used in this experiment. 2.00 V was arbitrarily chosen as a relatively small voltage would justify the use of small resistance and reactance values; the smallest power pack output available was 2.00 V. The resistance (𝑅) and reactance (𝑋𝐿 and 𝑋𝐶 ) phasors remain in phase with their corresponding voltages, 𝑉𝑅 , 𝑉𝐿 and 𝑉𝐶 . Changing the voltage would affect the value of 𝜙 and therefore the power factor for all impedances.

The frequency must be kept constant so that 𝜔 is also a constant value. If 𝜔 varied, it would affect the capacitive and inductive reactances and therefore the impedance.

Risk Assessment Table 5 details hazards, risks posed and mitigations to establish a safe experimental environment.

Table 5: Risk assessment.

Hazard

Risk posed

Risk of electric shock.

The risk of an electric shock can arise due to the one of the following reasons: 1. Using unsafe or defective electrical components.

2. Electrical conductors come in contact with wet hands.

3. Touching any exposed wires or any energised, uninsulated component.

These may cause skin burns, or even internal tissue and heart damage under certain conditions.

Burns or fire risk if shortcircuited.

Short-circuit may appear due to defective electrical components or incorrect connection in the circuit. The current may reach dangerously high levels, creating a fire hazard and risk of burns or electric shock.

Mitigation Each risk posed can be minimised by: 1. Using new capacitors, inductors, resistors and breadboards (which are less likely to be faulty) and visually inspect pre-used equipment to ensure there are no apparent defects.

2. Thoroughly dry hands before handling any electrical equipment and make sure that there is no water near the circuits.

3. Confirm that all cables and devices are securely plugged in. Use latex gloves when handling equipment.

Only work with the components with the power pack disconnected and off. Confirm that all cables and devices are securely plugged in and use well-insulated wires. Use latex gloves and identify the location of the first aid kit and fire extinguisher in the laboratory.

20

Dropping the oscilloscope or power pack on the ground.

The oscilloscope and power pack (each having a mass of a few kg) are quite heavy and may result in broken bones or other injuries if it falls on someone. It can also fall on the ground where someone may trip over it, or may cause structural damage.

Place the oscilloscope and power pack away from the edge of the laboratory bench and ensure that only the experimenter is near the devices. Neatly collect any loose hanging wires to mitigate trip hazard.

Equipment The required components for this experiment are shown in Table 6.

Table 6: Equipment list.

Quantity 1 1 1 pair 2 2 3 1 2 1 1 1 1 3 1 3 7 7

Equipment 100 MHz digital storage oscilloscope (PASCO SB-9621A) 50.0 Hz Battery pack (IEC LB2631-101) Latex disposable gloves Connector wires Alligator clips 1.00 × 10−2 F, 10V capacitors (CDE/Illinois 109TTA010M) 1.80 × 10−3 F, 40V capacitor (KEMET PEG225KG4180ME1) 1.00 × 10−3 F, 16V capacitors (Vishay MAL212015102E3) 1.30 × 10−4 F, 250V capacitor (Vishay 53D131F250HJ6) 8.20 × 10−4 F, 40V capacitor (Vishay MAL212517821E3) 4.70 × 10−3 H, 55 mA inductor (EPCOS/TDK B78108S1475J) 1.80 × 10−3 H, 95 mA inductor (EPCOS/TDK B78108S1185J000) 1.00 × 10−2 H, 0.06 A axial inductors (EPCOS/TDK B82144A2106J) 3.30 × 10−3 H, 62 mA inductor (EPCOS/TDK B78108S1335J) 1.00 × 10−6 H, 2.2 A inductor (EPCOS/TDK B82144A2102K) 1.00 Ω metal oxide resistors (KOA Speer MOSX2CT52A1R0J) Half-size breadboard (Digilent 240-131)

Uncertainty ± 0.5 ms ± 0.10 Hz – – – ±20% ±20% ±20% –10%, + 50% ±20% ±5% ±5% ±5% ±5% ±10% ±5% –

Method Part A: Circuit design (for the specific electrical circuit diagrams, refer to Appendix B).

1. Connect one 1.00 Ω resistor in each one of the seven breadboards.

2. Connect the capacitor and inductor and shown in Table 7 for circuit 1 in series to the resistor.

3. Repeat step 2 for circuits 2-7 on a separate breadboard. For circuit 2, the two capacitors must be connected in parallel to each other, and for circuits 5 and 7, the two inductors must be connected in series.

Table 7: List of components for each circuit.

Circuit 1

3 4

List of capacitors one 8.20 × 10−4 F one 1.00 × 10−3 F one 1.30 × 10−4 F one 1.80 × 10−3 F one 1.00 × 10−3 F

5

one 1.00 × 10−2 F

6

one 1.00 × 10−2 F

7

one 1.00 × 10−2 F

2

List of inductors one 1.00 × 10−6 H one 1.00 × 10−6 H one 1.00 × 10−6 H one 1.00 × 10−2 H one 4.70 × 10−3 H one 1.80 × 10−3 H one 1.00 × 10−2 H one 1.00 × 10−2 H one 3.30 × 10−3 H

Figure 4: Generic electrical diagram showing the RLC circuits.

Part B: Data collection 1. Connect the power pack to the two holes of the breadboard at the beginning and end of the RLC circuit 1.

2. Connect the ground and channel 1 probes of the oscilloscope across the 1.00 Ω resistor.

3. Connect the channel 2 probe of the oscilloscope at the point between the inductor and the power pack.

21

4. Turn on the power at 2.00 V and 50.0 Hz and adjust the vertical position of each curve so that the troughs (minimums) of the sinusoidal graphs lie on the horizontal axis. Also, adjust the horizontal scale of the oscilloscope so that appropriate phase difference measurements may be obtained.

5. Record the phase difference (∆𝑡) between the two sinusoidal waveforms in the oscilloscope screen as the horizontal displacement between the two troughs.

6. Repeat steps 2-5 another four times.

7. Repeat steps 1-6 using RLC circuits 2 to 7.

Results and Analysis Raw Data Qualitative Data When the oscilloscope probes were connected at the indicated points in each circuit, the screen displayed two sinusoidal curves corresponding to the voltage at each probe. The characteristics of the sinusoidal curves changed according to the variation in the value of impedance (amplitude and phase difference). An example showing the phase difference is evident in Figure 5.

Quantitative Data Quantitative raw data collected is shown in Table 8. Please note that during the data processing, the impedance magnitude was assumed to have no uncertainty. This was a necessary assumption as otherwise, according to the manufacturer tolerance values, the final uncertainty would be unreasonably large. An example of the aforementioned issue, and the relevant calculations, can be found in Appendix A.

Figure 5: Oscilloscope screenshot showing the phase difference between the two voltage curves in circuit 7.

Table 8: Quantitative raw data.

Circuit

Impedance (Ω)

1 2 3 4 5 6 7

4.01 2.99 2.03 1.00 1.99 3.00 3.99

Trial 1 25.0 23.0 21.0 0.0 21.0 23.0 25.0

Phase difference ∆𝒕 / ± 0.5 ms Trial 2 Trial 3 Trial 4 25.0 25.0 25.0 23.0 23.0 23.0 21.0 21.0 21.0 0.0 0.0 0.0 21.0 21.0 21.0 23.0 23.0 23.0 25.0 25.0 25.0

Trial 5 25.0 23.0 21.0 0.0 21.0 23.0 25.0

Data Processing A summary of all processed data is evident in Table 9.

Table 9: Summary of processed data.

Circuit

Average phase difference / ±0.0005 s

1 2 3 4 5 6 7

–0.0250 –0.0230 –0.0210 0.0000 0.0210 0.0230 0.0250

Average phase angle 𝝓 / ±0.03 rad) –1.25 –1.15 –1.05 0.00 1.05 1.15 1.25

Power factor 𝒑𝒇 0.315 ± 0.029 0.408 ± 0.028 0.498 ± 0.026 1.000 ± 0.000 0.498 ± 0.026 0.408 ± 0.028 0.315 ± 0.029

For circuit 4, the uncertainty for the phase angle was assumed to be ± 0.03 rad, as calculating with

∆𝜙 𝜙

would

result in a division by 0. Furthermore, the uncertainty in phase angle of ± 0.03 rad would result in 𝑝𝑓 with a value of 1.000 for both the upper and lower boundaries. This is a result of the nature of the cosine function, where cos(−0.03) = cos(0.03).

22

The average phase difference for each trial was calculated as follows: Average phase difference for circuit 1: 25.0 + 25.0 + 25.0 + 25.0 + 25.0 ∆𝑡 = 5 ∆𝑡 = 25.0 ms ∆𝑡 = 0.0250 s

0.5 + 0.5 + 0.5 + 0.5 + 0.5 5 uncertainty = ±0.5 ms uncertainty = ±0.0005 s uncertainty =

As explained earlier, the phase angle is illustrated as the horizontal displacement of the two curves in relation to one another. The oscilloscope’s horizontal axis has dimensions of time, whilst the phase difference has dimensions of angle. Hence the measurements must be normalised by dividing by the period 𝑇. Furthermore, 1 since 𝑓 = 𝑇, and the frequency in the experiments is always 50.0 Hz, 𝜙 and 𝑝𝑓 may be calculated as: ∆𝑡 𝜙 (rad) = = 𝑓∆𝑡 = 50.0∆𝑡 𝑇 𝑝𝑓 = cos 𝜙 Average phase angle for circuit 1: 0.01 0.0005 𝜙 = −50.0 × 0.0250 uncertainty = ( + ) × 100 = 2.02% 𝜙 = −1.25 rad 50.0 0.0250 2.02 uncertainty = × |−1.25| = 0.02525 ≈ ±0.03 100 Average power factor for circuit 1: 𝑝𝑓 = cos(−1.25) 𝑝𝑓lower boundary = cos(−1.28) = 0.286 𝑝𝑓 = 0.315 𝑝𝑓upper boundary = cos(−1.22) = 0.343 uncertainty = max(|𝑝𝑓upper boundary − 𝑝𝑓| , |𝑝𝑓lower boundary − 𝑝𝑓|) uncertainty = max(0.029, 0.028) uncertainty = ±0.029 Figures 6 and 7 are graphs showing the relationship between the power factor the impedance, for circuits 1-4 and 4-7 respectively.

Relationship between the Power Factor and Impedance (Circuits 1-4) 1.200

1.000

Power factor

0.800 0.600 0.400 0.200

0.000 0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

Impedance (Ω) Experimental Values

Theoretical Values

Figure 6: Graph showing the dependence of the power factor on impedance for the capacitive circuits. Please note that the circuit with impedance 1.00 Ω is neither inductive nor capacitive.

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Relationship between the Power Factor and Impedance (Circuits 4-7) 1.200

1.000

Power factor

0.800

0.600

0.400

0.200

0.000 0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

Impedance (Ω) Experimental Values

Theoretical Values

Figure 7: Graph showing the dependence of the power factor on impedance for the inductive circuits. Please note that the circuit with impedance 1.00 Ω is neither inductive nor capacitive.

In Figures 6 and 7, it is evident that the power factor has an inversely proportional relationship to the impedance. This trend was expected as the power factor is given by (as explained in the Background 𝑅 Information) 𝑝𝑓 = 𝑍 . The trend shows a purely resistive AC circuit (circuit 4, as 𝑋𝐶 = 𝑋𝐿 ) has the highest power factor. On the contrary, the circuits with most dominant capacitive (circuit 1) or inductive (circuit 7) nature demonstrated the lowest power factor.

Conclusion and Evaluation Conclusion To conclude, in this experiment the following hypothesis was tested: when the inductive reactance was of the same magnitude as the capacitive reactance, the power factor of the RLC AC was expected to be at a maximum. After the seven circuits were tested, and the results were processed, it was evident that the highest power factor was observed when 𝑋𝐿 = 𝑋𝐶 . In turn, the phase difference between the voltage and current in these circuits was 0 (𝜙 = 0), and therefore the power factor (𝑝𝑓 = cos 𝜙) obtained its maximum value of 1.

Hence, there is evidence to suggest the hypothesis can be accepted. The phenomena responsible for the observed trend can be attributed to the continuous charging and discharging of the capacitors and the induced magnetic fields in the inductors, both of which introduce additional resistance to the flow of current. Thus, in order to minimise losses in electrical power transmission and distribution in AC circuits, the inductive and capacitive effects must cancel out. As the presence of inductors and capacitors is unavoidable in most largescale AC circuits, such as power generation and industrial motors, an extensive effort must be applied to ensure that 𝑋𝐶 = 𝑋𝐿 . In most modern applications, this is achieved with the help of power electronics, which continuously monitor the power factor and if it deviates from 1, they dynamically modify 𝑋𝐶 or 𝑋𝐿 using a negative feedback architecture (Kundur, 1994).

The secondary hypothesis that could be tested in this experiment was whether the power factor of the circuit pairs with equal magnitude impedance values would be equal. The symmetry of capacitive and inductive effects in circuits with the same magnitude of |𝑋𝐿 − 𝑋𝐶 |, but opposite values of 𝜙 (for instance, circuits 1 and 7), was confirmed by the analysis of experimental results. This implies that regardless of whether there are capacitive or inductive effects in an AC circuit, the power factor experiences a similar drop. Individually, capacitors and inductors may cause significant power losses in AC RC and RL circuits; however, the aforementioned engineered symmetry could be utilised to minimise power losses. The principle used by power electronics to achieve power factors close to 1 in real life applications relies exactly on this circuit design.

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Evaluation In assessing the validity of this experiment, the choice of the independent (impedance) and dependent (power factor) variables, the control methods of the controlled variables, and the experimental methodology must be considered. In the research hypothesis, it was claimed that the power factor of the RLC circuit would depend on the impedance, and would obtain the maximum value when the impedance was minimum. Choosing the magnitude and nature of the impedance as the independent variable, and the power factor as the dependent variable, rendered the experiment valid. The variation of the independent variable was incremental in intervals of 1.00 Ω, starting from highly capacitive (4.00 Ω) to purely resistive (1.00 Ω) to highly inductive (4.00 Ω).

Since both the inductive and capacitive reactances depend on the angular frequency, which is not a conveniently integer number, extensive care was taken when designing each circuit to ensure that the impedance varied according to the 1.00 Ω intervals, as in Tables 2 and 7. Additionally, as shown in Table 4, appropriate measures were taken to ensure that all controlled variables were kept constant, which in turn, further improves the validity. Finally, the method steps were designed in a logical order so that all the variations in the impedance would produce a measurable effect on the power factor, and they could be replicated easily by any other researchers in the future without ambiguity.

In assessing the reliability of the investigation, the consistency of results must be evaluated. For each circuit configuration, the exact same independent variable values were used as input, and hence a reliable result would produce the same output, as was seen in Table 8. Given that all five phase differences in each circuit were measured to be the same, the reliability of the experiment can be deemed exceptionally high. Furthermore, the pairs of circuits with corresponding impedance magnitude, but opposite phase angles, (circuits 1 and 7; 2 and 6; 3 and 5) produced the exact same phase difference measurements, which confirms the high reliability.

The experiment featured measurements done with highly precise, digital equipment. Namely, the oscilloscope used was able to measure the phase difference (which was critical to calculating the power factor) with a precision of ±0.0005 s. This ensures the measurements were reliable and precise. On the other hand, with regards to precision, the tolerance provided by the manufacturer for the magnitudes of inductance, capacitance and resistance varied from ±5% to –10%, +50% (as was indicated in Table 6). Inevitably, such high tolerance values introduce the risk of having imprecise impedance in a particular circuit, especially circuit 2, where a high uncertainty capacitor was used. However, this is not a factor that can be controlled or mitigated, due to the inductors, capacitors and resistors available for purchase. Additionally, there was no evidence of circuit 2 having a higher percentage error compared to its pair circuit 6 (refer to Table 10). During the data processing, the impedance magnitudes were assumed to have no uncertainty, as otherwise the final uncertainty for the power factor would propagate to unreasonably large numbers. Lastly, all measured and processed values were obtained to 3 significant figures, ensuring that the number of decimal places corresponded with the uncertainty of each device. Such level of precision is satisfactory for an investigation of this nature.

The accuracy of the experiment may be assessed as the relative difference between the measured power factors 𝑅 and the theoretical power factors, calculated using 𝑝𝑓 = 𝑍 . The mean percentage error for each circuit can be seen in Table 10. All experimental values, with the exception of circuit 5, are overestimates of the theoretical values, which indicates a source of systematic error due to a potential, unconscious observer bias. As illustrated in Figure 5, the apex of both curves, and in particular the channel 1 curve, were exceptionally broad.

The process of exporting oscilloscope data directly to a spreadsheet could not be completed due to incompatibility issues (which produced incoherent measurements in the dataset); hence the phase difference had to be measured by eye as the horizontal difference between the troughs of the two curves. The broadness of the curves rendered an accurate measurement improbable.

Table 10: Comparison of experimental and theoretical values of the power factor. Red indicates high values.

Circuit Theoretical power factor 𝒑𝒇 Experimental power factor 𝒑𝒇 Mean percentage error (%) 0.315 ± 0.029 26.5 1 0.249 21.8 0.408 ± 0.028 2 0.335 1.22 3 0.492 0.498 ± 0.026 0.100 4 0.999 1.000 ± 0.000 0.797 5 0.502 0.498 ± 0.026 0.408 ± 0.028 22.2 6 0.334 25.5 0.315 ± 0.029 7 0.251 25

A sample calculation for the mean percentage error is shown below.

Percentage error for circuit 1 experimental value − theoretical value 0.315 − 0.249 =| | × 100 = | | × 100 ≈ 26.5% theoretical value 0.249 As evidenced in Table 10, the circuit 1 and 7, and 2 and 6 pairs had a notably high mean percentage error, as opposed to circuits 3-5 which had a very small mean percentage error. Mathematically, the reason for this was that the difference between experimental and theoretical power factors (numerator of the percentage error calculation) was much larger for circuits 1, 2, 6 and 7, exacerbated by the fact that the theoretical values (denominator) were also smaller. The common characteristic of these circuits is that they have the highest impedance values among all trials. The literature suggests that due to the nature of the oscilloscope probes and the way they take measurements, higher impedance values in a circuit may yield erratic results. At low AC frequencies, such as the 50.0 Hz used, the probe lead is characterised by high resistance in order to minimise the energy drawn by the circuit. When the circuit is close to resistive (circuits 3-5), the overall contribution of the reactances on the impedance is lower, and since the probe lead is very resistive, this effect is not very apparent, which in turn leads to lower percentage error. On the other hand, in circuits with higher reactances relative to the resistance (circuits 1, 2, 6, and 7) the high resistance of the probe “masks” the inductive and capacitive effects, leading to a higher percentage error (Maliniak, 2014). A full list of all factors which may contribute towards hindering the accuracy and precision can be seen in Table 11.

Table 11: Evaluation of sources of error, their impacts, and possible improvements which can be made to the experiment.

Source of error

Significance and impact of error Improvement to reduce error Systematic errors (which may affect accuracy) Extract the voltage data from the Overestimating the phase difference oscilloscope and determine the Observer bias when leads to a higher deviation of the minimum values and phase extracting the phase experimental power factor compared difference, rather than judging by difference from the to the theoretical values. Highly eye. If this is not possible, use oscilloscope curves by eye. significant. multiple experimenters or observers and take the average.

The impedance in circuits 1, 2, 6 and 7 Use a more sensitive oscilloscope (ie. with a highly inductive or probe that does not have such a The masking effect of the capacitive nature) was underestimated high resistance at low AC reactance contribution by as the reactance contribution was frequencies. Use an oscilloscope the oscilloscope probes at “masked”. This causes higher than with different modes for high or 1 the low AC frequency of expected power factors as 𝑝𝑓 ∝ 𝑍. low frequencies, or perform the 50.0 Hz. experiment with higher Highly significant for the inductive frequencies. and capacitive circuits.

Conduct the experiment in a Variations in absolute The impedance values of an RLC laboratory that is underground or humidity and air pressure in circuit may be slightly affected by in a building location without the laboratory between the these local environmental changes (Li windows to minimise the linkage time that each trial was & Yin, 2017). Relatively insignificant. between the ambient and interior conducted. conditions.

Random errors (which may affect precision) Since both reactances depend on 𝜔, The AC frequency was not which in turn depends on the Use a signal generator or power precisely 50.0 Hz in all frequency, there is a source of random control electronics to ensure a trials, as displayed on the error whenever the frequency is not stable frequency of the AC power oscilloscope (Figure 5). It exactly 50.0 Hz. Ultimately, this input. fluctuated slightly under and affects the measured power factor. over.

Low significance.

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The resistors, capacitors and inductors used had high manufacturer tolerance.

The contact between the oscilloscope probe and the electrical components’ metallic terminal may have been unstable.

The values of 𝑅, 𝐶 and 𝐿 all affect the impedance and thus the power factor.

Low significance as the circuit pairs displayed identical phase differences.

The probe tip was hooked onto the terminals of the axial resistors, capacitors and inductors. The possible instability may have affected the phase difference measurements. Low significance as the circuit pairs displayed identical phase differences.

Purchase and use more precise (but also more costly) components with a tighter range of manufacturer tolerance.

Ensure that the probes are stabilised and tightly wrapped around the terminals of each component.

Investigation Extension and Improvements If the resources and time were available, the experiment may be improved by testing more impedance values in shorter intervals (such as 1.25 Ω, 1.50 Ω, etc.) and a larger range (for example between 1.00 Ω and 100 Ω).

This improvement would further validate the observed trends and highlight any potential deviations at higher values of impedance.

The rationale behind this investigation was to study the effects of different magnitudes and types of impedance on the power factor of an AC RLC circuit. Extending the investigation could include designing and testing circuits that are closer to real-life case studies rather than basic laboratory circuits. Namely, using higher AC voltages and impedances (both capacitive and inductive) would allow the evaluation of more realistic power circuits, such as those used in industrial applications, generation, transmission and distribution. Additionally, a separate direction could be studying the effects of impedance on circuits that include semiconductors (transistors or diodes), which are the basis of modern electronics, rather than just RLC circuits.

An extended methodology could include an experiment that evaluates power factor correction solutions. After establishing the effects of impedance on the power factor, various power factor correction topologies could be tested and assessed in terms of how well they can minimise the reactive power stored in the circuit.

Reference List Hamper, C. (2014). Higher level Physics (G. Drabble, Ed.; 2nd ed.). Pearson Education Limited.

Hewes, J. (2007). Impedance and Reactance. The Electronics Club. https://dlb.sa.edu.au/rehsmoodle/file.php/466/kpsec.freeuk.com/imped.htm Kundur, P. (1994). Power system stability and control (1st ed.). McGraw-Hill Education.

Kuphaldt, T. R. (2020, August 16). 11.3: Calculating Power Factor. Workforce LibreTexts. https://workforce.libretexts.org/Bookshelves/Electronics_Technology/Book%3A_Electric_Circuits_I I_-_Alternating_Current_(Kuphaldt)/11%3A_Power_Factor/11.03%3A_Calculating_Power_Factor Li, Z., & Yin, Z. (2017). A Method for Controlling Parallel and Series RLC Circuits with Time-Varying Resistance, Inductance and Capacitance. Circuits, Systems, and Signal Processing, 37(6), 2629– 2638. https://doi.org/10.1007/s00034-017-0669-4 Maliniak, D. (2014, May 6). Oscilloscope Probes Influence Measurements. Electronic Design. https://www.electronicdesign.com/technologies/test-measurement/article/21799660/oscilloscopeprobes-influence-measurements Ling, S. J., Sanny, J., & Moebs, W. (2016). University Physics (Vol. 2). Openstax College Rice University.

Qabazard, M. A. A. (2015). Resonance Circuits and Applications. International Journal of Inventive Engineering and Sciences (IJIES), 3(5), 6–13. http://www.ijies.org/wpcontent/uploads/papers/v3i5/E0606043515.pdf

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Appendices Appendix A: Sample Calculation for Impedance Uncertainty in Circuit 4 As listed in Table 7, circuit 4 contains one 1.00 × 10−3 F capacitor and one 1.00 × 10−2 H inductor, as well as a 1.00 Ω resistor which all circuits contain. According to the manufacturer ratings as in Table 6: • capacitance 𝐶 = 1.00 × 10−3 ± 20% F • inductance 𝐿 = 1.00 × 10−2 ± 10% H • resistance 𝑅 = 1.00 ± 5% Ω • frequency 𝑓 = 50.0 ± 0.10 Hz The values of the capacitive reactance 𝑋𝐶 and the inductive reactance 𝑋𝐿 , and their uncertainties, are then calculated. The values of 𝑋𝐶 and 𝑋𝐿 correspond with Table 2.

1 𝑋𝐶 = 𝜔𝐶 1 = 2𝜋𝑓𝐶

percentage uncertainty of 𝑋𝐶 0.1 =( × 100) + 20% 50.0 = ±20.2%

1 2𝜋 × 50.0 × 1.00 × 10−3 = 3.18131 Ω 𝑋𝐿 = 𝜔𝐿 percentage uncertainty of 𝑋𝐿 0.1 = 2𝜋𝑓𝐿 =( × 100) + 10% −6 = 2𝜋 × 50.0 × 1.00 × 10 50.0 = ±10.2% = 3.14159 Ω Finally, the impedance 𝑍 and its uncertainty for circuit 4 is as follows: =

𝑍 = √𝑅2 + (𝑋𝐿 − 𝑋𝐶 )2 = √1.002 + (3.14159 − 3.18131 )2 ≈ 1.00 Ω

absolute uncertainty of 𝑋𝐿 − 𝑋𝐶 10.2 20.2 = × 3.14159 + × 3.18131 100 100 = ±0.963428 Ω percentage uncertainty of 𝑋𝐿 − 𝑋𝐶 0.963428 =| | × 100 3.14159 − 3.18131 = ±2321.17% percentage uncertainty of 𝑍 1 = [2(5%) + 2(2321.17%)] 2 = ±2326.17%

absolute uncertainty of 𝑍 2326.17 = × 1.00086 100 ≈ ±𝟐𝟑. 𝟑 Ω The same calculations for uncertainty were carried out for the remaining circuits. The results indicated a similarly high absolute uncertainty of the impedance. In circuit 4, where 𝑍 = 1.00 Ω, the values of ±20.2% and ±10.2% for the 𝑋𝐶 and 𝑋𝐿 uncertainties respectively produce a notably high uncertainty for the calculation of 𝑋𝐿 − 𝑋𝐶 in relation to 𝑍. Furthermore, the denominator in the calculation for the percentage uncertainty of 𝑋𝐿 − 𝑋𝐶 is miniscule (as circuit 4 is supposed to be purely resistive), which magnifies the final absolute uncertainty of 𝑍. As a result, it was decided to assume that the magnitude of the uncertainty of the impedance was 0 Ω, as the high value indicated above was the result of mathematical anomalies rather than physical errors.

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Appendix B: Circuit Diagrams

Circuit 1 (Capacitive)

Circuit 2 (Capacitive)

Circuit 3 (Capacitive)

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Circuit 4 (Neither)

Circuit 5 (Inductive)

Circuit 6 (Inductive)

Circuit 7 (Inductive)

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Comparing the viscosity of four different liquids Aim: To measure and compare the viscosity of four different liquids, namely: honey, corn syrup, golden syrup and molasses, and the effect they have on the speed of a marble dropped into a jar of the liquid.

Background research: According to Princeton University, “viscosity is a measure of a fluid’s resistance to flow” (n.d.). Liquids with high viscosity will “resist motion” because their molecular structure creates “a lot of internal friction” (Princeton University, n.d.). In contrast, liquids with low viscosity “flow easily” because their molecular structure creates “very little friction when...in motion” (Princeton University, n.d.). According to Education.com, when a marble is dropped into multiple liquids of different viscosities, the velocity of the marble will be slowest in the liquid that has the highest viscosity (n.d.). Furthermore, the viscosity of a liquid is affected by temperature (AZoM, 2013). High temperatures can decrease viscosity, and low temperatures can increase viscosity (AZoM, 2013). Viscosity is measured in Newton-seconds per meter squared (Ns/m2) (Science Buddies, n.d.b). The following equation can be used to calculate the viscosity of a liquid when a marble is dropped into the liquid (Science Buddies, n.d.b):

where: μ is the viscosity of the liquid in Newton-seconds per meter squared (Ns/m2). Δρ is the difference in density between the marble and the liquid in kilograms per meter cubed (kg/m3). g is the acceleration due to gravity, 9.81 meters per second squared (m/s2). a is the radius of the marble in meters (m). v is the average velocity (the distance the marble falls, divided by the time it takes to fall, in meters per second (m/s)).

According to NASA, this equation for viscosity depends on certain assumptions, including: the marble has reached its terminal velocity and the Reynolds number is very small (NASA, 2021). Terminal velocity refers to the maximum velocity (Cain, 2010). The Reynolds number refers to the ratio of inertial forces to viscous forces (NASA, 2021) and it must be very low, or the viscosity calculated using the formula above will be inaccurate (Science Buddies, n.d.b). To ensure that the Reynolds number is low, liquids such as water or oil cannot be used in the formula above (Science Buddies, n.d.b).

Both viscosity and density are physical properties of liquids (Pearson, 2017). Both density and viscosity are affected by temperature (Druin, 1968). Density is defined as the “measure of spaces between two particles in a given fluid” and refers to how compact a substance is (BYJU'S, n.d.). When a liquid is heated, the space between its particles increases, which results in a decrease in fluid density (Druin, 1968). Due to reduced fluid density, the liquid becomes less viscous (Druin, 1968). Therefore, as the temperature of a liquid increases, its

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density decreases and the liquid becomes less viscous (Druin, 1968). Here are the densities of some common household liquids: ● The density of honey is 1360 kg/m3 (SoftSchools, n.d.). ● The density of corn syrup is 1380 kg/m3 (Elert, n.d.). ● The density of golden syrup is 1430 kg/m3 (Rust, n.d.). ● The density of molasses is 1600 kg/m3 (Numerade, n.d.).

Storing these common household liquids becomes easier with knowledge of viscosity and its relationship with temperature. This can help one determine whether certain liquids should be stored in the refrigerator or at room temperature.

The scientific concept of viscosity has important real-life applications. For example, knowing the viscosity of a liquid can be beneficial when pouring liquid into a jar, or spreading liquid onto a surface. There are many practical applications for viscosity in everyday life. For example, according to QS Study, viscosity is crucial in inkjet printing, and food and drink manufacturing (n.d.). Viscosity is also important in lubrication in vehicles as it affects “the rate of oil consumption” and a car’s ability to “start in hot or cold conditions” (QS Study, n.d.). Additionally, viscosity is also crucial in the cooking industry as it “plays a significant role in the preparation and serving of food” through the use of cooking oils and fats as well as in “sauces, soups, and stews” (QS Study, n.d.). Moreover, manufacturing equipment also utilises knowledge of viscosity as lubrication is very important in the manufacturing industry (ThoughtCo, n.d.). For example, lubricants provide protection for moving parts and can prevent machines from malfunctioning (ThoughtCo, n.d.). In addition, a major consideration in the medical industry is blood viscosity (ThoughtCo, n.d.). Recognising the viscosity of blood is critical when performing injections or blood transplants as blood with a high viscosity “can form dangerous internal clots” and blood with a low viscosity is unable to clot, which can lead to “blood loss and even death” (ThoughtCo, n.d.). It is clear that viscosity is crucial in many areas of everyday life and if viscosity is not regulated properly in many industries, it will have devastating consequences.

Furthermore, there are real-life examples where knowledge of viscosity could save lives. For example, the Great Molasses Flood of 1919 was a tragic historical event where 21 people lost their lives (“Great Molasses Flood”, 2021). The flood occurred in Boston, USA, on 15 January 1919, when a giant steel tank containing molasses exploded (Sohn, 2020).

Approximately 7.5 million litres of molasses spread in a similar fashion to a tsunami wave, reaching speeds of up to 56 km/h (Lyons, 2009). This was a devastating event, with over 150 people injured (Sohn, 2020). The flood of molasses destroyed buildings, cars and other important infrastructure (“Great Molasses Flood”, 2021). Due to the cold weather on the day on which the tank exploded, the molasses became very viscous and this led to an increased number of casualties (Sohn, 2020). Casualties resulted from people being pinned under fallen infrastructure and becoming submerged in the viscous liquid (Lyons, 2009). According to Encyclopaedia Britannica, rescue efforts would have been easier if the weather was warmer.

It is clear that viscosity has many everyday applications and an interesting but tragic connection to history.

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Hypothesis: That the marble will take the longest amount of time to reach the finishing line when the jar contains molasses, followed by golden syrup, corn syrup and honey. This is because molasses will have a higher viscosity than the other liquids as it is very thick which will create resistance, causing the marble to fall slowly. This hypothesis is based on the background information that molasses has the highest density of the four liquids, of 1600 kg/m3, which means it is a compact liquid, which will create resistance against the marble and cause it to fall slowly.

Variables: Variable

Information

How the variable was controlled

Independent

The viscosity of each liquid (honey, corn syrup, golden syrup and molasses), measured in Newton-seconds per meter squared (Ns/m2).

N/A

Dependent

The amount of time it takes for the marble to reach the finishing line, measured in seconds.

N/A

Controlled Variables

Temperature of the liquid

The temperature of each liquid was measured before each trial, to ensure the liquids were the same temperature for each trial.

Volume of the liquid

The volume of the liquid (350mL) was measured using a measuring jug.

Size of marble

The same sized marble was used in all liquids and trials.

Mass of marble

The mass of each marble was the same (5g).

Type of jar

Four identical transparent glass jars of height 17cm and diameter 6cm were used.

Height at which the marble is dropped

The height at which each marble was dropped was the same (17cm).

Equipment and materials: ❖ 4 identical transparent glass jars of height 17cm and diameter 6cm ❖ 4 identical marbles of diameter 1.5cm and mass 5g

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❖ ❖ ❖ ❖ ❖ ❖ ❖ ❖ ❖ ❖ ❖ ❖ ❖

1 measuring jug of capacity 500mL 4 tablespoons 1 stopwatch 1 black marker pen 1 ruler (30cm) 1 thermometer 1 lab coat 1 pair of safety glasses 1 tea towel (30cm by 40cm) 350mL honey 350mL corn syrup 350mL golden syrup 350mL molasses

Please note that this experiment also requires a table or a flat surface, and access to a sink with running water.

Risk Assessment: Risk

Precaution

Broken glass - If the glass jars or the marbles break, broken glass may cut any exposed skin.

To prevent the glass jars and marbles from breaking, handle them carefully. A lab coat will also reduce the amount of skin exposed and therefore reduce one’s chance of becoming injured. Safety glasses must also be worn to protect the face.

If glass does break, ensure it is immediately and thoroughly swept up using a dustpan and broom.

Substance spills - If the honey, corn syrup, golden syrup or molasses spill on the floor, they will become a safety hazard and may cause injury.

To prevent honey, corn syrup, golden syrup or molasses from spilling, handle the substances carefully and inspect all jars and containers for leaks, cracks or evidence of deterioration before conducting the experiment. A lab coat and safety glasses must be worn during the experiment to prevent the substances from irritating the eyes and skin.

Method: 1. Four jars were carefully placed on the table.

2. 350mL of honey was measured using the measuring jug, and poured into the first jar.

3. The measuring jug was washed thoroughly and dried.

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4. 350mL of corn syrup was measured using the measuring jug, and poured into the second jar.

5. The measuring jug was washed thoroughly and dried.

6. 350mL of golden syrup was measured using the measuring jug, and poured into the third jar.

7. The measuring jug was washed thoroughly and dried.

8. 350mL of molasses was measured using the measuring jug, and poured into the fourth jar.

9. On each jar, a starting line was drawn 2cm below the top of the surface of the liquid using a marker pen (as shown in Figure 1).

10. On each jar, a finishing line was drawn 5cm above the bottom of the jar using a marker pen (as shown in Figure 1).

Figure 1

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11. The distance between the starting line and finishing line was measured and recorded for each jar to ensure they were the same distance apart, and the following table was drawn (Table 1): Table 1 Distance between starting and finishing line (cm to 2 d.p.)

Honey Liquid

Corn syrup Golden syrup Molasses

12. The equipment setup was checked to ensure that it matched the following diagram, Figure 2.

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13. The following table (Table 2) was drawn.

Table 2 Time taken for the marble to travel from the starting line to the finishing line (seconds to 2 d.p.)

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Average

Honey Corn syrup Liquid Golden syrup Molasses 14. The following table (Table 3) was drawn.

Table 3 Temperature of the liquid before each trial (℃ to the nearest degree) Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Average

Honey Corn syrup Liquid Golden syrup Molasses 15. The following table (Table 4) was drawn.

Table 4 Qualitative observations made during the experimental procedure Honey Trial

Observations

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Pre-trial 1 2 3 4 5 Corn syrup Trial

Observations

Pre-trial 1 2 3 4 5 Golden syrup Trial

Observations

Pre-trial 1 2 3 4 5 Molasses Trial

Observations

Pre-trial 1 2

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3 4 5 16. The temperature of the liquid in the first jar was measured using a thermometer and recorded in Table 3.

17. The thermometer was washed thoroughly and dried.

18. A marble was dropped into the middle of the first jar from a height equal to the rim of the jar.

19. At eye-level, when the marble reached the starting line, the stopwatch was started.

20. When the marble reached the finishing line, the stopwatch was stopped.

21. The time on the stopwatch was recorded in Table 2 and the stopwatch was reset.

22. Qualitative observations were recorded in Table 4.

23. Using a tablespoon, the marble was retrieved from the jar and thoroughly washed and dried.

24. Steps 16-23 were repeated 4 more times.

25. Steps 16-24 were repeated for the other three jars.

26. The average time taken for the marble to travel from the starting line to the finishing line was calculated and recorded in Table 2.

27. The average temperature of the liquid was calculated and recorded in Table 3.

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Results: The results of the experiment are shown in the tables and graphs below.

Table 1 Distance between starting and finishing line (cm to 2 d.p.)

Liquid

Honey

5.50

Corn syrup

5.50

Golden syrup

5.50

Molasses

5.50 Table 2 Time taken for the marble to travel from the starting line to the finishing line (seconds to 2 d.p.)

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Average

Honey

15.56

15.13

16.17

16.39

16.05

15.86

Corn syrup

104.80

101.42

100.04

103.50

99.51

101.85

16.95

16.89

16.98

16.90

16.22

16.79

2.92

2.15

3.05

2.02

1.92

2.41

Liquid Golden syrup Molasses

Table 3 Temperature of the liquid before each trial (℃ to the nearest degree)

Liquid

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Average

Honey

20

20

20

20

20

20

Corn syrup

20

20

20

20

20

20

Golden syrup

20

20

20

20

20

20

Molasses

20

20

20

20

20

20

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Table 4 Qualitative observations made during the experimental procedure Honey Trial

Observations

Pre-trial

The honey had a strong smell and was sticky. The liquid was brown in colour and transparent.

1

When the marble was dropped in the honey, the marble moved rather quickly at first, then settled into a steady, slow speed by the time it passed the starting line. The marble then continued to fall slowly.

2

As the marble entered the honey, a large hole appeared where the marble was and the honey sank with the marble. When the marble was fully submerged, the hole disappeared and the honey became level again. The marble fell very slowly.

3

This trial seemed slightly slower than Trial 1 and Trial 2. The marble fell at a very slow rate and small bubbles began to appear in the liquid.

4

The marble seemed to fall very slowly through the honey and more small bubbles appeared.

5

The marble slowly sank through the honey and several small bubbles were visible.

Corn syrup

Trial

Observations

Pre-trial

The corn syrup was considerably thicker than the honey and also rather sticky. There was no apparent scent. The corn syrup itself was transparent and colourless.

1

When the marble was dropped into the corn syrup, there was almost no movement at all and the marble fell extremely slowly.

2

The marble slowly sank through the corn syrup and small bubbles became visible. The marble seemed to fall slightly faster than the previous trial.

3

The marble fell very slowly through the corn syrup and even more bubbles appeared in the thick liquid.

4

The marble fell extremely slowly through the liquid and some more bubbles appeared in the corn syrup.

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5

In this trial, the marble continued to move very slowly through the corn syrup, but it seemed to be slightly faster than the previous trials. Lots of small bubbles were visible during this trial.

Golden syrup

Trial

Observations

Pre-trial

The golden syrup was thick, yellowy-brown in colour and a sweet odour was present.

1

When the marble was dropped in the liquid, it fell quickly at first, then settled into a steady, slow pace before it passed the starting line. The marble continued its slow descent past the finishing line.

2

The marble fell through the liquid in a similar fashion to the previous trial.

No bubbles were visible.

3

As the marble slowly fell through the liquid, small bubbles appeared.

4

The marble slowly sank through the golden syrup and small bubbles became visible. The marble seemed to fall slightly faster than in the previous trial.

5

More bubbles appeared in this trial when the marble fell through the golden syrup. This trial seemed to be faster than all of the previous trials.

Molasses

Trial

Observations

Pre-trial

The molasses was a dark, barely transparent liquid that had a strong odour.

It was rather thin and sticky.

1

The dark colour of the molasses made it slightly difficult to see the marble.

Luckily the marble was brightly coloured so it was visible as it fell through the liquid. The marble seemed to fall very quickly.

2

In this trial, the marble seemed to fall very quickly through the molasses and no visible bubbles occurred in the molasses liquid itself.

3

The marble appeared to fall very fast through the molasses. Again, no bubbles were visible.

4

When the marble was dropped into the molasses, it appeared to fall very quickly. No bubbles were visible.

5

In this trial, the marble appeared to fall faster than in all of the previous trials. The marble continued to move very quickly through the molasses.

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Qualitative observations - photographs Honey

Corn syrup

43

Golden syrup

Molasses

44

Processed data: Table 5 The average and the standard deviation of the results of the time taken for the marble to travel from the starting line to the finishing line (to 2 d.p.)

Liquid

Average (s)

Standard deviation

Honey

15.86

0.51

Corn syrup

101.85

2.26

Golden syrup

16.79

0.32

Molasses

2.41

0.53

The standard deviation values were calculated using the STDEV function in Microsoft Excel.

This function has the following formula: Standard deviation = where

is the sample mean and n is the sample size.

Figure 3

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Figure 4

Figure 5

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Calculating viscosity: The following equation was used to calculate the viscosity of each liquid:

where: μ is the viscosity of the liquid in Newton-seconds per meter squared (Ns/m2). Δρ is the difference in density between the marble and the liquid in kilograms per meter cubed (kg/m3). g is the acceleration due to gravity, 9.81 meters per second squared (m/s2). a is the radius of the marble in meters (m). v is the average velocity (the distance the marble falls, divided by the time it takes to fall, in meters per second (m/s)).

Calculating the density of the marble 1. Mass of marble is 5g which equals 0.005kg 2. Diameter of the marble is 1.5cm so the radius is half the diameter, which equals 0.75cm or 0.0075m 3. Volume of sphere = 4/3πr3 Therefore, V = 4/3 x π x 0.0075 3 V = 0.00000176714 m3 4. Density = mass / volume Therefore, density of the marble = 0.005kg / 0.00000176714 m3 Density = 2829.421211 kg/m3 Density of each liquid (sourced from background research) The density of honey is 1360 kg/m3 The density of corn syrup is 1380 kg/m3 The density of golden syrup is 1430 kg/m3 The density of molasses is 1600 kg/m3 Calculating average velocity of the marble The average time taken for the marble to travel from the starting line to the finishing line (a distance of 5.5cm) was used to calculate the average velocity of the marble in each liquid, as follows: Velocity of the marble in honey Velocity = distance / time V = 0.055m / 15.86s V = 0.055 / 15.86 m/s

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Velocity of the marble in corn syrup Velocity = distance / time V = 0.055m / 101.85s V = 0.055 / 101.85 m/s Velocity of the marble in golden syrup Velocity = distance / time V = 0.055m / 16.79s V = 0.055 / 16.79 m/s Velocity of the marble in molasses Velocity = distance / time V = 0.055m / 2.41s V = 0.055 / 2.41 m/s Calculating the viscosity of each liquid The viscosity of each liquid was calculated by substituting the appropriate values into the equation

Honey Viscosity = (2 x (marble density - honey density) x g x a^2) / (9 x (0.055 / time)) Viscosity = (2 x (2829.421211 - 1360) x 9.81 x 0.0075^2) / (9 x (0.055 / 15.86)) Viscosity = 51.95960232 Viscosity = 51.96 Ns/m2 Corn syrup Viscosity = (2 x (marble density - corn syrup density) x g x a^2) / (9 x (0.055 / time)) Viscosity = (2 x (2829.421211 - 1380) x 9.81 x 0.0075^2) / (9 x (0.055 / 101.85)) Viscosity = 329.1334156 Viscosity = 329.13 Ns/m2 Golden syrup Viscosity = (2 x (marble density - golden syrup density) x g x a^2) / (9 x (0.055 / time)) Viscosity = (2 x (2829.421211 - 1430) x 9.81 x 0.0075^2) / (9 x (0.055 / 16.79)) Viscosity = 57.62463193 Viscosity = 57.62 Ns/m2 Molasses Viscosity = (2 x (marble density - molasses density) x g x a^2) / (9 x (0.055 / time)) Viscosity = (2 x (2829.421211 - 1600) x 9.81 x 0.0075^2) / (9 x (0.055 / 2.41)) Viscosity = 7.266524803 Viscosity = 7.27 Ns/m2 48

Therefore, the viscosity of each liquid in descending order, rounded to 2 decimal places, is: corn syrup (329.13 Ns/m2), golden syrup (57.62 Ns/m2), honey (51.96 Ns/m2) and molasses (7.27 Ns/m2).

Discussion: Table 2 shows the time taken for the marble to travel from the starting line to the finishing line during the five trials in each liquid. The data reveals that the average time taken for the marble to reach the finishing line in each liquid was as follows (in descending order): 101.85s for corn syrup, 16.79s for golden syrup, 15.86s for honey and 2.41s for molasses. Table 5 and Figure 5 contain the standard deviation of these results. The standard deviation indicates the spread of the data. The data from the corn syrup trials had the highest standard deviation, 2.26. This was followed by the standard deviation for molasses, 0.53, and the standard deviation for honey, 0.51. Lastly, the smallest standard deviation was for golden syrup, 0.32.

As the standard deviation for each liquid was small, this reveals that there was little variation in the data collected and the data is therefore more likely to be reliable.

Table 1 confirms that the distance between the starting line and finishing line was consistent over all jars, and was 5.50cm. Table 3 confirms that the temperature of the liquid before each trial in each liquid was consistent, at 20℃. These were two important controlled variables which increased the validity of the experiment. The background research emphasised the importance of controlling temperature as when the temperature of a liquid increases, its density decreases and the liquid becomes less viscous (Druin, 1968).

Several trends arose from Figure 3 and Figure 4, as can be seen from the height of the columns in these column graphs. For example, it is evident that the marble took the longest amount of time to travel from the starting line to the finishing line on average in corn syrup, by a significant margin, as it is by far the tallest column in the graphs. Additionally, the average results for honey and golden syrup were similar, being only 0.93s apart, as shown by the columns of similar height. Finally, another trend is that the marble took the shortest amount of time to travel from the starting line to the finishing line on average in molasses, as indicated by the shortest column.

The following equation from the background research (Science Buddies, n.d.b) was used to calculate the viscosity of each liquid:

Substituting the relevant information into the equation revealed that the viscosity of each liquid in descending order is: corn syrup (329.13 Ns/m2), golden syrup (57.62 Ns/m2), honey (51.96 Ns/m2) and molasses (7.27 Ns/m2). This order reflects that of the average time taken for the marble to reach the finishing line in each liquid. This also is supported by the background research, which states that when a marble is dropped into multiple liquids of

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different viscosities, the velocity of the marble will be slowest in the liquid that has the highest viscosity (Education.com, n.d.).

Evaluation: The results from this experiment are reliable because each trial was repeated 5 times and the data collected was in close range of the other values, which suggests consistency and reliability. This is further supported by the low standard deviations calculated in Table 5. The experiment conducted was valid because all the variables apart from the independent and dependent variables were controlled. Furthermore, all measurements were precise, using equipment such as measuring jugs and thermometers, and the recorded data is therefore valid.

The data was also accurately displayed and a conclusion, fulfilling the aim of the experiment, could be formed based on the data. The method ultimately allowed appropriate data to be collected to either support or refute the hypothesis.

However, three main sources of error were identified in this experiment: 1. When the marble was removed, some liquid was also removed alongside the marble.

This caused less than 350mL to be remaining within the jar after each trial and may have impacted the time at which terminal velocity was achieved by the marble, overall impacting the data collected.

2. This experiment relies on the reaction time of the person timing the marble’s descent from the starting line to the finishing time. Reaction time is not very reliable because there are many factors that can impact it, such as amount of sleep, age and caffeine intake (Taheri & Arabameri, 2012). The average human reaction time is approximately 250 milliseconds (Ackerman, 2019). This makes the data collected less accurate.

3. During the third or fourth trial for all liquids except molasses, small bubbles started to appear. These were likely air bubbles caused by the marble moving through the liquid.

These air bubbles impacted the consistency of the liquid, which may have affected the overall results of the experiment.

To address these three sources of error, the following modifications could be made: 1. Before conducting the experiment, the 350mL line could be marked on the jar. This would enable the jars to be supplemented with additional liquid, if required, to ensure 350mL is in the jar before each trial.

2. To eliminate the experiment’s reliance on human reaction time, each trial could be filmed and the video timestamps would be used to determine the amount of time taken for the marble to fall from the starting line to the finishing line.

3. After each trial, the liquid must be left to settle, waiting until no bubbles are visible, before conducting the next trial. This will prevent bubbles from impacting the trial’s results.

Conclusion: In this experiment, the hypothesis was refuted. The hypothesis stated that the marble will take the longest amount of time to reach the finishing line when the jar contains molasses, 50

followed by golden syrup, corn syrup and honey. However, the experimental results indicated that the marble took the longest amount of time to reach the finishing line in corn syrup (101.85s), followed by golden syrup (16.79s), honey (15.86s) and molasses (2.41s), which did not support the hypothesis. Furthermore, this experiment concluded that the viscosity of each liquid in descending order is: corn syrup (329.13 Ns/m2), golden syrup (57.62 Ns/m2), honey (51.96 Ns/m2) and molasses (7.27 Ns/m2). The aim of the experiment was fulfilled as the viscosity of the four liquids was measured and compared.

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References Ackerman, E. (2019). Upgrade to Superhuman Reflexes Without Feeling Like a Robot. https://spectrum.ieee.org/enabling-superhuman-reflexes-without-feeling-like-a-robot Ahmad, B. (2019). Measuring Viscosity of Different Fluids Using Ball Drop Method. https://www.researchgate.net/publication/337707277_MEASURING_VISCOSITY_OF _DIFFERENT_FLUIDS_USING_BALL_DROP_METHOD Ali, H.S., Al-Zuky, A.A., Al-Saleh, A.H., & Mohamad, J.H. (2019). Measure liquid viscosity by tracking falling ball automatically depending on image processing algorithm.

Journal of Physics: Conference Series. 1294(2), 1-15. https://doi.org/10.1088/1742-6596/1294/2/022002 AZoM. (2013). How does Temperature Change Viscosity in Liquids and Gases? https://www.azom.com/article.aspx?ArticleID=10036 Cain, F. (2010). What Is Terminal Velocity? https://www.universetoday.com/73617/terminal-velocity/ Cool Science Experiments Headquarters. (2016, May 3). How to test the Viscosity of a Liquid [Video]. YouTube. https://www.youtube.com/watch?v=2Gdxu4XcsbY&t=116s Cool Science Experiments Headquarters. (2020). Viscosity of Liquids Science Experiment. https://coolscienceexperimentshq.com/viscosity-of-a-liquid-experiment/#Video Druin, M.L. (1968). Viscosity-density correlation of Newtonian liquids [PDF]. https://digitalcommons.njit.edu/cgi/viewcontent.cgi?article=2388&context=dissertation s Education.com. (n.d.). Marble Race: Finding Viscosity of Fluids. https://www.education.com/science-fair/article/compare-viscosity-fluids/ Elert, G. (n.d.). Density - The Physics Hypertextbook. https://physics.info/density/ Great Molasses Flood. (2021). In Encyclopaedia Britannica. https://www.britannica.com/topic/Great-Molasses-Flood Ito, M.A. (2012). Marble Viscosity Race [PDF]. https://csef.usc.edu/History/2012/Projects/J0113.pdf Lyons, C. (2009). A Sticky Tragedy: The Boston Molasses Disaster. https://www.historytoday.com/archive/sticky-tragedy-boston-molasses-disaster

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Mueller, P. (2018). Let It Flow: How to Calculate Viscosity. https://www.powerblanket.com/blog/let-it-flow/ National Aeronautics and Space Administration (NASA). (2021). Reynold’s Number. https://www.grc.nasa.gov/www/BGH/reynolds.html Numerade. (n.d.). The Density of Molasses. https://www.numerade.com/questions/the-density-of-molasses-is-1600-mathrmkg-math rmm3-find-the-mass-of-the-molasses-in-a-075-l-jar-2/ Pearson. (2017). Fluid Mechanics for Chemical Engineers. https://www.informit.com/articles/article.aspx?p=2832417&seqNum=4 Princeton University. (n.d.). Definition of Viscosity. https://www.princeton.edu/~gasdyn/Research/T-C_Research_Folder/Viscosity_def.html QS Study. (n.d.). Practical Applications of Viscosity. https://qsstudy.com/physics/practical-applications-of-viscosity Robb, A. (2016). Viscosity Experiment. https://study.com/academy/lesson/viscosity-experiment.html Rust, A. (n.d.). Syrup. https://sites.google.com/site/volcanologyanalogues/home/syrup Schellart, W.P. (2011). Rheology and density of glucose syrup and honey: Determining their suitability for usage in analogue and fluid dynamic models of geological processes.

Journal of Structural Geology, 33(6), 1079-1088. https://doi.org/10.1016/j.jsg.2011.03.013.

Science Buddies. (2015, August 27). Marble Race--in Liquid! Scientific American. https://www.scientificamerican.com/article/marble-race-in-liquid/ Science Buddies. (n.d.a). Race Your Marbles to Discover a Liquid's Viscosity - Procedure. https://www.sciencebuddies.org/science-fair-projects/project-ideas/Chem_p055/chemist ry/race-your-marbles-to-discover-liquids-viscosity#procedure Science Buddies. (n.d.b). Race Your Marbles to Discover a Liquid's Viscosity - Background. https://www.sciencebuddies.org/science-fair-projects/project-ideas/Chem_p055/chemist ry/race-your-marbles-to-discover-liquids-viscosity#background SoftSchools. (n.d.). Reynold's Number Formula. https://www.softschools.com/formulas/physics/reynolds_number_formula/78/

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Sohn, E. (2020). Why the Great Molasses Flood Was So Deadly. https://www.history.com/news/great-/molasses-flood-science Taheri, M., & Arabameri, E. (2012). The effect of sleep deprivation on choice reaction time and anaerobic power of college student athletes. Asian Journal of Sports Medicine, 3(1), 15–20. https://doi.org/10.5812/asjsm.34719 Tang, X.J. (2016). Measurements of fluid viscosity using a miniature ball drop device. AIP Publishing. 87(5), 054301. https://doi.org/10.1063/1.4948314 ThoughtCo. (n.d.). What Is Viscosity in Physics? https://www.thoughtco.com/viscosity-2699336 Warnant, P., Arena, P., Burrows, K., Lofts, G., Evergreen, M. J. (2017). Jacaranda Core Science Stage 5 NSW Australian curriculum. (2nd ed.). Milton: John Wiley & Sons Australia.

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SRP - The effect of sugar concentration on fermentation Sophie Rigon Aim: To investigate the effect of increased sugar concentration on the rate of fermentation with yeast by measuring the percentage of alcohol produced by the fermentation of liquids containing differing concentrations of sugar.

Background Research: Fermentation is an enzymatic process in which cells obtain energy from carbohydrates, even when there is an absence of oxygen (Helmenstine, 2020). During this process, enzymes of particular microorganisms, such as bacteria and fungi, break down sugar metabolites, thereby gaining energy (Tay, 2019). Glycolysis, which is the “metabolic pathway that converts glucose (a type of sugar) into pyruvate,” is one of the first main steps of fermentation (Alba-Lois & Segal-Kischinevsky, 2010). During glycolysis, glucose is converted into pyruvic acid, and enters a series of chemical reactions known as the citric acid cycle if oxygen is available. After undergoing these reactions, the acid proceeds to the respiratory chain, causing cells to produce “36-38 molecules of ATP for each molecule of glucose oxidised.” (Alba-Lois & Segal-Kischinevsky, 2010). However, in the absence of oxygen, the pyruvate produced during glycolysis does not continue through oxidation as well as the citric acid cycle, and the electron transport train does not occur (Khan Academy 2021). In this case, pyruvic acid can either be converted into ethanol (alcohol) and carbon dioxide through ethanol fermentation, where pyruvate in sugars are broken down by yeasts, producing alcoholic drinks and bread, or be converted into lactate through lactic acid fermentation, where starches or sugars are broken down to produce lactic acid and other waste products (Alba-Lois & Segal-Kischinevsky, 2010). The type of fermentation pyruvic acid takes part in is dependent upon the type of cell, however, this investigation will focus on ethanol fermentation rather than lactic acid fermentation.

Alternative metabolic routes following glycolysis.

(Alba-Lois & Segal-Kischinevsky, 2010) Yeast and other types of microorganisms can be used to break down pyruvic acid in order to produce ethanol (Alba-Lois & Segal-Kischinevsky, 2010). As eukaryotic microorganisms, yeasts perform fermentation by converting sugar into alcohol through which they are able to obtain energy (Maicas, 2020). Yeasts and ethanol fermentation are primarily used to produce beer, wine, and other alcoholic beverages, as well as bread (Helmenstine, 2020).

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During ethanol fermentation, pyruvate is broken down into ethanol and carbon dioxide. The net chemical equation for this process and the production of ethanol from glucose is: C6H12O6 (glucose) → 2C2H5OH (ethanol) + 2CO2 (carbon dioxide) Glycolysis of a glucose molecule produces two molecules of pyruvic acid, which are then reduced to two molecules of ethanol and carbon dioxide under anaerobic conditions (Malaker et al., 2020). Specifically, this process of breaking down glucose into ethanol involves a carboxyl group being removed from the pyruvate, which is subsequently released as carbon dioxide. This produces acetaldehyde, a two-carbon molecule, which causes ethanol to be formed after NADH passes its electrons to acetaldehyde, thereby regenerating NAD+ (Khan Academy, 2021).

Chemical process of ethanol fermentation. (Wikipedia, 2021) Sugar is one of the primary factors that can impact and affect the rate of fermentation. In particular, the concentration of sugar, “increases the fermentation production in yeast, until the saturation gradient is reached causing a stop in carbon dioxide,” and ethanol (Bryan et al., 2018). Sugar affects the rate of fermentation due to the fact that it is an input to cellular respiration, and aids the process of ethanol fermentation. Specifically, yeast is a fungus, meaning that a supply of energy is needed for its living and growth (Hewitson & Hill, 2018). This energy required by the yeast is supplied through sugar. Yeast is able to release the energy from sugar during fermentation, meaning that the yeast is more active if there are higher sugar levels, and will be faster in its growth up to a particular point (Hewitson & Hill, 2018).

Additionally, an increased level of sugar means that there is a greater availability of the reactant, in this case sugar, within the chemical reaction which thereby increases the rate of fermentation of yeast.

Hypothesis: The yeast placed in the liquid with the highest sugar concentration will produce a greater percentage of alcohol than liquids with lower sugar concentrations, meaning that it would have the highest rate of fermentation. This will be due to the fact that there is a greater availability of the reactant in the chemical reaction in which alcohol is produced, thereby increasing the rate of fermentation.

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Variables: Independent variable: The independent variable in this experiment is the sugar concentration of the liquids. This will be changed by using different types of fruit juices with differing concentrations of sugar.

Dependent variable: The dependent variable in this investigation is the percentage of alcohol produced during fermentation. This will be measured using a triple-scale hydrometer, which measures the specific gravity of a liquid, which is the ratio of a liquid’s density to that of water. From these measurements of the specific gravity of the liquid the percentage of alcohol can be calculated.

Controlled variables: ● Volume of fruit juice: This variable can be controlled by using a measuring cylinder to measure the same volume of fruit juice. ● Type and mass of yeast: This variable can be controlled by weighing the yeast using an electronic scale before placing it into the fruit juice, and ensuring that the same type of yeast is used for all trials. ● Size of the containers: This variable can be controlled by using the same size and type of containers for each trial. ● Amount of time over which the dependent variable is measured: This variable can be controlled by beginning each trial at the same or very similar time and ensuring that the amount of time between the initial and final measurements of the dependent variable is the same for each trial.

Equipment and Materials: ● 300mL tap water ● 300mL unsweetened apple juice ● 300mL cranberry juice ● 300mL grape juice ● 24g baker’s yeast ● 1x triple-scale hydrometer ● 12x 500mL plastic containers ● 1x measuring cylinder ● 1x electronic scale ● 1x large test tube Risk Assessment: Potential Risk

Precaution

Glass from triple-scale hydrometer causes cuts to hands and other body parts as a result of the triple-scale hydrometer being dropped.

Handle the triple-scale hydrometer with caution, use the instrument correctly, and keep in the centre of the workbench to prevent it from falling or being pushed off. Use a dustpan to clean up broken glass in the case that the triple-scale hydrometer is broken.

Slipping and injuries to the body as a result of liquids spilling.

Handle liquids with caution to prevent spillage, and ensure that they are in the containers at all

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times. Keep containers in the centre/away from the edges of the workbench to prevent them from falling off. In the case that the liquids are spilled, immediately clean up safely using absorbent towels.

Method: 1. 100mL of grape juice was measured and placed into a plastic container.

2. Step 2 was repeated twice; 100mL of grape juice was measured and placed into a plastic container, which was then repeated.

3. Steps 2-3 were repeated, with unsweetened apple juice, cranberry juice and water being used in place of grape juice.

4. The initial specific gravity of each liquid was measured using a triple-scale hydrometer.

5. 1g of baker’s yeast was added to each container containing the liquids.

6. The final specific gravity of the liquids in each container was measured using a triple-scale hydrometer six days after the initial specific gravity was measured.

7. The final percentage of alcohol of each liquid was calculated when the final specific gravity was subtracted from the initial specific gravity of each liquid, and then multiplied by 131.25.

Diagrams and photographs:

Set up of investigation before yeast was added to each container

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Measuring the specific gravity using a triple scale hydrometer Results: Table 1 - Initial specific gravity of each liquid: Concentration of sugar (%)

Initial specific gravity Trial 1

Trial 2

Trial 3

0.0

1.000

1.000

1.000

10.8

1.046

1.046

1.046

11.3

1.046

1.046

1.046

13.7

1.060

1.060

1.060

Table 2 - Final specific gravity of each liquid: Concentration of sugar (%)

Final specific gravity of each liquid (after six days) Trial 1

Trial 2

Trial 3

0.0

1.001

1.002

1.000

10.8

0.998

1.000

1.000

11.3

0.990

0.990

1.000

13.7

1.004

1.001

1.002

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Percentage of alcohol of each liquid: The final percentage of alcohol of each liquid was calculated using the following formula: Percentage of alcohol (%) = (Initial specific gravity - Final specific gravity) x 131.25 Table 3 - Final percentage of alcohol of each liquid: Concentration of sugar (%)

Final percentage of alcohol of each liquid (%) Trial 1

Trial 2

Trial 3

Average

0.0

-0.132

-0.263

0

-0.132

10.8

6.300

6.036

6.036

6.124

11.3

7.350

7.350

6.036

6.912

13.7

7.350

7.740

7.613

7.568

Qualitative observations: ● A few hours after the yeast had been added to each container, each liquid had a layer of foam, except for the water, where the yeast remained at the bottom of the container. This layer of foam indicated that the yeast was active. ● The foam at the top of each liquid decreased significantly two - three days after the yeast was added to each container, with the majority of the yeast that was still visible being located on the bottom of each container. ● Each liquid significantly discoloured and became quite cloudy throughout the experiment in comparison to the initial colour of these liquids as a result of the added yeast. In particular, the water became a murky brown colour. ● On the sixth day of the experiment, the yeast had not fully dissolved within each liquid.

Additionally, a soft substance had formed at the bottom of each container which was particularly noticeable when the liquid was poured into the test tube so that the final specific gravity could be measured.

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Graphs:

Figure 1: Effect of sugar concentration on fermentation (hand drawn graph)

Figure 2: The effect of sugar concentration on fermentation (Excel graph)

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Discussion: Describing and explaining trends: The results of this experiment demonstrate that liquids containing higher sugar concentrations produce higher percentages of alcohol when fermentation with yeast takes place. The trend shown in both graphs supports this; the liquid that had a sugar concentration of 13.7% (grape juice), which was the highest sugar concentration used in the investigation, produced the greatest average percentage of alcohol, meaning that it had the fastest rate of fermentation. Specifically, the percentage of alcohol produced by this liquid was 7.568%. Conversely, the liquid with the lowest sugar concentration of 0% (water) produced the lowest average percentage of alcohol of -0.132%. However, this value was unexpected in regards to the negative value of the percentage of alcohol produced during trials involving water. In particular, due to the fact that the sugar concentration of the water is 0%, it was expected that the final percentage of alcohol would be equal to or slightly greater than 0%. Additionally, a negative value for the percentage of alcohol is impossible as this value must always either be positive or equal to 0%. Notwithstanding this possible inaccuracy, the results of the experiment clearly show that higher concentrations of sugar produce higher percentages of alcohol during fermentation with yeast, meaning that the rate of fermentation is faster, as reinforced by the positive gradient of both graphs which demonstrate that there is a strong positive correlation between sugar concentration and the percentage of alcohol produced during fermentation.

These results and the trends shown through the data and graphs relate to the background research of this report in regards to how sugar concentration affects the rate of fermentation. In particular, through the background research, it was discovered that sugar is an input to cellular respiration and fermentation, and is a reactant within this chemical reaction, meaning that higher concentrations of sugar will produce greater levels of alcohol as a result of the greater availability of the reactant (sugar).

This corresponds with the results of this investigation, as the liquid with the highest sugar concentration of 13.7% (grape juice) produced the greatest percentage of alcohol (7.568%) when compared to the liquids with lower concentrations of sugar.

Evaluation: The results gained and collected during this investigation were reliable, as three trials of each change to the independent variable were completed. The results of each trial were, for the most part, very similar.

Hence, the results of this experiment were reliable as the repetition of the experiment produced very similar results, however, the results of this investigation would have been more reliable if the experiment had been completed many more times.

This experiment was valid, as the method investigates and collects results regarding the percentage of alcohol produced during fermentation with liquids containing different concentrations of sugar, which is the aim of the investigation. It also uses the correct materials, successfully addresses the independent and dependent variables, and controls the variables that are required to be consistent and controlled throughout the experiment, however, there were some issues in regards to inconsistent measurements of the mass of the yeast placed in each liquid, which was one of the controlled variables, as a result of the inaccurate electronic scales used to weigh the yeast. Thus, while there were issues in regards to the accuracy of certain equipment and instruments, this experiment was valid due to the way in which the method successfully tests the aim of this investigation.

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One of the primary problems that was faced in this experiment was the use of tap water rather than distilled water for the controlled set up of the experiment, which most likely resulted in the negative value of the average final percentage of alcohol produced during fermentation with this liquid. This problem would have affected the measurements and results of these trials as the additional chemicals that are added to tap water by water companies could have possibly affected the rate of fermentation as well as the final readings. This use of tap water rather than distilled water would have impacted the final percentage of alcohol produced during each trial as the unknown chemicals present within tap water could potentially affect and hinder the rate of fermentation. Furthermore, another problem that was encountered during this experiment was that the mass of the yeast was not controlled across all trials as a result of the inaccuracy of the electronic scale being used. In particular, the electronic scale did not record decimal places and occasionally had difficulty picking up the weight of yeast, which would have therefore affected the results of the experiment as the mass of the yeast used during each trial differed slightly. Hence, the final percentage of alcohol produced during each trial would have been affected by the differing masses of yeast used due to the fact that greater or lesser amounts of yeast can increase or decrease the rate of fermentation respectively.

If this experiment was to be carried out again, distilled water would be used in place of tap water so that the chemicals present within tap water could not impact the rate of fermentation during the trials involving water. Additionally, a more accurate scientific electronic scale that records decimal places would be used rather than a standard kitchen scale in order to ensure that the mass of the yeast is consistent and controlled throughout each of the trials, thereby ensuring that the results of the experiment are fair and accurate.

Conclusion: The hypothesis of this experiment was supported as the liquids with higher concentrations of sugar produced the greatest percentage of alcohol during the process of fermentation when compared to the liquids with lower concentrations of sugar. This is evident through the results of the investigation, as the grape juice, which had the highest sugar concentration of 13.7%, produced the greatest percentage of alcohol, 7.568%, whereas the liquids with lower sugar concentrations produced lower percentages of alcohol during fermentation with yeast.

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Reference list: Alba-Lois, L. & Segal-Kischinevzky, C. (2010). Yeast Fermentation and the Making of Beer and Wine.

Nature Education. 3(9). https://www.nature.com/scitable/topicpage/yeast-fermentation-and-the-making-of-beer-14372 813/ Bryan, A., Hart, C., Wise, M. & Roberts, B. (2018). Glucose Concentrations Effect on Rate of Fermentation in Yeast. Journal of Undergraduate Biology Laboratory Investigations. https://undergradsciencejournals.okstate.edu/index.php/JUBLI/article/view/8738 Helmenstine, A. (2020). What is Fermentation in Chemistry? https://www.thoughtco.com/what-is-fermentation-608199 Hewitson, J. & Hill, C. (2018). How Does Sugar Affect Yeast Growth? https://www.saps.org.uk/saps-associates/browse-q-and-a/169-q-a-a-how-does-sugar-affect-yea st-growth Khan Academy. (2021). Fermentation and anaerobic respiration. https://www.khanacademy.org/science/ap-biology/cellular-energetics/cellular-respiration-ap/a/ fermentation-and-anaerobic-respiration Maicas, S. (2020). The Role of Yeasts in Fermentation Processes. Microorganisms. 8(8). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7466055/ Malakar, S., Kr Sanjib, S. & Jolvis Pou, K.R. (2020). Biotechnological Interventions in Beverage Production. The Science of Beverages. https://doi.org/10.1016/B978-0-12-816678-9.00001-1 Tay, A. (2019). The Science of Fermentation. https://www.labmanager.com/insights/the-science-of-fermentation-1432 Warnant, P., Arena, P., Burrows, K., Lofts, G., & Evergreen, M.J. (2018). Jacaranda Core Science 5.

John Wiley & Sons Australia.

Wikipedia. (2021). Ethanol fermentation. https://en.wikipedia.org/wiki/Ethanol_fermentation

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Scientific Report - SRP - Maree Sialepis Investigating the relationship between different colour noises and the amount of deep sleep they stimulate Aim: To investigate the effect of pink, brown and white noise on the duration of deep sleep found through a percentage of overall sleep.

Background Research Deep sleep allows the brain to effectively store memories and information as well as affecting its ability to recall information (Medical News Today, 2019). According to Suruchi Chandra M.D Integrative Psychiatry and Medicine (2021), “During sleep, our brains produce slower brain waves that allow the body and brain to enter a state of rest and restoration. There are fewer of the faster brain waves that occur during periods of focus and concentration.’’ Colour noises are able to amplify the slow rate at which the brain waves travel, stimulating more deep sleep. When brain waves slow down, this is accompanied by a relaxation of muscles including the eyes. Heart rate, blood pressure and body temperature will also decrease resulting in an overall better sleep. Through my proposed investigation, I will be able to uncover if colour noise really does reap the benefits of increasing or at least bringing deep sleep to a healthy level in individuals or perhaps if brown or white noise produces better results.

Through this, I can promote the use of the most effective colour noise, something extremely accessible to the general public that can promote high quality sleep and brain functioning.

Neha Pathak, a doctor of medicine (2020), states that, “Pink noise is a constant sound in the background.

It filters out things that distract you, like people talking or cars going by, so they don’t interrupt your sleep. You may hear it called ambient noise.” Pink noise achieves this through its constant and steady frequency or pitch which creates a flat, even sound like rain or tidal waves. Doctor Michelle Drerup confirms that, “The pink noise actually enhances brain activity that’s associated with deep phases of sleep.” This is because pink noise is a spectrum of frequencies whether the power per hertz decreases in intensity at a rate of 3 decibels per octave whilst the frequency increases simultaneously. As a result, “the lower frequencies in pink noise are louder and have more power than the higher frequencies” (Castro, 2013). The body also feels comfortable when listening to it because it changes in equal octave intervals. This constant decrease in sounds mimics natural rhythms of the body such as heart beating.

Brown noise is described to be deeper than pink and white noise. These soothing, lower frequencies may stimulate slower brain waves and thus produce deeper sleep. According to Dr Axe (2020), “Brown noise is considered a layered sonic hue that has a sound like a low roar.” The power of brown noise decreases as frequencies increase.. “Some report that brown noise serves as the perfect soft, monotonous soundtrack to life...brown noises can also be used to boost relaxation and focus.

Continuous, soft noise can be used to help “quiet your mind” and tune out thoughts that may be running through your head.” This instant relaxation of the brain and muscles is essential to stimulating deep sleep. White noise is a sound that contains all bands of frequency just as the colour white contains all colours of the spectrum. White noise has flat spectral density meaning that it is a signal that has equal power at different frequencies. The monotony and ‘one sound’ produced by white noise is proven to “help people who suffer with tinnitus and people who have trouble sleeping. That is because our brain can determine a single source frequency, but it becomes difficult to hear multiple” (Bazoyan, 2020). Thus, the continuity and stable nature of white noise does contend as an effective colour noise that can slow down brain waves and contribute to the stimulation of deep sleep.

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Hypothesis: I hypothesise that white noise will promote longer deep sleep because it contains all bands of frequency. This means that it is more effective in masking a variety of frequencies in sounds (ranging from 20 Hz to 20 000 Hz) that could disrupt the brain’s process in entering the deep sleep phase.

Variables in your investigation Independent Variables

Dependant Variables

Controlled Variables

Colour noise

The duration of deep sleep as a percentage of overall sleep

The duration of colour noise being played

-

-

The time at which the subject prepares for sleep and wakes up

-

-

The volume of the colour noise

-

-

The distance of the audio device from the head of the subject

-

-

The diet and interactions of the subject

-

-

The room temperature

-

-

The amount of light within the room

Equipment and Materials - Fitbit watch and app - Audio device - 30 mins of white, brown and pink colour noise (provided by an audio device) - Appropriate place to sleep Risk Assessment Risk

Precaution

The subject may feel at discomfort with listening to the sound.

In this unlikely case, choose another subject.

The subject may struggle with maintaining a constant diet or experience digestive issues.

Ensure that the diet does not need to be the same each day but rather typical with no fluctuations caused by consumption of caffeine, excess sugar etc.

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The subject’s typical and natural sleep patterns may be disrupted leading to mood/health problems.

If discomfort in sleep patterns causes extreme impacts, select another subject.

The Method 1.

2.

3.

4.

5.

6.

7.

Prepared the subject for sleep at 11:00pm, ensuring they were in a comfortable position.

Ensured no air conditioning was on and only one small lamp outside the room was on.

Situated the audio device (phone) approximately 50cm away from the subject’s head.

Ensured the subject was wearing the fitbit device.

Played the first colour noise audio for 30 minutes.

Woke the subject up at 8:30am using an alarm clock.

Checked the statistics on the Fitbit application and recorded the percentage of deep sleep results into a table of results.

8. Repeated the experiment 3 days in a row with the same colour noise.

9. Repeated the experiment for another 3 days for a different colour noise, and then repeated for an additional 3 days using another colour noise.

Results: Qualitative Data: Duration of deep sleep as a percentage of overall sleep (%) Trial 1

Trial 2

Trial 3

Average

No Colour Noise

17

16

18

17

Pink Noise

20

22

22

21

Brown Noise

16

17

19

17

White Noise

25

23

23

24

Quantitative Data: Through observation of myself and my experience with the colour noises, I found that the pink noise did feel comforting and settling, seeming as though it changes in equal octave intervals so the stability of the sound may enable a sense of relaxation. The pink noise was also powerful enough to filter out the surrounding sounds but not to the point where it was a disruption to my body’s process in shutting down and calming down thoughts. The brown noise on the other hand was quite low and soft which meant that it was ineffective in blocking surrounding sound. The weakness of it may have affected its ability to slow down the speed of brain waves. White noise however was deemed, from an observational perspective, the most effective in putting me to sleep. This is because white noise is the auditory equivalent to white light, meaning it includes every frequency so even the smallest disruptive sounds can be masked, regardless of their frequency.

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Photographic Evidence:

(sample screenshots of experiment)

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Processing and Analysis of Results Graphs

FIGURE 1

FIGURE 2

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The Discussion Describing and Explaining Trends An evident feature found within the results is that white noise was the most effective colour noise for inducing deep sleep in comparison to brown noise which had close to no effect and pink noise which was successful but still did not produce as much deep sleep as white noise. White noise produced an average of 24% which was 7% more than usual deep sleep levels. Additionally, brown noise had no effect on deep sleep at 17% and pink noise produced slightly better results with an average of 21%. A trend found within the results was that white noise was more effective than pink and brown noise because it is a sonic embodiment of every frequency. This means it can range from 20 Hz to 20 000 Hz that fire off at random times throughout with equal power. White noise is also more effective than pink noise because it features frequencies that are both high and low whilst pink noise only features lower frequency sounds. This variety of frequency means that white noise is more successful in blocking out a range of different sounds produced by the subject’s environment. It prevents random and unprecedented inconsistencies in sound from disturbing the brain and thus deep sleep. In isolation of brown and pink noise, pink noise is deemed more effective because its lower frequencies are louder than the higher frequencies meaning the sound is less abrasive.

However, although brown noise does have stronger and louder low frequencies this can be excessive and the deep sound may be too ‘rough’ or deep for the human ear and brain thus disrupting the body’s process in reducing the speed of brain waves.

Evaluation I ensured my experiment was reliable by repeating it 3 times for each colour noise and finding an average in the results. However, in order for this reliability to be present the results despite being repeated, needed to be similar which is what I indeed achieved. Across my trials I found a 1-2% difference in results.

In order to receive valid results and ensure the experiment itself was conducted with validity, multiple variables were controlled resulting in a controlled environment. In attempting to achieve this, I went to bed at the same time each night and woke up at the same time with a 5-10 min leeway. It was also ensured that the same amount of light came through the bedroom, the colour noise played for 30 mins each night at the same volume, room temperature remained constant, my daily routine and diet remained relatively constant and the subject slept in the same place. Despite this, there was some difficulty in controlling the wake up and sleep time variables considering I did not have total control over what time my brain would go into a sleep phase thus I applied a 5-10 minute leeway which could have interfered slightly with my results although they were considerably consistent. In terms of measurement technique, my results relied on the fitbit tracking device.

Although the source is extremely reputable and scientifically proven, I could only rely on my visual and auditory observations to a limited extent meaning any error derived from the results would be from the form of measurement. Furthermore, the amount of deep sleep was calculated as a percentage of overall sleep rather than based on hours as I didn’t want external factors such as the

70

amount of REM or Light sleep to impact the amount of hours of deep sleep. This thus looked at the data as a ratio rather than an isolated value.

The main error lies in the reliability of the fitbit watch itself. The watch relies on a combination of movement, heart rate and oxygen level patterns to determine the stages of sleep entered by the body. It also uses heart rate variability as this fluctuates depending on when the body enters deep, REM or light sleep. Despite the detailed collection of data being considerably accurate and reliable, complications such as movement of the watch on the wrist could have interfered with the fitbit monitor’s recording of this information. However, this is something that cannot be controlled in the experiment but would affect the results to a minor degree.

A second source of error that lied within the experiment is the control of diet and stress levels. This is yet another variable that could not be controlled due to the fact that the experiment was not conducted in an isolated laboratory but rather a normal environment where I, the subject, could not avoid undergoing certain events such as changes in diet (although not drastic), participating in exams, sports etc. These are compulsory events that I had to partake in which could have influenced the results. However again, the results only differentiated by 1-2% thus this error wouldn’t have had an immense influence.

In order to modify my experiment, I would ensure that the subject could, if possible, maintain a very similar diet over the course of the testing and undergo the experiment at a time where there were no ‘events’ that could impact stress, heart rate levels, mood or food/beverage consumption. I would also perhaps find a way to ensure that the fitbit watch results were reliable by using an additional different sleep tracking device beforehand to compare if the results obtained are similar and thus reliable.

Conclusion: In the experiment, the relationship between different colour noises of pink, brown and white and the deep sleep they produced as a percentage of overall sleep was investigated. The results obtained support the hypothesis that white noise would be most effective in producing the most deep sleep.

Its range of frequencies allow it to more successfully ‘mask’ or ‘block out’ external sounds of the environment that could thus disrupt the reduction of brain wave activity/speed.

APA Reference List Bazoyan, A. (2020). White Noise and Other 5 Noise Colors. Krisp. https://krisp.ai/blog/did-you-know-that-noise-has- a-color/ Britannica, T. Editors of Encyclopaedia (2013). Noise. Encyclopedia Britannica. https://www.britannica.com/science/noise-acoustics#ref894665 Capritto, A. (2019). The secret to better sleep: Pink, Black, Blue and Brown noise. CNET. https://www.cnet.com/health/sleep/white-noise-pink-noise-blue-noise-brown-noise/

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Castro, J. (2013). What is Pink Noise? Live Science. https://www.livescience.com/38464-what-is-pink-noise.html Chandra, S. (2021). Pink Noise For Better Sleep, Memory & More. Suruchi Chandra Integrative Psychiatry /////& Medicine. https://www.chandramd.com/blog/pink-noise-benefits-sleep-memory Health Essentials. (2018). Why "Pink Noise' Might Just Help You Get a Better Night's Sleep. Cleveland /////Clinic.https://health.clevelandclinic.org/why-pink-noise-might-just-help-you-get-a-better-nights-slee Johnson, J. (2019). What to know about deep sleep. Medical News Today. https://www.medicalnewstoday.com/articles/325363 Johnson, G., Losos, J., Mason, K., Singer, S., Raven, P. (2010). Biology Ninth Edition. McGraw Hill.

Levy, J. (2020). What is Brown Noise? Benefits + How to Use it for Better Sleep. Dr Axe. /////https://draxe.com/health/brown-noise/ Robinson, K, M. (2020). Pink Noise: Can it Help You Sleep? WebMD. https://www.webmd.com/sleep-disorders/pink-noise-sleep Silver, N. (2020). How Long Does It Typically Take to Fall Asleep? Healthline. https://www.healthline.com/health/healthy-sleep/how-long-does-it-take-to-fall-asleep

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HS - Chemistry IA - FINAL

by Hannah Svoboda

Submission date: 02-Jun-2021 07:27PM (UTC+1000) Submission ID: 1598939033 File name: HS_-_Chemistry_IA_-_FINAL.pdf (2.32M) Word count: 6340 Character count: 31447

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jmw083 Alcohols are organic compounds containing the hydroxyl (-OH) functional group, directly bonded to carbon.

The carbon which is directly attached to the OH is referred to as the "carbinol" carbon (Ashenhurst, 2021), which can be seen in red on the ethanol structure in Figure 2. However, it is this carbinol carbon that is key to understanding common classifications used for alcohols, including "primary", "secondary" and "tertiary" alcohols (Ashenhurst, 2021). The names of alcohols can also be identified through their suffix '-ol'. To conclude the classification of an alcohol, the carbinol carbon must be examined: if it is attached to "one carbon, the alcohol is primary; two, secondary etc."

Ethanol is a simple primary alcohol with the molecular formula C2 H 5OH and the primary intoxicating ingredient of many alcoholic beverages including beer, wine, and spirits (Encydopaedia Britannica, 2019). A highly water-soluble liquid at room temperature, ethanol is also widely used as a solvent in cosmetics and pharmaceuticals (Pearson Chemistry 12 - New South Wales, 2019). The oxidation of primary alcohols, like ethanol, is a two stage process where an aldehyde is produced first, followed by a carboxylic acid. Any primary alcohol follows the sequence of oxidation seen in Figure 2.

H

I

H

I

H-C-C-OH

I

H

I

+!OJ.heat

H

I

H-C-C

o

-f'

H

H

c1hanol primary alcohol

ethanol

primary alcohol

+!OJ. heat

reflux

cthanal

aldehyde

I

o

-f'

H-C-C H

011

cthanoit :u;:id arboxylic .acid

cth.Jn..il aldehyde +!OJ.heat ----->

H

+!OJ.heat -----> n-flux

ethanoic acid

carboxylic acid

Figure 2: Ethanoic acid formation from the oxidation of ethanol, with intermediate ethanal (Brown, & Ford, pg 505. 2014)

Ethanol is oxidised to ethanal which can be viewed as oxidation by removal of hydrogen, evident in Figure 2.

For ethanoic acid to be formed, the aldehyde, in this case ethanal, must remain in the reaction mixture to oxidise for a longer period of time, essentially requiring a complete oxidation of ethanol. Ethanol also undergoes bacterial oxidation to form ethanoic acid. This form of oxidation is a problem for wine producers as air contains a large proportion of bacteria called Acetobacter, which use atmospheric oxygen to oxidise ethanol in wine, producing a weak solution of ethanoic acid. When a bottle of wine is exposed to oxygen in the air, bacteria slowly oxidise the ethanol to ethanoic acid, in extreme cases oxidising all of the ethanol into vinegar. Although ethanol is less reactive than other phenolics (chemical compounds consisting of one or more hydroxyl groups bonded directly to an aromatic hydrocarbon group [Swanson, 2003]), its high concentration in wine makes it a significant competitor in the oxidation-reduction potential medium when strong oxidants are present or the length of time to oxidise is extended (Margalit, 2016).

Ethanoic acid (CH 3COOH) is the main volatile acid in wine. The term 'volatile acidity', or VA, is used to define a number of acids in this group. According to Victoria Burt, of the Wine & Spirit Education Trust (WSET), volatile acidity can be caused by "ethanoic acid bacteria, inadequate levels of sulfur dioxide, and excessive exposure to oxygen." (Clark, 2019). Sulfur dioxide, or SO2 , is an approved wine additive and used often as a preservative. Chemicals such as acetaldehyde can be added to wine, binding to the sulfur dioxide and making it unavailable as an antioxidant, hence the wine is available to oxidise.

This investigation will aim to determine ethanoic acid concentrations in red wine as the high ethanol content allows for higher concentrations of H+ ions to be detected through the complete oxidation of the alcohol.

Sodium hydroxide (NaOH) will be used as a strong base and titrated against the weak ethanoic acid

75

jmw083 (CH 3 COOH) of the red wine , represented by the first equation below where the reactants are in a 1 :1 molar ratio. The second equation represents the conjugate acid/base pairs:

CH3COOH(aq) + NaOH(aq) CH3COOH(aq) + H20(1J

"'-

"'-

NaCH3C00(aqJ + H20{IJ

CH3COO·(aqJ + H3 0+ (aq)

The reaction of a carboxylic acid like ethanoic acid is reversible so the equation is written with equilibrium arrows.

Ethanoic acid is classifi ed as a weak acid hence it only partially dissociates in water to form hydronium ions.

" "

Titntlon of• wHk acid with■ 1tton1 ban

10

Titration curves are graphs used in Chemistry which plot the volume of titrant (NaOH/strong base in this situation) against pH. Generally, the initial pH of a weak acid is higher than a strong acid. The beginning of the curve only increases gradually because the solution acts as a buffer, continuing until the base overcomes the buffering capacity 0 • 10 JO JO (S ohn & Thornton, 2019). The equivalence point sees the pH to be greater than 7 as seen in Figure 3 because all of the acid (CH3 COOH) has been converted to its conjugate base (CH 3 COO-). Figure 3 (above right): The titration of a weak acid with a strong base (Freyre, n.d [CC])

-•poin1

.&O

'°

An increase in the production of ethanoic acid through the oxidation of wine would result in the same initial pH reading of the wine and therefore the same initial concentration of W ions. However, different concentrations of the acid in the actual wine itself would be present given wine is not just H• ions, rather a very complex mix of chemicals in solution. The titration would hence see more base required to reach the equivalence point in the trials with lower oxidation times as the overall concentration of acid in the wine is decreasing with the W ions increasing (W ion concentration will be greater than the initial acid concentration and require less base for neutralisation).

Hypothesis: The number of moles of ethanoic acid in the wine will increase as the time for exposure to oxygen increases because when the ethanol present in wine oxidises, it forms ethanoic acid.

Variables: Figure 4: Independent and dependent variables with justification Variable

Information

Justification

Independent

Time in minutes for 2020 Wolf Blass Shiraz to be exposed to oxygen

The selected time increments in minutes (0.0, 15.0, 30.0, 45.0 and 60.0 ± 0.1 minutes) will ensure the wine has a range of times to be exposed to oxygen and tested to provide distinguishable results.

These five increments tested will include data from five consistent, precise trials to ensure reliability of the experiment.

Dependent

Number of moles of ethanoic acid in the wine

This will be determined using molar calculations after the titration reaches the equivalence point, indicated by the data logger graph.

Then comparison between tests can help determine if there is any relationship between the oxidation of wine and acidity levels.

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jmw083

Controlled variables:

Figure 5: Controlled variables; method and reason to control

Control

Method to Control

Reason to Control

Type of wine being tested

Purchase the same brand and type of wine to use for all tests and trials.

Alcohol content varies between brands and types of wine so it is important to ensure only 2020 Wolf Blass Shiraz will be bought and tested.

Batch of wine being tested

Purchasing the same harvest of wine: this experiment will be using 2020 Wolf Blass Shiraz

Using the same batch of wine ensures constant: grapes; growing conditions; production; treatment; harvesting time; and soil quality.

Hence, confidence increases that changes in acid levels of the wine after oxidation are related to the experimental aim.

Volume of wine used per trial

Use a 25cm3 glass pipette to measure out 25.000 ± 0.045cm3 of wine each trial.

Ensures any acidity changes are accounted for as the oxidation of ethanol in wine. Also allows for the volume of wine to remain controlled, hence molar calculations will be more accurate.

Beaker to contain wine

Use the same 100cm3 Pyrex glass beakers.

The size of beaker used to contain wine samples must remain constant to ensure the same surface area of wine is exposed to air, preventing any further oxidation during the titration.

Molarity of NaOH solution

Use a lab technician prepared 0.100 mol sodium hydroxide solution

The molarity of the sodium hydroxide (NaOH) solution must remain constant throughout trials to ensure molar calculations after the data is collected will be consistent.

Volume of

Teacher will add, with a pipette, 1.0 ± 0.1cm3 of acetaldehyde to each larger beaker of wine.

To best model the aging of wine, acetaldehyde will be added to "bind sulfur dioxide, making it unavailable as an antioxidant" so oxidation may occur (Cordingley, B. [Personal communication (email)]. November 25, 2020).

Air stone/

pipe diameter

The AquaOne 500 air stone, pump and pipe diameter will be used.

Air will be bubbled into the wine using an air stone to get a larger gas/liquid interface. This models oxidation of wine over time and allows results to be obtained over the experimental period.

PASCO pH probe

Use the same PASCO digital pH probe each trial.

No need to consider varying calibrations of the equipment if the same probe is used for all trials.

Temperature/

Conducted on the same bench with temperature and anomalies monitored.

"An increase in temperature typically increases rate of reaction" (Key, 2020) so this control ensures only oxygen exposure time affects acid levels, eliminating environmental interference.

acetaldehyde

added to wine

location of experiment

Confounding variables: • The wine may continue to oxidise with the air during the titration, after pumping with the aquarium bubbler. This could therefore lead to some ethanoic acid formation after the set time planned (0.0, 15.0, 30.0, 45.0 and 60.0 ± 0.1 minutes) for the wine to oxidise • If two bottles of the same wine are needed for the experiment due to the number of trials conducted, one may have a slightly higher concentration of ethanol for acid formation, even if they were produced under the same conditions • The ambient temperature in the lab may fluctuate over the five day period the experiment was conducted due to varying weather in the location of the lab

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jmw083

Equipment:

Figure 6: Equipment list, uncertainty of equipment and justification for specific purpose Equipment

Uncertainty

Purpose

15 x 100cm3 beakers

± 10cm3

Used to contain the 25 ± 0.045cm3 of red wine for each trial under the burette for titration against the sodium hydroxide. Conical flasks could not be used due to also needing to place the pH probe in the solution.

20.0cm3 acetaldehyde

± 0.1cm3

Acetaldehyde is needed in this experiment to "bind sulfur dioxide, making it unavailable as an antioxidant" (Cordingley, B. [Personal communication (email)]. November 25, 2020), hence oxidation can occur at a faster rate.

2 x bottles 2020 Wolf Blass Shiraz

N/A

Used as a general model for ethanol conversion into ethanoic acid in red wine after oxidation. This wine is being chosen due to easy access to the product and no environmental or ethical concerns as it is sourced locally.

1250cm3 X NaOH solution (0. 1 oo mol)

± 0.001 mol

Acts as the titrant in the experiment so that the concentration of acid in the wine can be calculated stoichiometrically.

1 x AquaOne 500 aquarium pump, air rock, tube

N/A

The aquarium pump and tube will aerate the beakers containing wine to be exposed to oxygen. The air rock attaches to the tube and sits at the bottom of the beaker, so air can pump through the entire volume of wine.

1 x PASCO digital pH probe

± 0.01 pH units

Takes precise pH readings in the titration, providing a data logger graph to display the equivalence point, and determine that value to record.

1 x plastic pipette

± 0.1cm3

Used by the teacher to add the volume of acetaldehyde to the wine.

1 x pipette filler

N/A

Used to release air and draw the wine into the glass pipette safely.

1 x 25.000cm 3 glass pipette

± 0.045cm3

Needed to measure a constant and accurate volume of the red wine into the beakers each trial for the titration.

1 x 50.00cm 3 burette

± 0.05cm3

Contains the NaOH. The ability to control precision over other equipment is beneficial for performing acid content molar calculations in the wine.

1 x burette clamp

N/A

Required to hold the burette level and securely for the titration.

1 x retort stand

N/A

Support the burette clamp and hold equipment at a comfortable height.

3 x 200.0cm 3 beaker 1 x funnel 1 x Fastime stopwatch

± 7.5cm3

To contain each test of the red wine which will be exposed to oxygen before dividing into smaller beakers for the trials and each titration.

N/A

To safely pour NaOH solution into the burette, minimising spillage risk.

± 0.01 seconds

Ensures the time the wine is left to oxidise is equal to that stated in the independent variable.

1 x IEC magnetic stirrer and bar

N/A

Used to ensure that the solution of wine with base is mixed automatically while the titration is occurring without the need for manual stirring.

Distilled water

N/A

To rinse the pH probe after each trial.

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Risk Assessment/Environmental and Ethical Considerations:

jmw083

Figure 7: Risk, environmental and ethical assessment, hazards and methods to minimise risk

Hazard

Risk

Broken glass

Cuts to skin

• •

Acetaldehyde is a possible carcinogen

Increased cancer risk

• •

Inhalation of acetaldehyde

Airway and lung irritation

Acetaldehyde exposure

Skin and eye irritation

Wine ingestion

Allergic reaction

Ingestion of NaOH solution

Stomach irritation

End-product disposal

Environment contamination

• • • • • • • •

Method to Minimise Risk Be careful when handling glass apparatus If glass breaks, alert teacher and clean up using dustpan, wrap in newspaper before putting it in the bin. Do not use fingers Teacher use only Do not ingest, inhale or come in contact with chemicals. Wear gloves, goggles and a l ab coat (PubChem, 2021) Teacher use only, do not stand over or inhale the chemicals Should inhalation occur, contact Poisons Information Centre Australia Causes serious contact irritation and bums, teacher supervision or use. Immediately call medical assistance if contact occurs Wash hands after use and keep away from others while using Do not drink to limit contamination or allergen spread Do not ingest chemicals, may cause abdominal pain (LabChem, 2018) If ingested, contact Poisons Information Centre Australia Dispose of acetaldehyde solution in chemical waste bin through lab technicians to avoid waterway contamination (IMAP, 2017)

T here are no ethical considerations as no animal or human subjects were used in the investigation.

Method:

1) Gather all equipment and reagents. Put on a lab coat and goggles.

2) Open the bottle of Wolf Blass 2020 Shiraz and pour into a 200cm 3 beaker. **Note: Steps 3-5 of this method do not need to be completed for the first test of 0.0 minutes± 0. 1 minute 3) Set up the AquaOne 500 aquarium pump, attach the tube and the air stone then place the air stone at the bottom of the beaker. The stone will ensure the tube remains submerged.

4) Teacher required to add 1 ± 0.1 cm 3 acetaldehyde to the beaker using a plastic pipette under a fume hood.

5) Turn on the aquarium pump and star t the stopwatch for the time exposed to oxygen for each test (0.0, 15.0, 30.0, 45.0 and 60.0 ± 0.1 minutes) as stated in the independent variables table, Figure 4.

6) While the pump is bubbling air into the wine, set up the retort stand and burette clamp.

7) Rinse the burette with the prepared 0.100 mol NaOH solution into the sink.

8) Ensuring the stopcock is closed, fill the burette using a funnel with NaOH solution to the zero mark, with the bottom of the meniscus on the mark. Place the burette into a clamp on the retort stand. ) After the time for the test reads on the stopwatch, turn off the pump and take out the air stone.

9 10) Use a 25cm3 glass pipette and filler to draw up 25 ± 0.045cm3 of wine from the beaker into a 100cm 3 beaker and place under the burette tip on the magnetic stirrer, placing the stirrer bar in the beaker.

11) Turn on the PASCO digital pH probe and follow instructions to pair via Bluetooth to the SparkVUE app.

12) Place probe inside the beaker, ensuring a pH reading is visible on the screen. T his probe replaces the need to use an indicator for equivalence point, considering wine is a coloured solution.

13) Begin to titrate the wine ag ainst the NaOH by twisting the stopcock open. T he SparkVUE app is a data logger and requ ires the screen to be clicked for every 1cm 3 of base added to the wine, creating the graph.

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jmw083 14) Titrate the full 50 ± 0.05cm 3 of NaOH solution to graph a full titration curve on the data logger. Export the trial for when the equivalence point will be determined at a later time and recorded in Figure 9.

15) Rinse the pH probe with distilled water over the sink and set aside.

16) Pour mixture of red wine and NaOH into a large waste beaker and move the beaker aside.

17) Repeat Steps 8 to 14 until 5 trials of the same test yield consistent data.

18) Repeat Step 15 for the remaining test times for the wine to be exposed to oxygen.

19) Clean up the lab bench and dispose of waste chemicals with lab technician assistance.

Justification of Method:

This method was designed for accurate and reliable data collection through consideration like the chemicals and equipment used to convey and obtain results, trial repetition and general accessibility of the experiment.

A total of five time intervals (0.0, 15.0, 30.0, 45.0 and 60.0 ± 0.1 m inutes) for the wine to be exposed to oxygen will be tested, with each test repeated until five sets of consistent results are yielded. Equipment used l ike the PASCO digital pH probe and glass pipette have very low uncertainties of ±0.01 pH units and ±0.045cm 3 respectively which increases the precision of the experiment. The method is accessible as materials can be sourced through a school laboratory with moderate testing times.

Measurement Uncertainties:

Figure 8: Measurement uncertainties table and considerations

Measuring Instrument

Uncertainty

Smallest Quantity Measured

% Measurement Error

PASCO pH probe

± 0.01 pH units

3.77 pH units

0.265%

25cm3 glass pipette

± 0.045cm3

25.000cm3

0.180%

50cm3 burette

± 0.05cm3

25.40cm3

0.197%

Measurement uncertainties (Figure 8) should ideally be lower than 1% in Chemistry and a strength of this experiment is that the hig hest percentage measurement error is 0.265%, for the PASCO pH probe. This shows that the percentage errors of the measuring instruments and equipment used to collect the data for this investigation have been considered and are within an appropriate range to increase precision.

Data Collection and Processing:

Raw Data:

Figure 9: Raw data table of the volume of sodium hydroxide rnrated to reach the equivalence point± 0.05cm 3 for all trials

Time for wine to be exposed to oxygen± 0.1 minutes

Volume of NaOH titrated to reach the equivalence point± 0.05cm3 Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

0.0

29.50

30.10

30.10

29.70

30.30

15.0

27.10

27.05

27.15

27.05

27.20

30.0

26.70

26.75

26.85

26.80

26.70

45.0

25.55

25.55

25.70

25.75

25.65

60.0

24.65

24.70

24.75

24.70

24.55

The raw data seen in Figure 9 was attained through a data logger on the SparkVUE app, which sourced data from the PASCO digital pH probe during the titration. Images/files of each titration trial for the data logger were exported and inserted into a software (WebPlotDigitizer) which found the equivalence point on the graph to two decimal places. *Note: WebP/otDigitizer software source located in reference list

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jmw083

Processed Data:

Figure 10: Processed data table

Time for wine exposure to oxygen ± 0.1 minutes

Average volume of NaOH titrated to reach equivalence point ± 0.05cm 3

Average number of moles of ethanoic acid in the wine (mol)

0.0

29.94

2.994 X 10" ± 3.494 X

1Q -5

15.0

27. 11

2.711 X 10" ± 3.211 X

1Q-S

30.0

26.76

2.676 X 10" ± 3.176 X

1Q -5

45.0

25.64

2.564 X 10" ± 3.064 X

1Q -5

60.0

24.67

2.467 X 10" ± 2.967 X

1Q -5

Qua I itative/Photos: Observations: The wine colour turned from deep red to green/blue once in a solution with Na OH. The odour also changed from a distinct alcohol-based wine scent to one similar to vinegar at the conclusion of each trial. The initial pH of the wine remained constant for all trials at approximately 3.77 ± 0.01 pH units.

Photos: Figure 11 is an annotated photo of the experimental setup. Once prepared as per this image, the data logger was turned on and the trial could begin. Figure 12 displays the data logger graph which was then subse uentl anal sed b a software to determine the e

.

'

��-f-"""'i--O.o.ldlNd' 2020

Wolflllus'Shiru

Figure 12: Data logger graph (Trial #1 of Test #5)

Figure 11: Annotated photo by author of experimental setup

Graph:

·i -s

0.003 0.0029

�

..

0.0028

-�

...,

0,0027 0,0026

·o

i

::, c;

t

e average num er o mo es o et the time for wine to be exposed to oxygen

+ l I

....

••••••····

....

f

0,0025

•••••••······· ....

-...

.... . .. ....

R2 =-0.9108

0,0024

�

0.0

10,0

20.0

30.0

40,0

50,0

60.0

Time for wine to be exposed to oxygen± 0.1 minutes

Figure 13: Scatter graph of the relationship between ethanoic acid levels of wine and time exposed to oxygen (Note: graph has been broken on the y-axis to allow for more effective observation of data points)

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jmw083 Discussion: Analysis:

The data gathered from this investigation exploring the relationship between the time wine is exposed to oxygen and the levels of ethanoic acid in the wine shows a very strong, negative linear correlation, which can be observed in Figure 13. As the time for the wine to be exposed to oxygen increases, the average number of moles of ethanoic acid in the wine decreases. This decrease is also displayed in the processed data in Figure 10, as when the wine was exposed to oxygen for 0.0± 0.1 minutes, the average number of moles of ethanoic acid in the wine over five trials was 2.994 x 10·3± 3.494 x 10· 5 mol, whereas when the wine was exposed to oxygen for 60.0± 0.1 minutes, the average number of moles of ethanoic acid was lower at 2.467 x 10·3± 2.967 x 10·5 mol. The vertical error bars on each data point in Figure 13 represent the absolute error propagated for each test, or the uncertainty of the average number of moles for each time interval the wine was exposed to oxygen. Therefore, higher uncertainty exists between the data points at the 15± 0.1 minutes and 30± 0.1 minutes tests as the error bars overlap. The R-value calculated from the data collected in this experiment was -0.9108. This value supports the very strong, negative linear relationship between the data points and is represented above the trendline in Figure 13. However, an outlier in the data existed at 15± 0.1 minutes of oxygen exposure with a value of 2 .711 x 10·3± 3.211 x 10· 5 mol and the R-value could have been closer to -1 (perfect negative correlation) if excluded from analysis or re-tested. It is unable to be exactly determined what caused this value to deviate from the expected trendline but it is likely to be attributed to a combination of the many listed errors in Figure 14.

Conclusion:

The results of this investigation revealed that as the time for the wine to be exposed to oxygen increased, the average number of moles of ethanoic acid in the wine decreased. The equivalence point differed between tests as seen by the average volume of Na OH required to titrate to the equivalence point± 0.05cm 3 in Figure 10. There is a general decrease in the volume of NaOH required with 29.94± 0.05cm3 needed for the 0.0± 0.1 minutes test and 24.67± 0.05cm3 needed for the 60.0± 0.1 minutes test. The pH of the equivalence point remained greater than 7 for all tests because all of the acid (CH3 COOH) was converted to its conjugate base (CH 3COO·). This showed that the overall concentration of acid in the wine decreased as the H+ ions increased, because Wion concentration was greater than the initial acid concentration and required less base for neutralisation. These results are supported by the scientific understanding of reactions between weak acids and strong bases as detailed in the background information. Because ethanoic acid is a weak acid, it only partially dissociates in water to form hydronium ions and there is a buffer until the base (NaOH) overcomes the buffering capacity, hence the reaction is in constant equilibrium.

Also, the reactants were in a 1 :1 molar ratio which allows for the explanation that higher volumes of NaOH were required to neutralise the higher number of moles of ethanoic acid in the wine, seen when there was less time for the wine to oxidise. The percentage error could not be calculated for this experiment given the lack of theoretical values available. Despite extensive research and re-contacting the AWRI, no theoretical values which could be attained as a reference to this investigation, hence evidence as to whether the main sources of error were systematic or random cannot be produced. Generally, suggestions and ways to minimise error and improve the investigation are listed in Figure 14.

The experimental findings connect to industry in the properties and changes of wine under varying environmental conditions, such as oxygen exposure which was tested in this investigation. Winemakers often dedicate additional time into researching the chemical processes and importance of determining acid content in wine through methods explored similar to titration and molar calculations, better facilitating production, storage and consumption. Ensuring the consistency of these industrial processes is vital in winemaking as there are many internal and external variables for oenologists to understand and control.

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jmw083 Evaluation and Extension: The purpose of this investigation was to explore if a relationship exists between the time 2020 Wolf Blass Shiraz is exposed to oxygen and the number of moles of ethanoic acid in the wine. The experiment had a number of weaknesses, limitations and errors which are all outlined in Figure 14, a table of systematic and random errors, their impact on the experiment and methods to eliminate these sources of error. As the data was collected over many days, one way to improve the overall precision in results would have been attempting to conduct all trials on the same day, where conditions inside the lab would be constant.

Although the experiment was quite reliable as the five trials completed yielded similar results (as seen in Figure 9), the range between these trials could always be further reduced through the additional improvements and minimised error methods in Figure 14. The investigation also had a high degree of validity as focus remained on the experimental aim, the hypothesis was supported and all controlled variables were kept. The precision regards the uncertainty and repeated measurements of a trial, which were outlined in both the measurement uncertainties table of Figure 8 and the processed data table of Figure 10. Percentage uncertainty of the mean for the test times the wine was exposed to oxygen (0.0, 15.0, 30.0, 45.0 and 60.0 ± 0.1 minutes) were all under 1.20%, where sample calculations can be found in the Appendix. The precision of the experiment was hence somewhat high, but should ideally be lowered to under 1% to be considered highly precise. In this case, the limitation of precision was due to measuring and calculating such small values of ethanoic acid formation in the wine, with 2.467 x 10·3 ± 2.967 x 10· 5 mol (or 0.0024670 ± 0.0000297 mol) being the lowest value detected through calculation. This could be improved by exposing the wine to oxygen for longer periods of time so more d istinguishable results may be obtained with further ethanol conversion to ethanoic acid. However, the overall impact of these errors is unable to be connected to accuracy as no literature values were obtained despite extensive research to calculate percentage error or determine how close the experimental data was to established values.

Some strengths of the investigation included: having a wireless, digital pH probe to indicate for the equivalence point; using a data logger to obtain a pH curve directly from the raw data; obtaining the equivalence point using the raw data from an accurate software; and the repetition of at least five, consistent trials per test. To extend this investigation, the experiment could be tested with: • • •

Longer periods of time for the wine to oxidise - given wine spoilage often occurs over longer periods of time, allowing the wine to have longer oxygen exposure may give a more accurate indication of the relationship between oxidation time and ethanoic acid formation in wine Different ambient temperatures of the wine - to determine if other environmental conditions impact upon levels of ethanoic acid formation in wine, which could link to storage methods of wine A different wine variety (eg. Riesling, Pi not Grigio etc.) to Shiraz - allows comparison to be made between different types of w ine to investigate any similarities or differences

Figure 14: Random and systemati c errors, effect on experiment and method to mi nimise impact

Error

Detail

Effect on Experiment

Method to Minimise Impact

Systematic

Different bottles of wine used

Chemical concentrations could vary between bottles

Error cannot be minimised as the wine volume required is greater than a bottle

Systematic

Changes in acid content of solution

CO 2 may be dissolved in solution (in equilibrium with carbonic acid)

Consideration this error and as it may cause a larger range between trials

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jmw083 Systematic Human error in timing tests and turning off the aquarium pump

AF astime stopwatch was used to time the wine aeration but human error was involved in reacting to turn the pump off

Account for the human error by including an uncertainty in the time the wine was exposed to oxygen of± 0.1 minutes (or 6 seconds)

Systematic

Parallax error of reading the burette

The burette was set up very high to compensate for the magnetic stirrer hence eye-level could not be maintained on the meniscus

Use a small step ladder to ensure eye-level can be maintained with the meniscus of the burette or use a lower lab bench/desk

Random

Calibration of the pH probe may have varied over the trial duration

Lead to variance in interpretation of the equivalence point for each trial, ultimately affecting the values in Figures 9 and 10

Consistency in initial values should be checked with another probe. Lab technicians to assist in recalibrating the probe each day if needed

Random

Varying ambient conditions over the duration of the experiment

Change in temperature across the days the experiment was conducted may have led to slightly varying reaction rates

Note all fluctuations in temperature in the lab over the period the experiment was conducted or attempt to conduct all trials during the same day

Random

Distraction of the experimenter when using the data logger

The experimenter had to use complete focus while using the data logger by clicking for each 1.00 ± 0.05cm3 that the meniscus passed along the burette

Ensure others in the room know that concentration is required during trials, limit external distractions and redo any trials which were influenced by distractions or misclicks

Random

Magnetic stirrer bar getting caught on pH probe

This resulted in the stirrer stopping mid-trial so the solution would not be thoroughly mixed

Re-start any trials where the stirrer bar gets caught in the beaker to allow for consistency in data collection/method

Reference List:

Ashenhurst, J. (2021). Alcohols (1) - Nomenclature and Properties. RetrievedFebruary 17, 2021, from https://www.masterorgani cchemistry.com/2014/09/17/alcohols1nomenclatureandproperties/ Brown & Ford. (2014). Chemistry for the IB Diploma. (2014 edition). Oxford: Oxford University P ress.

Retrieved February 28, 2021.

Cordingley, B. [Personal communication (email)]. November 25, 2020.

Encyclopaedia Britannica. (2019). Ethanol. Retrieved February 1, 2021, from https://www.britannica.com/science/ethanol IMAP. (2017). Acetaldehyde: Human health tier II assessment. Retrieved February 13, 2021, from https://www.industrialchemicals.gov.au/sites/default/files/Acetaldehyde_Human%20health.pdf International Organisation of Vine and Wine. (2017). 2017 World Vitiviniculture Situation. Retrieved March 1, 2021, from https://www.oiv.int/public/medias/5479/oiv-en-bilan-2017.pdf LabChem. (2 018). Sodium Hydroxide, 0. 1 N (0.1 M) Safety Data Sheet. Retrieved February 13, 2021, from http://www.labche m. com/tool s/msds/msds/LC24270.pdf Margalit, Y. (2016). Concepts in Wine Chemistry. (third edition). USA: Wine Appreciation Guild. Retrieved February 1, 2021.

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jmw083 Miller, M.(n.d). Monitoring Acids and pH in Winemaking. Retrieved February 11, 2021, from https://www.accuvin.com/wp-content/upl oads/201 5/04/MonitoringAcidsandp Hin Winem aki ng .p df PubChem.(2020). PubChem Compound Summary for CID 177, Acetaldehyde. Retrieved February 23, 2021, from https://pubchem.ncbi.nlm.nih.gov/compound/Acetaldehyde Robinson, J.(2006). The Oxford Companion to Wine. (third edition). Oxford: Oxford University Press.

Retrieved February 23, 2021. *Rohatgi, A.(2020). WebPlotDigitizer. Retrieved April 17, 2021, from https://automeris.io/WebPlotDigitizer/index.html Sohn, H & T hornton, J.(2019). Titrations of a Weak Acid with a Strong Base. Retrieved February 17, 2021, from https://chem. Ii bretexts. org/ Ancillary_Materials/Demos_Techniques_and_ Experiments/ Swanson, B.(2003). TANNINS AND POLYPHENOLS. Retrieved March 9, 2021, from https ://www. sciencedi rect.com/science/arti cle/pii/8012227055X0 11780 Wine Australia.(2018). Introduction to Wine - Educator Guide. Retrieved March 1, 2021, from https://www.google.com/url?ntroduction-to-Wine-Educator-Guide.pdf%3Fext%3D.pdf

Appendix of Calculations:

Figure 8: % measurement error Example from PASCO pH probe - row 1 : [uncertainty x 100]/amount measured 0.01 X [ 100/3.77] = 0.265%

=

% error

Figure 10:

Uncertainty of average volume of NaOH titrated to Average volume of NaOH titrated to reach reach equivalence point equivalence point (± 0.05cm 3) Example from Test #1 (0.0 ± 0.1 minutes): Example from Test #1 (0.0 ± 0.1 minutes): (0.05 + 0.05 + 0.05 + 0.05 + 0.05)/5 (29.50 + 30.10 + 30.10 + 29. 70 + 30.30)/5 = 29.94 ± 0.05cm 3 = 29.94 ± 0.05cm 3 Propagated percentage (%) error for the number of Absolute propagated error for the number of moles moles (n) of CH3COOH (n) of CH3COOH Example from Test #1 (0.0 ± 0.1 minutes): Example from Test #1 (0.0 ± 0.1 minutes): Prop. % error = %1erro r ofwlume) + %1errorofconcentration) Absolute error = 1.1 670004 ... % x 2. 994 x 10·3 Absolute error = 0.00003494 moles Prop. % error = 1% + 0.1670004 ... % 5 = Absolute error ± 3.494 x 10- moles Prop. % error = ± 1.1670004 ... % Finding the average number of moles of NaOH Percentage(%) error for the concentration( c) of CH3COOH titrated to reach equivalence point Example from Test #1 (0.0 ± 0.1 minutes): C(cH,cooH) = 0.100 ± 0.00 1 mol dm·3 n(CH,COOH) = n(NaOH) % error = (0.001/0.100) x 100 'given they are in a 1 :1 molar equation % error = ± 1% = Percentage(%) error for the volume 01) of CH3COOH n cV = C(CH,C00H) X V(CH C00H) n 3 trialO ( ) % error =(0.05/29.94) x 100 = 0.1 X (29 .94/1000) n trialO ) ( % error = ± 0.1670004 ... % n(trial O) = 0.002994 = 2.994 x 10· 3 moles Average number of moles of ethanoic acid in the wine in moles Example from Test #1 (0.0 ± 0.1 minutes): : . = 2.994 X 10·3 ± 3.494 X 10·5

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Measuring Coral Replantation Efficacy HSC Science Extension Eloise Struthers

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Abstract; As a result of increasing anthropogenic pressures, Australian marine life already lives in a 1ºC world, resulting in the decline of the functional lungs of the ocean. As the coral reefs are home to over a quarter of all marine life, studies must be conducted into how these ecosystems can be better preserved. In attempts to uncover a factor that improves the success of restoration studies, this paper tested whether the use of Acroporid corals enhances the efficacy of the replantation projects, Using the publicly accessible ‘Coral Restoration Database’, a formula was created to measure the efficacy of each study, whereby the use of Acropora was identified as the independent variable. It was found that studies that focused on the coral had no statistically significant difference in efficacy to those that didn’t.

Accordingly, more complex research is required into factors that make restoration more successful.

Literature Review: Coral reefs are the most biodiverse ecosystem worldwide and directly sustain over 500 million people. (IUCN, 2017). As such, the importance of these ecosystems is a multiplicity of ecological, aesthetic and economic factors. However, the increasing effects of climate change, as a consequence of anthropogenic pressures, pose a major threat to the reefs. (Bay, 2020). When the water temperature exceeds conventional levels, the zooxanthellae (symbiotic algae) living in the host coral produce toxic oxygen species. This reaction instigates the expulsion of the symbiotic algae, in a process known as ‘bleaching’. (NOAA, 2021). Though this state is not fatal, the host is more susceptible to death from disease or abiotic stressors, due to energy deprivation. (DeCarlo et al, 2020). It is therefore apparent that the preservation of coral reefs is paramount to maintaining all life on earth, but the dispute remains as to how to achieve said conservation. This literature review highlights Acropora as one of the most prominent and well-researched coral genera, having great historical significance. Additionally, the review illustrates the variety of coral conservation methods and the shift towards more modern approaches, such as the use of genetic technologies.

Over the past two million years, ‘Acropora’ has remained the most abundant coral genus.

Though the species have been in existence for over 50 million years, preeminence was only established after the series of ice ages that continuously caused the ocean to rise and fall. The historical success of the Acropora species is therefore widely attributed to its ability to withstand changing sea levels. (Pavid, 2016). Pavid (2016) further illustrates the scientific consensus on Acropora’s success due to their ‘highest growth rates’, growing 5-10x faster than other corals (‌ Cramer et al, 2020). This is largely due to their reproduction via fragmentation, (figure 1a) as well as the evolved process that enables a transfer of nutrients from a side branch to the tip, ensuring active polyp growth. (Figure 1b). Subsequently, Acropora has remained a prominently durable and dominant coral genus over the past two million years.

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Figure 1a. Coral Fragmentation

Figure 1.b Acropora polyp branching

Acropora corals, however, are extremely sensitive to changes in temperature and pH levels (ocean acidification). Resulting, climate change has provoked a ‘particularly startling’ deterioration of Acroporid coral globally, as these environmental factors are greatly altered.

(Pavid, 2015). The reason for the mass mortality of Acropora, however, cannot be identified solely as human activities, as in the late 70s, outbreaks of White-band disease (WBD) killed approximately 80% of the species‌(Cramer et al, 2020). Though the origin and transmission of WBD remain unknown, rising sea temperatures and excess nutrients exacerbated the detrimental outbreaks, due to the abiotic suppression of coral immunity. Accordingly, it is believed efforts should be directed towards eliminating local anthropogenic stressors to revitalise Acroporid corals. However, much of the scientific community disputes this idea, reinforcing preservation efforts should encompass many different species, to ensure functional biodiverse reefs. (Hoegh-Guldberg, 1999) With the necessity to conserve all corals, various passive and active restoration techniques have been employed globally. Passive techniques include protection to allow natural rejuvenation, whereas active restoration comprises immediate intervention that intends to accelerate recovery. (The Nature Conservancy, 2021). Passive methods take precedence as an approach, however, can typically only occur where central contributors to survival amid temperature rise are present. The main factors are; ● Genetically predisposed to survive warmer temperatures ● Naturally protected from hot spells due to cold currents ● Able to constantly remain well-fed due to plankton presence Reefs lacking in this natural protection often result in more dire bleaching events, requiring active restoration procedures to be implemented. With the scant knowledge of bleaching susceptibility, scientists must take a variety of approaches to find a conservation model that suits many coral genus. (Dance, 2019). Dance (2019), aimed to introduce enough new corals to allow the reef to reproduce independently. To do so, ocean-based nurseries were 88

established in tree-like structures and left to grow. A sample of the grown coral was then tested in the environment, and if proven successful, was planted on the seafloor. (figure 2) This method has proven to be prosperous, however, the article does not mention the process to select the coral used, only specifically referencing their use of Acroporid corals. The other most prominent approach to coral conservation shifts the emphasis away from study the coral itself, and rather the changes to the environment instead. DeCarlo et al (2020) tested the role of nutrients in modulating bleaching susceptibility, revealing nutrient stress is equally a factor of bleaching (figure 3). This work was consistent with similar studies conducted in the Red Sea and thus provided a reliable perspective, asserting the need to shift scientific focuses.

Figure 2; Ocean-based nursery in Curaçao island

Figure 3; Coral bleaching in response to of heat & nutrient stress

In recent years, there has been an inclination to concentrate on the genetics of coral, in which understanding the genetic mechanisms that unpin bleaching is considered fundamental to conservation. (Bay, 2020) The major impediments in understanding coral molecular and cell biology include the deficit of experimentally compliant models and of genetic techniques that assess protein function and gene expression. (Cleves et al, 2020) However, a breakthrough study, Cleves et al, (2020) has utilised the microinjection of CRISPR/Cas9 into an Acropora specie, to mutate the Heat Shock Transcription Factor 1 (HSF1). To do so, two sgRNAs were created to target exons and cause a frameshift mutation, producing a non-functional protein.

It was found that corals with the HSF1 mutation had reduced thermal tolerance, thus confirming the importance of the gene. Due to the variety of the transcription factor, however, the most vital HSF1 target in stress reduction remains unknown. This research has since been republished globally, providing scope for further research worldwide regarding specific HSF1 targets and their exact roles.

As the effects of climate become more pronounced, the need to conserve coral reefs becomes increasingly urgent. While research shows the historical significance of the Acropora species, accelerating anthropogenic pressures have initiated the coral’s decline. Resultingly, various passive and active methods have been implemented to stop and reserve the devastation. From traditionally labour-intensive setups, nutrient regulation and gene knockout methods, a variety of techniques have been tested. There is progress but the question, however, remains as to whether a specific species is more likely to survive than others, and whether the synthesis of new techniques should adhere to this.

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Scientific Research Question: How does the use of Acroporid Corals affect the efficacy of replantation projects, as measured through meta-analysis of a multivariable database?

Scientific Hypothesis: HA: The replantation studies that have used Acropora as their primary coral will produce higher efficacy scores than those that did not, due to the extensive understanding and historical dominance of the coral.

Methodology: Ethics Statement This research was conducted in consultation with the Head Of Science at Santa Sabina College, which involved receiving ethical approval. Through this process, risks within the data collection stages were anticipated and hence, alleviated. The secondary data was obtained through the publicly accessible ‘Coral Restoration Database 2020’ as published by the International Coral Reef Initiative (ICRI) (figure 1/below). All following meta-analysis was conducted judiciously, ensuring an accurate reflection of the given data was represented.

Study Design Due to the nature of this research project, the initial step necessitated finding relevant secondary data. Once this was acquired, a formula was derived as a metric to measure the efficacy of each restoration project. Figure 2 highlights the process of ethically creating the formula to apply, in which relevant columns within the database were selected and manipulated to reduce the risk of inflating the final score.

Inclusion/exclusion Criteria To ensure valid research currency, all reef restoration projects conducted before the year 2000 were excluded from the study. Further, any studies that did have a value provided in one of the eight columns used in the formula were excluded.

Data Collection The formula was applied to the remaining 133 studies, each producing a comparable value for efficacy. The use of Acroporid coral was then identified as the independent variable, and the scores were subsequently divided into those with and those without, as shown in figure 3.

Studies that used more than 4 species, including Acropora, were identified separately and discarded before further analysis.

Data Analysis The mean, median, range and standard deviation of each set of data were calculated using standard calculations. (figure 4a) An F-test was then used to determine whether the variance between the two datasets is significantly different. The f-test result is always about the null hypothesis, where if the calculated p-value is greater than the alpha value, the null hypothesis cannot be rejected (figure 4b). A two-tailed t-test was then applied to determine whether the

90

null hypothesis (that studies using Acroporid corals were not more efficacious than those without) could be rejected (figure 4c).

Results Figure 1 - Sample from the Coral Restoration Database Accessible through; https://docs.google.com/spreadsheets/d/1qLZV4crHMj26t96leX3rt_cFCR9QfjaXpbgChGohBxU/edit#gid=1206000473

Figure 2a - Justification and process of creating a formula to measure the efficacy.

Columns that naturally provided larger values were divided to reduce inflation, whereas the rest were simply added.

In columns, AJ, AK and AR, the data was presented as yes or no’s, however, these columns were vital in calculating efficacy, so a numerical value was assigned to each word.

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Figure 2b - Final Formula - using columns from coral database

Figure 3 - Tables with efficacy metric sorted into studies “With Acropora” and “Without Acropora” (table split across two images).

The third column was created for studies that mentioned Acropora, as well as four or more other species. This column was subsequently disregarded in further calculations, due to the lack of specificity.

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Figure 4a - Means and standard deviations Lower order statistical tests were completed on both groups, as displayed in the table.

Group A (with Acropora)

Group B (without Acropora)

Mean

75.91

72.54

Median

76.55

75.24

Range

120.61

97.33

Standard Deviation

22.06

21.69

The graph shows the mean and standard deviations of the efficacy of each group, using 10% error bars.

The mean of group A is higher than that of group B, however, the standard deviation and median are similar. To determine whether the difference in overall efficacy in each group is statistically an F-test was applied.

Figure 4b - F-test ● Use to determine whether the variance is statistically significant ● Always in reference to the null hypothesis; HO: The use of Acroporid corals does not enhance the effectiveness of replantation studies and thus projects that focus on the genus will not produce a higher efficacy score. ● If P > 𝞪, the null hypothesis cannot be rejected ● F Value = Larger sample variance/Smaller sample variance 93

Group A

Group B

Sample Mean

75.914

72.537

Sample SD

22.059

21.690

Population size

64

50

Calculated P-Value

0.416

Critical F-Value (𝞪)

0.05

The calculated P-value > Critical F value, meaning the null hypothesis cannot be rejected.

However, a further statistical analysis must be applied to ensure the validity of these results.

Figure 4c - Unpaired T-test ● A statistical test of whether two sample means are equal. ○ This test is appropriate when the variances of the two populations are equal ● Always in reference to the null hypothesis ● If T > 𝞪, the null hypothesis is rejected (= H A)

Degrees of Freedom

112

Critical (𝞪) Value

1.980

T-value

0.817

The calculated T value < critical value. There is more than a 5% probability that the difference in means is due to chance. The null hypothesis is accepted.

Discussion: This research paper tested whether the use of Acroporid Corals in replantation projects altered the study’s overall efficacy. Due to the extensive understanding of the coral genus, as well as its historical dominance (Pavid, 2016), it was hypothesized that studies that primarily focused on Acropora would have higher levels of potency. After the efficacy was measured, using an arbitrary formula (figure 2), it was found that studies that used Acropora (group A) had a top score of 157.50, whereas the top score of those that didn't (group b) was 115.55.

However, the means of each group had a much smaller difference, in which group A exhibited a mean of 75.91, whereas group B produced a slightly lower average of 72.54.

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Accordingly, a more complex statistical analysis was required and proved that the difference in means nor variance was not statistically significant (figure 4), and thus the use of Acropora does not enhance the potency.

Statistical analysis To determine whether the null hypothesis could be rejected or not, higher-order statistical analyses were applied to the data. Generally, an F-test is used to determine whether the variance between two datasets is significantly different, and subsequently decides which type of t-test may be undertaken. An F-test was selected as both groups are highly variable, thus necessitating a comparison of the variation between datasets. The result of the F-test is always in reference to the null hypothesis (HO), where if the calculated p-value is less than the given α value, the null hypothesis must be rejected. When determined, using the mean, standard deviation and range of each group, the p-value equalled 0.415 (figure 4), where the critical α value was 0.05. As such, p-value > α value = HO could not be rejected. Furthermore, the unpaired t-test is a statistical test of whether two sample means are equal, in which the two groups are independent. As the means of group A did not affect those of group B, this test was selected to further attest to a conclusive finding.

If the calculated t-value > given α value, = HA the null hypothesis must be rejected. When solved, the T-value was 0.817, which was lower than the predetermined critical value of 1.980, (figure 4) which further asserted that the null hypothesis could not be rejected. As such, it was proven that the specific use of Acroporid corals does not enhance the efficacy of a replantation study. The data, however, is limited due to the specificity of the independent variable, as there are only two groups available for final comparison, meaning that there are no identifiable trends between studies. As such, further analysis into the types of study and location would enhance the overall value of this paper.

Accounting for the null hypothesis Despite limitations, the findings of this research are conclusive with peer-reviewed papers, in that the decline of Acropora is largely attributed to increasing anthropogenic pressures and is, therefore, more vulnerable in current climates (Sakai et al., 2019). As such, the newfound knowledge that Acoproa does not increase study success is consistent with the recognition of its recent mortality.

Whilst the Acropora genus has previously been identified as the most abundant coral, there has since been a notable decline in coral coverage globally, placing ⅓ of all coral at risk of extinction (Carpenter et al. 2018). Scientists are consequently endeavouring to discover new conservation methods which suit the majority of reefs, many of which are largely Acropora species. A study released in November 2020 showed that HSF1 had a vital role in Acroporid heat tolerance, uncovering a major breakthrough into coral genomics. This discovery subsequently inspired the rationale that studies using the coral would be more efficacious, as it demonstrated a complex understanding on a genomic level (Cleves et al., 2020). This paper, however, would not have influenced any of the studies found in the Coral Restoration Database, as they were all completed by 2020 & thus could not yet have applied this knowledge for techniques. Hence, the results of this paper showed that the null hypothesis 95

could not be rejected but with increasing genomic breakthroughs, this may change in future studies.

Validity, Reliability, Accuracy and Ethical Considerations The validity of an experiment refers to the controlled use of variables to address the aim (internal) and whether the succeeding conclusion can explain the phenomenon (external).

Validity was hence achieved as the experiment only used one independent variable (the use of Acropora), which was kept clearly defined. The following effect of the independent was measured through the dependent variable, being the efficacy score. The phenomenon demonstrated was that the use of Acropora did not have a statistically significant effect on the efficacy of studies. The conclusion was able to explain this finding, as methods have not yet been better adapted to suit this specific genus nor have they accounted for its vulnerability due to changing temperature and pH. However, the validity could be improved through the use of two control studies, which kept all variables within their experimental design the exact same but for the use of Acropora.

Reliability refers to the size of the data and the repeats with consistent results. Due to the nature of the research, no repeats were trialled, hence limiting the reliability. That being said, the final data contained a large sample size of 112, being reflective of many published and credible replantation studies conducted in the past 20 years.

The accuracy of the experiment was suppressed through the creation of an arbitrary metric for efficacy, as it is not comparable to other studies. However, in the production of the formula, skilful choices were made so as to reduce the risk of inflation/deflation from specific columns (figure 2). As this was a meta-analysis, all data was ethically obtained from a publicly accessible site. Nevertheless, the results may still have been influenced by bias when selected for use eg. all studies prior to 2000 were culled.

Limitations and Areas for Future Research By obligation, various metrics have been used from the given database to compare different coral restoration experiments. However, factors such as the survival rates may not be a suitable determinant of efficacy, due to the increased risk of bias. Published data has inadequate incentive to produce unsuccessful projects, likely due to general concerns of not appeasing the intended audience. As such, there is presumably a general bias favouring survival. Survivorship may also be inflated thanks to shorter experimental timeframes or areas of naturally high survival. Furthermore, an absence of uniformity in reporting success limits the ability to compare studies, even with values provided for each metric. The deficit of specificity in distinguishing fragment size, for example, hinders further ability to analyse projects and determine how to fairly compare them. An additional limitation of this research paper is the lack of detail explored in each individual study. Though the independent variable was identified and handled accordingly, there was no acknowledgement of differences in the methods beyond just the coral used. As such it is recommended for future studies to recognise the differences between each study and identify another factor to be considered as part of the

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independent variable. Genomics endeavours are also crucial to saving coral, whereby the more complex knowledge must be applied to restoration techniques.

Conclusion This study aimed to find whether the use of Acroporid corals enhanced the efficacy of the replantation projects. A meta-analysis of the ‘ Coral Restoration Database’ was conducted, in which an arbitrary metric to measure the efficacy of studies was applied. The following scores were divided into two groups; studies that focused on Acropora and those that didn’t and were compared. Though there was a slight difference in means, statistical analysis proved this difference to be insignificant. As such, the null hypothesis could not be rejected, meaning that the use of Acropora in replantation projects does not enhance the efficacy of the study.

This is likely due to the vulnerability of the coral genus in environments with increased temperature, pH or nutrient density, as exasperated by climate change. Furthermore, breakthrough understandings of Acropora have not yet been implemented into restoration techniques. This paper, therefore, asserts the need for further research into the factors that enhance replantation methods to provide coral reefs with a chance for survival.

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Reference List Bauer, A. (2021). Importance of Coral Reefs - Biodiscovery and the Great Barrier Reef Queensland Museum. Qld.gov.au; Queensland Museum. https://www.qm.qld.gov.au/microsites/biodiscovery/05human-impact/importance-of-coral-ree fs.html Cleves, P. A., , Bradford, J., & Bay, L. K. (2020). Reduced thermal tolerance in a coral carrying CRISPR-induced mutations in the gene for a heat-shock transcription factor.

National Academy of Sciences, https://doi.org/10.1073/pnas.1920779117 ‌ ramer, K. L., Jackson, J. B. C., Donovan, M. K., Greenstein, B. J., Korpanty, C. A., Cook, C G. M., & Pandolfi, J. M. (2020). Widespread loss of Caribbean acroporid corals was underway before coral bleaching and disease outbreaks. Science Advances, 6(17), https://doi.org/10.1126/sciadv.aax9395 Dance, A. (2019). These corals could survive climate change -- and help save the world's reefs. Nature Publishing Group. https://link.gale.com/apps/doc/A650920221/AONE?u=61ssc&sid=bookmark-AONE&xid=d 00d5864 ‌ DeCarlo, T. M., Hammerman, N. M., Pandolfi, J. M., Monroe, A. A., & Berumen, M. L. (2020). Nutrient-supplying ocean currents modulate coral bleaching susceptibility. Science Advances. https://doi.org/10.1126/sciadv.abc5493 Hoegh-Guldberg, O. (1999). Climate change, coral bleaching and the future of the world’s coral reefs. Marine and Freshwater Research. https://doi.org/10.1071/mf99078 International Union for Conservation of Nature. (2017). Coral reefs and climate change.

IUCN. https://www.iucn.org/resources/issues-briefs/coral-reefs-and-climate-change Pavid, K (2016). ​World's most important coral species in major decline. Natural History Museum. https://www.nhm.ac.uk/discover/news/2016/april/worlds-most-important-coral-in-major-decli ne.html Sakai, K., Singh, T., & Iguchi, A. (2019). Bleaching and post-bleaching mortality of Acropora corals on a heat-susceptible reef in 2016. PeerJ, 7, e8138. https://doi.org/10.7717/peerj.8138 ‌ ECORE International | Why coral reefs need our help. (2015). Secore.org.

S http://www.secore.org/site/corals/detail/why-coral-reefs-need-our-help.23.html US Department of Commerce, National Oceanic and Atmospheric Administration. (2021).

What is coral bleaching? Noaa.gov. https://oceanservice.noaa.gov/facts/coral_bleach.html

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Van Oppen, M. J. H., & Oakeshott, J. G. (2020). A breakthrough in understanding the molecular basis of coral heat tolerance. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.2020201117

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Investigating how the Height (from 0.0000-5.0000 ± 0.0005 m) from which Water falls impacts the amount of Hydropower (W) produced.

1. Context: Renewable energy sources such as hydropower are more sustainable, increasingly available, and important for addressing Climate Change. Coal power stations “can only extract 25% of the energy” from their “fuel source” while “hydropower stations… achieve over 90% efficiency,” (Clean Energy Council, 2012). However, “coal and gas… still represent close to 60% of global electricity supply,” (IEA, 2020). In 2019, hydropower provided 16% of the world's electricity and produced 5-7% of Australia’s supply for decades, (Australian Government, 2021). Despite increasing use of renewable energy, they remain the minority while society remains dependent on non-renewable energy sources that contribute to Climate Change. Consequently, as renewable energy is becoming increasingly necessary globally to reduce greenhouse gas emissions that contribute to Climate Change, this investigation explores the renewable energy source of hydropower which connects to Topic 8.

2. Research Question: How does the height (from 0.0000-5.0000 ± 0.0005 m) water falls from impact the power (W) produced in a turbine-powered electricity generator?

3. Aim: To investigate how hydropower (W) produced by turbine generators changes with the height water falls (from 0.0000-5.0000 ± 0.0005 m).

4. Background Research and Justification of Hypothesis: 4.1 Primary Energy (Renewable & Non-Renewable Energy Sources) & Secondary Energy.

There are two types of primary energy found in nature that have not been processed in any way, being either renewable or non-renewable energy. Non-renewable sources are “natural resources,” (Energy Education, 2020b) extracted from nature such as fossil fuels (i.e. oil, natural gas and coal). These fuels are not reusable and are finite, being depleted faster than they can be extracted. Future projections predict that oil availability will end by 2052, gas by 2060, and coal by 2090, (MAHB Admin, 2019). While “energy-rich and relatively cheap to process,” (National Geographic Society, 2019), nonrenewables pollute and contribute greenhouse gases to the atmosphere, thus contributing to climate change. Conversely, renewable sources are natural processes from which energy can be extracted and transformed in a useful way. Relying on Earth's systems rather than Earth’s resources, they replenish their energy supply, rendering them environmentally sustainable. Examples include solar radiation from the Sun, or water falling from great heights from which solar and hydropower can be harnessed respectively.

Secondary energy is produced when primary energy is transformed into other forms of energy such as electricity or kinetic energy, that are easier and more convenient to use, transport, and/or store.

4.2 Hydropower & Hydroelectric Power Plants: Hydropower is a renewable energy source derived from moving masses of water, infinite in supply because the water cycle is a consistently replenishing system. The kinetic energy of flowing water is converted into mechanical and electrical energy. It can store large amounts of energy and quickly produce power. However, it is dependent on location, requires drastic changes to the environment (eg; flooding of areas for dams), and initial costs as a result are high.

Hydroelectric power plants are generally turbine driven by water flow from a higher point. It requires an upper and lower reservoir of water. Energy can be stored as gravitational potential energy in the upper reservoir.

Sluice gates or valves control the water flow and thus the rate of electricity generation. When water from the upper reservoir passes through the turbine, the pressure on the turbine’s blades causes the shaft to rotate.

Figure 1: Hydroelectric Power Station, (Brown, 2020).

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The generator connected to the turbine then converts the motion, or kinetic energy, into electrical energy that can be transferred to power lines for people’s use. Though hydroelectric power plants typically only work in the one direction, a pumped storage system can reverse the direction of water flow to refill the upper reservoirs, (Brown, 2020).

4.3 Energy Store Changes: Gravitational Potential Energy ⇒ Kinetic energy ⇒ Electrical energy.

Table 1: Energy Store Changes in hydroelectric power plants by stage, (Brown, 2020).

Stage Energy Store Mechanical Change 1 Gravitational Potential Energy stored in water from the upper reservoir 2 ⇒ Kinetic Energy of flowing water 3 ⇒ Kinetic Energy of the turbine rotation and generator rotation of a coil in a magnetic field 4 ⇒ Electrical Energy the generator outputs (from the rotation of the coil that produces an emf).

4.4 The Turbine: The process’ efficiency depends on the turbine. In the investigation, Seeed Studio’s Micro Water Turbine (DC-5V) is used. Like in a hydroelectric plant, the pressure of flowing water rotates the turbine within the small device. Connected to the motor, it will rotate as well, generating electricity. The wires extending from the device can be connected to a circuit to measure voltage and current.

Table 2: Features and specifics of the turbine, (Seeed Studio, 2021 and Core Electronics, 2021b).

Feature Name Maximum Output Voltage (V) Maximum Output Current (mA) Resistance (Ω) Maximum Working Pressure Outlet Closed Highest Pressure Resistance

Feature 5.0 ± 0.3 150.0 10.5 ± 0.5 1.2 MPa 0.6 MPa

Feature Name Axial Clearance Approximately Start Water Pressure Flow range (dm3.min-1) Mechanical Noise Working Temperature Range

Feature 0.2 - 1.0mm 0.05 Mpa 3.5-20 ≤ 55dB 0 - 80 ° C

4.5 Variables affecting the production of Hydropower: Table 3: The principle behind determining hydropower (used in Table 13, Appendix 1-2).

A mass of water (m) is falling down a vertical height (h). The potential energy, or work that can be harnessed is mgh, where g represents gravity.

Thus…

Potential energy = work = kinetic energy = mgh Power (P) then equals work done over time. 𝑤𝑜𝑟𝑘 P = 𝑡𝑖𝑚𝑒 =

Figure 2: Water falling from a vertical height h has its potential energy converted into kinetic energy, which can be used to drive turbines, (Tsokos, 2014).

𝑚𝑔ℎ

• • •

𝑡

•

P = Power (W). m = mass (kg). h = height (m). g = gravity = 9.81 m.s-2.

The mass (m) then equals the product of the density of water (ρ) and the volume it occupies (∆V). 𝑚𝑔ℎ • m = mass = ρ∆V (kg).

P= 𝑡 -3 =

𝜌∆𝑉𝑔ℎ 𝑡

• •

ρ = the density of water = 1000 kg.m . ∆V = the volume it occupies (m3s-1).

The Quantity (Q), known as the flow rate, is the volume that passes through over time. Thus… 𝜌∆𝑉𝑔ℎ • ρ = the density of water = 1000 kg.m-3.

P= 𝑡 • ∆V = the volume it occupies (m3). ∆𝑉 = 𝜌𝑄𝑔ℎ • Q = quantity = flow rate = (m3.s-1). 𝑡

In terms of energy, Power is also known to equal voltage by current. Thus the equation…

• V = Voltage (V).

P = VI • I = Current (A). ∴ VI = 𝜌𝑄𝑔ℎ

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4.6 Modelling Hydroelectric Power Plants: An Isolated Representation of Reality.

The investigation will attempt to imitate hydroelectric power plants with water falling vertically from the upper to lower reservoir, on a smaller scale using pipes and a turbine. A carton (Table 6-7) attached to the top of the pipe(s) acts as the upper reservoir, whilst water from a garden hose will be poured into the carton and piped through in a constant stream. The pressure from garden hoses on average is 1.372.41 MPa, (Williams, 2017), and exceeds the turbine’s maximum working pressure. By pouring the water through varying heights of the pipe (from 0.0000-5.0000 ± 0.0005 m) and measuring the voltage and current output of the turbine, the impact of the height on hydropower production can be determined experimentally (P = VI). A theoretical value can be ascertained for comparison using the equation P = ρQgh, wherein only the flow rate is subject to change and will be determined in the experiment.

5. Hypothesis: Considering the equation VI = ρQgh (Table 3), voltage (V) and current (A) have a directly proportional relationship with the height (m) water falls from. Thus, as the height increases, so too should the voltage and current from the turbine generator and as such, the power (W).

Dependent

Independent

6. Variables: Table 4: Independent, dependent, and controlled variables and method of measurement or control.

Type Variable Method of Measurement or Control The height (± 0.0005 m) from which water Ensure height of pipe is measured from the same point, using falls from through a pipe before reaching a ruler (to the nearest mm) to improve validity and reliability the turbine. Six pipe heights: 0.0000of method. Subtracting the power from 0.0000m high will 5.0000m tall. The power at 0.0000 m high account for possible zero errors (systematic), as will be subtracted from the others. hypothetically there would be no stored energy to be converted into power.

The Power (W) produced from the turbine Use a correctly calibrated multimeter with a minimum of 2 generator. decimal places to discern a digital reading of voltage and current with small uncertainty for precision and reliability.

Controlled

Materials/equipment used.

Use the same equipment and materials detailed in Table 6 for each trial in the method for validity.

Intervals from which height of pipe is Ensure different heights are equal increments apart for measured (1.0000 ± 0.0005 m intervals). continuous data with a small uncertainty for precision.

-3 The density (ρ, kg.m ) of the liquid passing Ensure the same liquid, from the same source, is used to through the turbine. The density of water = avoid adversely affecting the validity of theoretical data.

1000 kg.m-3.

Perform experiment at same location where temperature remains relatively constant (around 26°C), to not affect water The location/conditions (ie; temperature). density.

The proportion by which the flow rate Flow rate is influenced by the hose pressure and height water (m3.s-1) changes. falls from. Ensure the hose’s tap turned the same proportion for all trials (a 3/4 turn). Using the same hose, the proportion Hose Pressure (MPa). On average, 1.37flow rate changes should remain consistent with height.

2.41 MPa, (Williams, 2017).

Acceleration due to gravity (g) = 9.81 m.s-1, Conduct the experiment in the same place. considering P = 𝜌𝑄𝑔ℎ.

Pipe diameter (mm). All pipes used will be Ensure the pipe is the same width so that only the length 25 mm in diameter. changes.

7. Risk Assessment: Table 5: Safety, environmental and ethical considerations.

Hazard Risk Method to Control Risk Slipping Spilt water can pose a risk for people Handle all liquids outside (in a garden) to minimise hazard. walking by, causing minor or severe injury the potential for water to go onto the floor or hard through slipping or falling. surfaces in a greater area.

Water near Water coming in contact with electronics Keep all electronics and the circuit as far from water electronics. poses danger of electrocution. as possible. If water is spilt, dry surface with towel.

Water spray. Water spray coming into contact with one’s Wear safety glasses and lab coat to protect oneself eyes, causing slight irritation. and one’s eyes from possible water spray.

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Wastage Wasting unnecessary amounts of of water. fresh water would have a negative environmental impact.

Use water from a hose outside and collect water in a bucket to be used to water plants in the gardens so no water is wasted. As flow rate will be measured in a 1dm3 measuring cylinder, secure it to base of bucket so any spillage beyond 1dm3 can be caught.

Cutting Potential for serious injury of cutting Cut the pipes with supervisor guidance and ensure no of pipes. oneself, particularly one’s hands. distractions while using the cutting equipment.

8. Equipment: Table 6: Quantity (Qty), Brand and Uncertainty of Equipment.

Quantity Equipment 1 2 1 1 1 1 1 1 1 5 4 2 1 1 1 1 1 1 1 1 1 1

Micro Water Turbine: Hydroelectric Generator 5V 3.00 m tall PVC Pipes (25mm in diameter) Digital Multimeter (Cat: QM-1500) Milliammeter Scale: 50 mA Scale: 500 mA 1000 cm3 Measuring Cylinder.

Measuring Tape Stopwatch Packet of Blu Tack Duct Tape (48mm by 54.8 m) Wires Alligator Clips Metal Bar from Retort Stand Hose connected to a tap outside Diffused LED (5mm PTH) Hacksaw Clamp 4 dm3 Distilled Water Carton Scissors Male Thread PVC Pipe Fitting Adapter Bucket 2B Pencil Protective: Safety Glasses, Gloves and Lab Coat

Brand Seeed Studio Holmon Jaycar PeakTech Polylab Stanley Fastime Bostik 3M Haines Educational Haines Educational Haines Educational Pope Sparkfun Stanley Trojan Glendale Packaging Studymate Uxcell House & Home Staedtler N/A

Uncertainty ± 0.3 V ± 0.0005 m ± 0.01 V ± 0.5 mA ± 5 mA ± 5 cm3 ± 0.0005 m ± 0.01 s N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

9. Diagrams: Table 7: Labelled Images of the Experiment (Method).

Method Part 1 Carton & Turbine Connector of Blu Micro Water Turbine (5V) Tack surrounded by Duct Tape (Seeed Studio, 2021 and Core (for the 0.0000 ± 0.0005 m Pipe).

Electronics, 2021b).

Method Part 2 Circuit Diagram (Terminal/battery represents the 5V Micro Water Turbine).

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Method Part 2 Blu Tack surrounded by Duct Tape Connector between 2 Pipes with support metal bars

Method Part 3 1.0000-3.0000 ± 0.0005 m Pipes 4.0000-5.0000 ± 0.0005 m Pipes

10. Method: Part 1: Setting Up the Equipment for 0.0000m, 1.0000m, 2.0000m, and 3.0000m tall Pipes.

1. Cut the distilled water carton in half and discard the base.

2. Mark along the edge of one 3.0000m pipe at 1.0000m high using a pencil and measuring tape.

3. Secure the pipe with a clamp to ensure it does not move whilst cutting the pipe.

4. Cut the pipe along the marking made using a hacksaw.

5. Remove the now 1.0000m and 2.0000m high pipe from the clamp.

6. Place the end of the turbine generator against one end of the 1.0000m pipe.

7. Place Blu Tack all along the edge connecting the turbine generator with the 1.0000m pipe. This should also help prevent water from leaking in the gap.

8. Secure the turbine generator and Blu Tack to the bottom of the 1.0000m high pipe with duct tape, going around the pipe at least 4 times. This should also help prevent water leaking in the gap.

9. Place the lid opening of the water carton against the top end of the 1.0000m high pipe and secure using Blue Tack and duct tape to help prevent water from leaking.

10. Complete Part 3.

11. Repeat Steps 6-10 for the 2.0000m and then 3.0000m pipe lengths.

12. Repeat Steps 6-10 for the 0.0000m pipe length, attaching the water carton and turbine together directly using Blue Tack and duct tape to help prevent water from leaking.

Part 2: Setting Up the Equipment for 4.0000m and 5.0000m tall Pipes.

1. Connect a 1.00m pipe to 3.0000m pipe as previously to form a 4.0000m pipe.

2. Duct tape two metal bars from the retort stands on either side of where the two poles joined to improve the rigidity of the structure.

3. Connect a 2.0000m pipe to 3.0000m pipe as previously to form a 5.0000m pipe.

4. Repeat steps 6-10 from Part 1 with each pipe length.

Part 3: Obtaining Results 1. Set up equipment as per diagram (see Table 7) and described in Part 1 using the 1.0000m tall pipe.

2. Attach a 1000 cm3 measuring cylinder to the base of a bucket.

3. Hold the pipe over the measuring cylinder.

4. Block the turbine’s opening at the base of the pipe.

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5. Turn on the hose (3/4 turn) and wait until the water carton half is around half full.

6. Unblock the turbine’s opening to allow water to begin flowing into the pipe.

7. Record the time taken for 1dm3 of water to be filled using the measuring cylinder and stopwatch.

8. Record the voltage and current using the Multimeter and Milliammeter, respectively.

9. Turn off the hose.

10. Empty the measuring cylinder and bucket of water into the garden.

11. Repeat steps 1-10 four more times.

12. Repeat steps 1-11 for each other pipe length.

13. Clean up the workstation.

11. Quantitative Results: Halfway through collecting data, the current exceeded the 50mA scale. The 500mA scale had to be used to measure current at 4.0000-5.0000m heights, seen in separate tables with different uncertainties to maintain precision in the preceding values taken with the 50mA scale (0.0000-3.0000m heights).

Table 8: Raw data for the Current (±0.5 mA), Voltage (± 0.01 V) and the Time to Fill 1 dm3 (± 0.01 s) when water falls from a Pipe of Heights (± 0.0005 m) 1.0000m, 2.0000m and 3.0000m tall.

Height (± 0.0005 m) Trial Current (± 0.5 mA) Voltage (± 0.01 V) Time to Fill 1 dm3 (± 0.01 s) 0.0000 1 0.0 0.00 35.40 2 0.0 0.00 35.51 3 0.0 0.00 35.45 4 0.0 0.00 35.35 5 0.0 0.00 35.34 1.0000 1 11.0 2.00 15.14 2 10.0 2.00 14.86 3 11.0 2.00 15.09 4 11.0 2.00 15.02 5 10.0 2.00 14.94 2.0000 1 28.0 2.08 10.55 2 28.0 2.08 10.60 3 28.0 2.07 10.50 4 27.5 2.08 10.36 5 28.5 2.07 10.59 3.0000 1 38.0 2.13 8.50 2 39.0 2.12 8.66 3 38.5 2.09 8.62 4 38.0 2.10 8.55 5 38.0 2.10 8.58 Table 9: Raw data for the Current (± 5 mA), Voltage (± 0.01 V) and the Time to Fill 1 dm3 (± 0.01 s) when water falls from a Pole of Heights (± 0.0005 m) 4.0000m and 5.0000m tall.

Voltage (± 0.01 V) Time to Fill 1 dm3 (± 0.01 s) Height (± 0.0005 m) Trial Current (± 5 mA) 4.0000 1 50 2.12 7.36 2 55 2.13 7.53 3 50 2.11 7.46 4 50 2.12 7.39 5 55 2.12 7.44 5.0000 1 60 2.14 5.42 2 60 2.14 5.40 3 60 2.14 5.41 4 60 2.14 5.45 5 60 2.14 5.42

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12. Qualitative Results: Table 10: Qualitative data observed during the experiment.

Process Observation Water initially After the hose was turned on, the base of the turbine was blocked to allow the filling the carton. carton at the top to fill up to halfway. When turbine was unblocked, for the carton to remain consistently filled to halfway, the tap had to be adjusted consistently from a 3/4 turn.

Water falling The water flow when it exited the turbine at the base of the pipe was laminar through the pipe (smooth and regular). As the height increased, the same volume of water and exiting turbine. filled the measuring cylinder in a shorter time and the flow seemed stronger.

13. Data Processing: Table 11: The Average; Current (± 0.5 mA & ± 0.0005 A), Voltage (± 0.01 V) and Time to Fill 1dm3 (± 0.01 s) when water falls from heights 0.0000-3.0000m ± 0.0005 m tall, (Appendix 1-2).

Height Average (± 0.0005 m) Current (± 0.5 Current (± 0.0005 Voltage (± 0.01 Time to Fill 1dm3 (± 0.01 mA) A) V) s) 0.0000 0.0 0.0000 0.00 35.41 1.0000 10.6 0.0106 2.00 15.01 2.0000 28.0 0.0280 2.08 10.52 3.0000 38.3 0.0383 2.11 8.58 Table 12: The Average; Current (± 5 mA & ± 0.005 A), Voltage (± 0.01 V) and the Time to Fill 1dm3 (± 0.01 s) when water falls from heights 4.0000-5.0000m ± 0.0005m tall, (Appendix 1-2).

Height Average (± 0.0005 m) Current (± 5 mA) Current (± 0.005 A) Voltage (± 0.01 V) Time to Fill 1dm3 (± 0.01 s) 4.0000 52 0.052 2.12 7.44 5.0000 60 0.060 2.14 5.42 Table 13: The Experimental Power (W), Average Flow Rate, Q (m3.s-1) & Theoretical Power (W), for each height (0.0000-5.0000 ± 0.0005 m), (Table 3, Appendix 1-2).

Height (± 0.0005 Experimental Power, P Average Flow Rate, Q Theoretical Power, P 3 -1 m) (W) (m .s ) (W) 0.0000 0.0000 ± 0.0000 2.82 x 10-5 ± 1.49 x 10-7 0.0000 ± 0.0000 1.0000 0.0212 ± 0.0011 6.66 x 10-5 ± 3.77 x 10-7 0.6536 ± 0.0040 2.0000 0.0581 ± 0.0013 9.51 x 10-5 ± 5.66 x 10-7 1.8650 ± 0.0116 -5 -7 3.0000 0.0807 ± 0.0014 11.65 x 10 ± 7.18 x 10 3.4293 ± 0.0217 -5 -7 4.0000 0.110 ± 0.011 13.45 x 10 ± 8.53 x 10 5.277 ± 0.034 5.0000 0.128 ± 0.011 18.45 x 10-5 ± 12.62 x 10-7 9.050 ± 0.063 Table 14: Percentage error for each height (0.0000-5.0000 ± 0.0005 m), (Appendix 3).

Height (± 0.0005 m) 0.0000 1.0000 2.0000 3.0000 4.0000 Percentage Error (%) 0.0000 96.7562 96.8832 97.6457 97.911

5.0000 98.581

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14. Graphs: Table 15: The Relationship between the Height (0.0000-5.0000 ± 0.0005m) from which water falls and the Theoretical (W) and Experimental Power (W) produced.

Graph 1: Experimental Power, P (W) Graph 2: Theoretical Power, P (W)

15. Analysis: 15.1 Describing Trends: The line of best fit in Graph 1 (LOBF-1) indicates that there is a linear directly proportional relationship between the height from which the water falls through the pipes (0.0000-5.0000 ± 0.0005m) and the power produced (W). While not all values pass within the error bars of the graph (from 1.0000-2.0000 ± 0.0005m heights), the same number of points are above and below the line and uncertainties are small, indicating high precision (± 0.0005m for height and ± 0.0000-0.011 W for experimental power). This is reinforced by the high r2 value (r2 = 0.9928) and correlation constant (r = 0.9964), indicating a strong correlation. Thus, the LOBF-1 in Graph 1 supports the hypothesis, power increases as height increases.

Similarly in Graph 2 detailing the expected theoretical power (W) produced, the line of best fit (LOBF2) has a high r2 value (r2 = 0.9934), indicating a strong correlation. While not all values pass within the error bars of the graph (at 0.0000m, 2.0000m, 4.0000m and 5.0000m heights), the same number of points are above and below the line and the uncertainties are small, indicating high precision (± 0.0005 m for height and the range ± 0.0000-0.063 W for theoretical power). Whilst the power is still suggested to increase as the height increases, the LOBF-2 is a curved line, indicating a non-linear relationship between the height and power produced, slightly deviating from the predicted directly proportional relationship, considering the equation VI = ρQgh (Table 3).

Percentage Error =

| 𝒍𝒊𝒕𝒆𝒓𝒂𝒕𝒖𝒓𝒆 𝒗𝒂𝒍𝒖𝒆−𝒆𝒙𝒑𝒆𝒓𝒊𝒎𝒆𝒏𝒕𝒂𝒍 𝒗𝒂𝒍𝒖𝒆| 𝒍𝒊𝒕𝒆𝒓𝒂𝒕𝒖𝒓𝒆 𝒗𝒂𝒍𝒖𝒆

× 𝟏𝟎𝟎

The varying scales on the y-axis required to present the data in Graphs 1 and 2 alongside the differing shapes of LOBF-1 and LOBF-2 indicate low accuracy when comparing the expected theoretical and experimental power produced. This is reinforced by the high percentage error (Table 14) for heights 1.0000-5.0000 ± 0.0005 m high, (the 0.0000 m height the exception), and the theoretical values (m = 0.3240) greater rate of increase than the experimental values (m = 0.0266).

15.2 Explaining Trends: In Graph 1, the larger error bars for 4.0000-5.0000 ± 0.0005m heights reflect the change of scale required to measure the current (from 50mA to 500mA) for the experimental power since it exceeded the limit using the smaller scale. The different shapes of LOBF-1 and LOBF-2 can be attributed to the varying scales of power produced, (0.0000-0.1600 W in Graph 1 and 0.0000-10.0000 W in Graph 2).

Due to the larger scale in Graph 2, what initially appears as a linear relationship in Graph 1 could potentially imitate the curve of Graph 2 as more power is produced. The difference was perhaps also because the flow rate (Q) did not change proportionally as the height increased, (Table 13).

The large percentage error is perhaps indicative of the efficiency of the turbine and the effectiveness of the height. The calculation of theoretical power assumed all the energy produced by the falling water 107

converted into electrical power (Table 1 and Table 3), and that the turbine and generator were both 100% efficient. In reality, efficiency is “always less than 100%,” (Open University, 2020). With proper infrastructure, efficiency is around 80-90%, (Blakers, Lu, & Stocks, 2020). Considering this is a model using pipes, duct tape, Blu Tack and a 5V turbine-generator, the system would likely be far less efficient. As water passes through the pipe and turbine generator, at each stage depicted in Table 1 there will be a loss of energy to the water, reservoir walls (the carton and pipe walls), turbine, and generator due to sound, heat and friction (Open University, 2020), since “water is doing work against the surface of pipes and turbine blades,” (Brown, 2020) and the turbine has mechanical noise ≤ 55dB (Table 2).

The turbine itself has a resistance of 10.5 ± 0.5 Ω which would restrict the current (A) that could pass through the circuit, limiting the experimental power measured.

Unlike a real hydroelectric power plant, not all water used to calculate flow rate nor experimental power dropped from the expected height, for both the carton and pipe were full before the investigation began.

Some dropped from anywhere between 0.0000m and the total height. The 0.0000 m height was the exception with no percentage error considering it did not produce any power. At 0.0000 m high, there would be no potential energy in water to convert to kinetic and then electrical energy (Table 1).

Experimentally, this is reinforced by the flow rate at 0.0000 m high (2.82 x 10-5 ± 1.49 x 10-7 m3.s-1), which was not within the turbine-generator’s flow range required (3.5-20 dm3.min-1) to produce power.

The large discrepancy between the theoretical and experimental power produced is also because the voltage (V) measured was limited to the LED attached in parallel to the multimeter [with voltage range 2.0-2.4 V, (Core Electronics, 2021a)]. All measured values for voltage (V) fell within the range, and were perhaps limited to this measure. In this case, the experimental power derived from the circuit (Table 13) would be the power needed to light the LED, not the power produced by the turbine itself.

The theoretical power measured, based on the flow rate of water exiting the turbine, was perhaps not an accurate representation of the power produced by the turbine. Rather, it contained the excess energy that did not spin the turbine or generator to convert kinetic energy into electrical energy and power. The useful power converted to electrical energy could be much smaller. Nonetheless, excess power would also increase proportionally based on the systems efficiency. The fact water leaving the turbine came out increasingly quickly as the height increased, (Table 10-12), thus increasing flow rate and theoretical power, (Table 13), still demonstrated the general trend that power increased as the height increased.

16. Conclusion: The precision and accuracy of the experimental and theoretical data was adversely affected due to systematic and random errors. This included the; turbine’s resistance limiting the current (A), inefficiency of energy conversion, ineffectiveness of total height, LED voltage limit, and the measure of water’s flow rate showing excess energy rather than useful energy which did not have a proportional increase. The degree of precision is high with small uncertainties and a strong correlation between height and the power produced, however the accuracy when comparing the expected theoretical and experimental values was low, apart from the 0.0000m height which produced no power overall, as expected. This is reflected in the percentage error values (%, Table 14).

While it is yet to be clarified whether height and power have a linear directly proportional or non-linear relationship, the general trend in the literature values is nonetheless reflected in the experimental values.

The data in both Graph 1 and 2 shows there is a definite increase in power with increasing height, as expected considering the equation P = VI = 𝜌𝑄𝑔ℎ. Thus, this investigation’s data validly fulfills the aim to determine how hydropower (W) produced by turbine generators changes with the height water falls (from 0.0000-5.0000 ± 0.0005 m).

While hydroelectric power stations are dependent on location, require drastic changes to the environment (ie; flooding of areas for dams), and initial costs as a result are high, it is worth investing in these systems in the long run to reduce people’s reliance on fossil fuels and their negative environmental impact, contributing to air pollution and global warming. The theoretical energy produced in excess as part of the 10-20% inefficiency of typical hydropower systems, is testament to the large amounts of power that can be produced with the assumed 80-90% efficiency, (Blakers, Lu, & Stocks, 2020) with greater heights and proper infrastructure on a larger scale (most likely as a dam), without contributing to Climate Change.

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17. Evaluation: Though the experiment can be improved, it remained somewhat valid and reliable.

17.1 Literature Values.

As identified in the Analysis and Conclusion, the data followed the same general trend. The precision is high with small uncertainties and a strong correlation between height and the power produced.

However, there was a large discrepancy between the theoretical and experimental values, indicating low accuracy. This is due to both systematic and random errors, as identified below. While not all individually significant, collectively they can have a large impact.

17.2 Systematic and Random Errors: Validity, Accuracy, Reliability and Precision.

Only one independent and dependent variable was involved in the experiment (the height water fell and the power produced, respectively). The weather as a random error could not be controlled completely. By performing the investigation in the same location (outside, Table 5), conditions like temperature fluctuations were limited. Temperature and water density were thus controlled, unlikely to have adversely affected reliability of data to a significant degree. However, not all other variables were controlled, adversely affecting the validity of the investigation and reliability of the data.

While using the same hose, the degree the hose’s valve turned did not remain the same for each height and changed to maintain the water carton being half full. While the hose pressure was uncontrolled, the water carton modelled the upper reservoir of a hydropower plant. Since water was not directly from the hose, its pressure did not affect the flow rate, nor impede the investigation’s validity. However, the fact the pipe was capped to allow the pipe to fill before the carton filled to halfway meant not all the water used to calculate the flow rate and the theoretical power dropped from the expected height. The pressure on one side of the pump was thus different for all trials. This likely prevented the flow rate (Q) and theoretical power from increasing proportionally as height increased. This systematic error meant the intervals height increased, water pressure, and increase of flow rate was uncontrolled, adversely affecting the investigation’s validity and accuracy by contributing to the discrepancy between the experimental and theoretical values.

The method allowed for appropriate data pertaining to the relationship between the height water falls and the power produced to be obtained that fulfilled the aim and supported the general trend of the hypothesis. Data credibly coincided with background research. However, while the proposed method was valid theoretically considering the equation P = 𝜌𝑄𝑔ℎ, in practice the efficiency of the turbine, the height’s effectiveness, and energy losses needed to be considered.

The measure of water’s flow rate based on water exiting the turbine showed excess energy rather than useful energy. The actual theoretical power produced could be more or less than the data showed. The turbine itself also limits the measure of current with a resistance of 10.5 Ω, impeding efficiency. As systematic errors, they adversely affected the validity of the percentage error (%, Table 14) considering the theoretical calculation assumes 100% of all energy converted into electrical energy. There were also potential energy losses within the pipe and turbine to friction, heat, and sound, (Brown, 2020). Further, the turbine would not work at any height that does not produce a flow rate within the turbine’s flow range (3.5-20 dm3.min-1) like the 0.0000m height, impeding the accuracy of power produced. Thus, not all heights are as effective as the total would imply as some energy goes into turning the turbine. Additionally, the voltage of experimental power was limited by the turbine (5V), and the LED (range of 2.0-2.4 V), adversely affecting experimental values validity.

The investigation involved five trials under five different conditions (from 0.0000-5.0000m heights), and averages were found and used. However due to time constraints, the investigation was only repeated once with the same pipe model for each height, meaning any discrepancies in how the pipe was built and attached to the turbine and generator would impact all trials and the average. The flow rate’s (Q) disproportional increase, adversely affecting the validity of the theoretical values, is compounded by the room for human error with reaction time. This was a random error that adversely affected the accuracy and reliability of the data timing how long it took for 1dm3 of water to fall, (Table 8-9). Additionally, when reading the volume on the measuring cylinder, there was potential for parallax error. As a systematic error, it adversely affected the validity of the data. Further, there was some distance between where water would exit the turbinegenerator, and the measuring cylinder. While a very small amount, some water vapour and droplets likely exited the turbine-generator in a wider radius than the measuring cylinder, and were not considered, impeding data’s reliability.

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The experimental uncertainties were small. Correctly calibrated scientific equipment was used where possible. Whilst initially 2 multimeters were to be used to measure both the voltage and the current to 2 decimal places (d.p), they had difficulty measuring current. As a result, a milliammeter (mA) was used.

While less precise (1d.p using the 50mA Scale & no d.p using the 500mA scale), impeding the reliability of the current, data was collected. Halfway through collecting the data using the 50mA scale, the current exceeded the scale. From 4-5m high, the 500mA scale had to be used, (Table 8-9 & 11-12). This inconsistency in equipment used and discrepancy in uncertainties adversely affected the precision and reliability of the data gathered, more so for 4.0000-5.0000m, as evident in Graph 1.

17.3 Limitations of Models.

With a range of 0.0000-5.0000m and a carton used to imitate an upper reservoir, this investigation is a scientific model that conceptually represents change in heights impact on power produced in hydroelectric systems on a much smaller scale. Whilst a scientific model is useful to limit the number of external and uncontrollable variables that could adversely affect the validity of the results, it is not a true representation of reality, limiting the investigations’ ability to be compare these samples data to real hydroelectric systems.

Temperature, weather changes, rainfall, nitrites, agricultural run-off and turbidity would constantly vary and influence the density of water in dams and other sources that power hydroelectric systems. Further, this investigation only considered one turbine, and was influenced by its efficiency and limits for current and voltage. Despite Blu Tack and duct tape connectors, there is still potential water escaped before passing through the turbine. A valve or sluice gate needed to be imbedded between the carton and pipe to allow the carton to fill to halfway, like an upper reservoir, without the pipe itself being filled. The small scale, simple design, lack of a valve, and environmental limits may have adversely affected the data’s validity and applicability to larger hydroelectric systems.

17.4 Improvements.

To improve the validity of the investigation considering both the efficiency of the turbine and effective height, rather than total height, the equation P = 𝜌𝑄𝑔ℎ can be changed to P = 𝜂𝜌𝑄𝑔ℎ𝑒 , where η = the efficiency of the hydroelectric turbine and generator, while he = the effective height to produce power, (Open University, 2020). A valve or sluice gate like structure can be imbedded between the carton and pipe to allow the carton to fill to halfway, like an upper reservoir, without the pipe itself being filled. To avoid connecting a 1m and 2m pole to a 3m pole to get 4-5m tall heights, a complete pole of those heights can be used, minimising water leakage and improving the validity of the method and model.

Base flow rate calculations on the difference between the time it takes for water to fall from different heights with and without the turbine attached to discern the power that is used by the turbine. To mitigate human error regarding potential parallax errors and varying reaction times, use calibrated scientific equipment such as a recording device set up on flat ground in line with the reading of 1dm 3 on the measuring cylinder that can be used to pinpoint the exact time it fills, improving the reliability of timing and theoretical data. The method should also be changed to remove the distance between the turbine’s exit and the measuring cylinder’s opening to ensure all water leaving the turbine enters the measuring cylinder, improving the validity of theoretical values based on flow rate.

Considering the multimeters trouble measuring current and need to change scales, more capable digital equipment with a larger range that is calibrated correctly to measure values to a minimum of 2 d.p should be used consistently so the scale is not limited, improving data’s precision and reliability. With the turbine’s resistance known (10.5 Ω), it can be used with current to discern it’s efficiency considering how much power was dissipated by the turbine using the equation V = IR, ∴ P = I2R. To circumvent the voltage limit for the LED, a multimeter can be attached directly to the turbine in parallel to measure the voltage produced.

Conversely, a lightbulb or LED with a higher voltage range that exceeds the turbine so as not to confuse the two limits can be used to measure power produced.

Systematic

Table 16: Sources of Error.

Type Error Impact Distance between Some water exited the turbine in a water exiting the wider radius than the measuring turbine-generator, cylinder, minimally adversely and the measuring affecting the validity of the method cylinder. and theoretical data.

Improvement Method changed to minimise distance between turbine’s exit and the measuring cylinder.

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Assumption entire Adversely affected the validity of a system was 100% comparison between the theoretical and efficient. experimental results.

Turbine resistance (10.5 ± 0.5 Ω).

LED Voltage Limit (from 2.02.4 V).

Systematic

Turbine’s flow range (from 3.520 dm3.min-1) and consideration of effective height.

Interval’s height increased uncontrolled.

The flow rate’s disproportionate increase.

Water pressure uncontrolled.

Random

Flow rate based on water exiting the turbine showed excess energy rather than useful energy.

Water leakage and pole connectors.

Parallax error reading volume of the measuring cylinder.

Improperly measured time due to human error; ie varying reaction times.

Inconsistency of equipment used and the uncertainties with equipment.

Temperature fluctuations.

The turbine’s resistance limits the measure of current, adversely affecting the efficiency of the turbine and validity of the experimental power alongside its comparison to theoretical values.

The experimental power is perhaps limited by the LED which, connected in parallel to the multimeter, impedes the validity of experimental values (P = VI).

The turbine would not work at any height that does not produce a flow rate within range. Not all heights are as effective as the total would imply as some energy turns the turbine.

Adversely affects the accuracy of data.

The pipe was capped. Not all water used to calculate flow rate and theoretical power dropped from expected height. Likely prevented flow rate and theoretical power from increasing proportionally as the height increased. Adversely affected validity and accuracy of the investigation by contributing to the discrepancy between experimental and theoretical values.

Unclear whether actual theoretical power produced could be more or less than was calculated and compared with experimental results, adversely affecting the validity of the percentage error (%, Table 14).

Despite Blu Tack and duct tape connectors, there is still potential some water escaped while passing through the pole before reaching the turbine.

The error adversely affects the accuracy of the measurements of time, and thus the validity of the theoretical power expected to be produced.

Any variation in time would propagate all errors in theoretical calculations, thus adversely affecting the reliability and precision of results gathered.

The measuring tape used had an uncertainty of ± 0.0005m, measuring cylinder ± 5cm3, micro water turbine ± 0.3V, stopwatch ± 0.01s, the multimeter ± 0.01V, and the milliammeter ± 0.5mA and ± 5mA depending on scale. These uncertainties adversely affect the reliability and precision of results.

Affects the density of the water and minimally adversely affects the reliability of theoretical and experimental data.

Determine the efficiency of the turbine and the power lost before exiting the turbine. Subtract from theoretical data.

Known turbine resistance (10.5Ω) can be used with measured current to discern efficiency and how much power was dissipated by the turbine (V = IR, ∴ P = I2R).

Either attach multimeter directly to the turbine or a lightbulb or LED with a higher voltage range than the turbine to not confuse the two limits.

To improve the validity of the investigation considering both the efficiency of the turbine and effective height, rather than total height, the equation P = 𝜌𝑄𝑔ℎ can be changed to P = 𝜂𝜌𝑄𝑔ℎ𝑒 , wherein η = the efficiency of the hydroelectric turbine-generator, while he = the effective height to produce power. A valve or sluice gate like structure can also be imbedded between the carton and pipe to allow the carton to fill to halfway, like an upper reservoir, without the pipe itself being filled.

Base flow rate calculations on difference between time it takes for water to fall from different heights with and without turbine attached to discern power used by the turbine.

A complete pole of those heights can be used, minimising water leakage and improving the validity of the method and model.

Use calibrated scientific equipment such as a recording device set up on flat ground in line with the reading of 1dm3 on the measuring cylinder that can be used to pinpoint the exact time it fills, improving the reliability of timing and theoretical data.

Use more capable calibrated scientific equipment that has a larger range and can measure values to a higher number of decimal places (such as a multimeter for current).

Be consistent in what equipment and scale is used for each variable being measured.

Perform the investigation in a more controlled environment that remains convenient to capture wasted water to use ethically in gardens to improve reliability.

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Appendix: Appendix 1: The experimental power (W) for each height (P = VI, Table 11-13).

Height

(±0.0005m) Voltage (±0.01 V)

0.0000

0.00

Average Current (A)

Time, t (±0.01 s)

Experimental Power, P (W) (P = VI)

0.0000 ± 0.0005

35.41

P = 0.00(0.0000) = 0.0000000

Uncertainty (m3s-1) ∆𝑽 ∆𝑰 P (𝑽 + 𝑰)

∆𝑉

∆𝐼

0.0000( 𝑉 + 𝐼 ) = 0.000000 0.01

0.0005

1.0000

2.00

0.0106 ± 0.0005

15.01

P = 2.00(0.0106) = 0.0212000

2.0000

2.076

0.0280 ± 0.0005

10.52

3.0000

2.108

0.0383 ± 0.0005

8.582

P = 2.076(0.0280) = 0.0581280 0.058128(0.01 + 0.0005) = 0.001318 2.08 0.0280 P = 2.108(0.0383) = 0.0807364 0.0807364( 0.01 + 0.0005) = 0.001437

4.0000

2.12

0.0520 ± 0.0050

7.436

P = 2.12(0.0520) = 0. 1102400

0.0807364(2.12 + 0.0520) = 0.011120

5.0000

2.14

0.0600 ± 0.0050

5.42

P = 2.14(0.0600) = 0.1284000

0.1284000(2.14 + 0.0600) = 0.011300

0.0212(2.00 + 0.0106) = 0.001106

2.108 0.01

0.0383 0.005

0.01

0.005

Appendix 2: The average flow rate (m3s-1, volume = 0.001± 5x10-6m3) and, and theoretical power (W, P = ρ𝑄𝑔ℎ, water density=1000 kg.m-3, gravity=9.81 m.s-2) for each height, and their uncertainties, (Table 3,11-13).

Height (±0.0005 m) 0.0000

1.0000

2.0000

3.0000

4.0000

5.0000

Average Flow Rate, ∆𝑽 Q = 𝒕 (m3s-1)

Uncertainty (m3s-1) ∆𝑽 ∆𝒕 Q (𝑽 + 𝒕) −6

0.001

5 × 10 0.01 Q = 35.41 2.82406 … x 10-5 ( 0.001 + 35.41) = 2.8240609997…x 10-5 = 1.4917837051601… x 10-7 ≈ 2.82 x 10-5 ≈ 1.49 x 10-7 0.001 5 × 10−6 0.01 Q = 15.01 6.662225 … x 10-5 ( 0.001 + 15.01) -5 = 6.662225 …x 10 = 3.7749650… x 10-7 ≈ 6.66 x 10-5 ≈ 3.77 x 10-7

Theoretical Power, P (W), (P = ρ𝑸𝒈𝒉) P = (1000)(Q)(g)(0.00) = 0.0000

Uncertainty (W) ∆𝑸 ∆𝒉 P (𝑸 + 𝒉) ∆𝑄

∆ℎ

0.00( 𝑄 + ℎ ) = 0.0000

3.77… x 10−7 0.0005 P = (1000)(6.66... x 100.65... (6.66… x 10−5 + 1.00 ) 5)(9.81)(1.00) = 0.004030022845085... = 0.653564… ≈ 0.0040300228 ≈ 0.6536 −6 -5 0.001 5.66… x 10−7 0.0005 5 × 10 0.01 Q = 10.52 6.662225 … x 10-5 ( 0.001 + 10.52) P = (1000)(9.505… x 10 ) 1.86... (9.51… x 10−5 + 2.00 ) (9.81)(2.00) = 1.8650190… = 0.01156418169778… = 9.5057034…x 10-5 = 5.6564356865069 … x 10-7 ≈ 1.8650 -7 ≈ 0.0115641816 ≈ 9.51 x 10-5 ≈ 5.66 x 10 -4) 0.001 7.183… x 10−7 0.0005 5×10−6 0.01 P = (1000)(1.1652... x 10 -4 3.42... (11.65… x 10−5 + 3.00 ) Q = 8.582 1.1652295... x 10 ( 0.001 + 8.582) (9.81)(3.00) -4 = 0.02171378532451… = 1.165229550221...x 10 = 7.1839076558162 … x 10-7 = 3.429705 … ≈ 0.0217137853 ≈ 11.65 x 10-5 ≈ 7.18 x 10-7 ≈ 3.4297 −6 -4 0.001 8.53… x 10−7 0.0005 5×10 0.01 Q = 7.436 1.344809… x 10-4 ( 0.001 + 7.436) P = (1000)(1.344… x 10 ) 5.27... (13.45… x 10−5 + 4.00 ) (9.81)(4.00) = 0.034141380663859… = 1.3448090371…x 10-4 = 8.53256 … x 10-7 = 5.277030661646...

-7 ≈ 0.0341413807 ≈ 13.45 x 10-5 ≈ 8.53 x 10 ≈ 5.2770 −6 0.001 0.0005 5×10 0.01 P = (1000)(1.845… x 10-4) 9.04... (12.63… x 10−7 + 5.00 ) Q = 5.42 1.8450… x 10-4 ( 0.001 + 5.42) 18.45… x 10−5 (9.81)(5.00) =9.049815498...

-4 = 0.06285113560545… = 1.84501845018…x 10 = 1.26291853324 … x 10-6 ≈ 9.0498 ≈ 0.0628511356 ≈ 18.45 x 10-5 ≈ 12.63 x 10-7

Appendix 3: Percentage Error for each height (0.0000-5.0000 ±0.0005m), (Table 14).

Height (± 0.0005 m) 0.0000 1.0000 2.0000 3.0000 4.0000 Percentage Error (%) Theoretical = − 𝐸𝑥𝑝𝑒𝑟𝑖𝑚𝑒𝑛𝑡𝑎𝑙 Experimental (𝑇ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 × 100) 𝑇ℎ𝑒𝑜𝑟𝑒𝑡𝑖𝑐𝑎𝑙 ∴ 0.0000000

0.6536−0.0212 × 0.6536

100

= 96.75624873

1.8650−0.058128 × 1.8650

100

= 96.8832489

3.4297−0.0807364 × 3.4297

100

= 97.6456684

5.2770−0.11024 × 5.2770

100

= 97.9109463

5.0000 9.0498−0.1284 × 100 9.0498

= 98.5811865

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Candidate Code: jmw082

Determination of Vitamin C Mass by Titration in Different Fruits Research Question: Out of five fruits (oranges, blueberries, red capsicum, green kiwifruit and yellow kiwifruit) which has the greatest vitamin C mass (±*)? *for error refer to table 6 (propagated uncertainty) Aim: To investigate the various vitamin C masses (mg) of different fruits (orange, blueberry, green and yellow kiwifruit).

Introduction: I have always been interested in nutrition and the way the human body works. In the future I hope to study in the medical field which requires an interest in various processes in the human body. Hence, I was motivated to pursue further research into vitamin C and its effect on the human body as I have researched the ill effects of vitamin deficiencies and have an interest in this area of nutrition. Moreover, through this experiment I will be able to see what kind of fruit or vegetable is best suited for a high vitamin C diet. Additionally, this investigation may allow me to understand which kind of fruits are best suited to grow in low and middle income nations so that these individuals can still receive sufficient vitamin C intake and maintain a healthy lifestyle.

Background Information Vitamin C, also called ascorbic acid, is a relatively simple, oxygen rich molecule containing multiple polar functional groups. Several hydroxyl groups and ester fragments in the molecule can form multiple hydrogen bonds with water making it water soluble (Bylikin, Horner, Murphy, & Tarcy 2014, p. 593). Vitamin C is involved in certain metabolic processes in animals. In some animals, vitamin C can be synthesized, but in humans it is necessary in their diet. The consumption of vitamin C prevents scurvy, a disease characterised by soreness and stiffness of the joints and lower extremities rigidity, swollen and bloody gums, and hemorrhages in the tissues of the body (T. Editors of Encyclopaedia, 2018).

Figure 1: 2D molecular structure of vitamin C (Brown & Ford, 2014, p.726)

Relatively large amounts of vitamin C are required in human bodies for nutrition. For example in an adult male it is said that 70mg of vitamin C is required in their diet per day. Effective sources of dietary vitamin C include citrus fruits and fresh vegetables. However, vitamin C is easily destroyed in reactions with oxygen, especially in neutral or alkaline solutions or at elevated temperatures and therefore is difficult to preserve in foods.

Ascorbic acid is a powerful antioxidant and reducing agent capable of donating one or two electrons in biochemical redox reactions.

The concentration of vitamin C in a solution can be determined by redox titration using DCPIP (2,6-Dichlorophenolindophenol) as an indicator. In the presence of ascorbic acid the pink solution of the protonated indicator becomes colourless as DCPIP is reduced. As depicted by this chemical equation: Figure 2: Molecular depiction of ascorbic acid in a biochemical redox reaction. (Bylikin, Horner, Murphy, & Tarcy 2014, p. 593)

C12H8NCl2O2++ C6H8O6 → C12H9NCl2O2 + C6H6O6 + H+

During the titration ascorbic acid reacts with the titrant (oxidizing reagent) and the solution remains colourless. When the titration is complete all the ascorbic acid is oxidised to dehydroascorbic acid and the pink colour of the protonated DCPIP reappears. The chemical equation is displayed below:

C12H9NCl2O2 → C12H8NCl2O2+ + H+ + 2e-

(Bylikin, Horner, Murphy, & Tarcy 2014, p. 594) Through various studies, it has been determined that many fruits contain high concentrations of vitamin C in organelles such as the cytosol, cell wall and chloroplasts. “It reaches a concentration of over 20 mM in chloroplasts and occurs in all cell compartments, including the cell wall.” (Smirnoff & Wheeler, 2000). Therefore it can be concluded that due to the differences in fruit structure, those with similar structure will have similar differences in vitamin C mass. However, as the majority of the fruits in this experiment are structurally unique there will be a significant difference in vitamin C mass.

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Candidate Code: jmw082 Hypothesis H1: There is a statistically significant difference in the vitamin C mass between different fruits.

H0: There is no statistically significant difference in the vitamin C mass between different fruits.

Variables Table 1: Variable Table Variable

Information

Method to Control / Measure (for dependent variable)

Independent

Type of vegetable vitamin C is determined from (Orange, Blueberries, Red Capsicum, Green and Yellow Kiwi)

For the independent variable, different kinds of fruit juices will be used for each subsequent categoric test.

Dependent

Mass of Vitamin C derived from fruit.

To measure the mass, the DCPIP redox titration will be carried out through the steps in the method before using stoichiometry to determine the vitamin C mass.

Controlled

Mass of fruit used to make solution (g) (±0.01)

Using an electronic balance (±0.01) the mass of 50.00g of fruit. This will ensure the same mass of fruit is being tested in the independent variable solution.

Volume of H₃PO₄ used to create fruit solution (cm3) (±0.5)

250cm3 of H₃PO₄ is added to the solution and is measured with a 250cm3 measuring cylinder (±0.5). This will ensure the solutions are diluted equally.

Volume of distilled water to wash fruit solution through muslin filter (cm3) (±0.5)

300cm3 of distilled water is used to wash the fruit solution and is measured with a 250cm3 measuring cylinder (±0.5).

Volume of fruit solution that is titrated (cm3) (±0.04)

This is measured with a 25cm3 glass pipette (±0.04) before being titrated against DCPIP. Will ensure the same masses of fruit are titrated to find vitamin C mass.

Volume of distilled water added to fruit solution (cm3)(±0.5)

50cm3 of distilled water is used to wash the sides of the conical flask and rinse the tip of the burette. This is measured using a 50cm3 measuring cylinder (±0.5) before being added to the solution periodically.

Molarity of phosphoric acid added to fruit solution (2M)

This will be ensured and kept constant by using a consistent molar acid for all the tests. So solutions are equally concentrated and DCPIP readings are fair.

Consistent equipment use.

Throughout the experiment the equipment e.g. glassware will be used to ensure consistency throughout results.

Equipment Table 2: Table of Equipment Quantity

Equipment

Uncertainty

Quantity

Equipment

1

Lab Coat

1

Chopping Board

1

Safety Goggles

1

Knife

1

Electronic Balance

25

Watch Glasses

100cm3

±0.01

1500cm3

1% ascorbic acid solution

1

5cm3 glass pipette

1

Food Processor

±0.05

1 1500cm3

Uncertainty

2M H₃PO₄ Strainer Distilled Water

250g

Orange

±0.01

2

300cm3 Measuring Cylinder

±0.5

250g

Blueberries

±0.01

5

25cm3 glass pipette

±0.04

250g

Red Capsicum

±0.01

1

50cm3 burette

±0.05

250g

Green Kiwi

±0.01

1

Burette clamp

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Candidate Code: jmw082 250g

Yellow Kiwi

5

Funnel

100cm3

DCPIP

±0.01

1 25

Retort Stand 100cm3 conical flasks

Method 1.

2.

3.

4.

5.

6.

Scientific Diagram

16.

Put on safety equipment.

Prepare materials and reagents.

Place a burette on the retort stand using a burette clamp.

Place a filter funnel at the top of the burette.

Pour 50cm3 (±0.5) of DCPIP into the burette.

Using a 5cm3 glass pipette (±0.05) measure 5cm3 of ascorbic acid into a conical flask.

Titrate the ascorbic acid against the DCPIP indicator solution until a pink endpoint is reached.

Record the volume of DCPIP used in the titration in a results table.

Repeat steps 3 to 5, five times to standardise the DCPIP. Place a watch glass on the electronic scale and tare the weight.

Using an electronic scale accurately weigh 50.00g (±0.01) of yellow kiwifruit.

Place 50.00g of yellow kiwifruit in the food processor.

Using a 300cm3 measuring cylinder, measure 300cm3 (±0.5) of 2M H₃PO₄.

Ensure the lid of the food processor is securely attached and combine acid and kiwifruit.

Using a 300cm3 measuring cylinder, measure 300cm3 (±0.5) of distilled water.

Remove the lid of the food processor.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

Pour the solution of acid and kiwifruit through a sieve using 300cm3 of distilled water to wash the solution through.

Place the burette on the retort stand using the burette clamp.

Place a filter funnel at the top of the burette.

Pour 50cm3 (±0.5) of DCPIP into the burette.

Using a 25cm3 glass pipette (±0.04) measure 25cm3 of the acid and kiwifruit solution into a conical flask.

Titrate this solution against the DCPIP indicator solution until a pink endpoint is reached.

Record the volume of DCPIP used in the titration in a results table.

Repeat steps 10 to 23, four more times.

Repeat steps 10 to 23 with green kiwifruit, yellow capsicum, red capsium and oranges.

Clean up and remove safety equipment.

7.

8.

9.

10.

11.

12.

13.

14.

15.

Figure 3: Diagram of Yellow Kiwifruit Vitamin C Determination Set Up

Risk Assessment Table 3: Risk Assessments Risk

Hazard

Precaution

Broken Glass

●Cuts from damaged or broken glass to skin.

●Wear safety equipment to reduce the chance of cuts such as safety goggles and lab coats. ●Sweep up broken glass with a dustpan and broom.

Use of 2M H₃PO₄

● May cause harm to the eyes or in a cut to the skin. ● Causes severe burns and eye damage. ● Unsafe disposal leading to environmental damage.

● Wear such as safety goggles and lab coats to protect the skin. ● If H₃PO₄ comes into contact with eyes or cuts, flush with water immediately for at least 15 minutes and remove contaminated clothing. ● Wash hands and other exposed areas with mild soap and water before eating and drinking to ensure safety. ● H₃PO₄ does not pose a threat to the environment however, acid should be neutralized with calcium hydroxide or carbonate before disposal.

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Candidate Code: jmw082 Table 3: Continued Risk Assessments Risk

Hazard

Precaution

Spilling liquids

● Slipping and falling therefore causing injury to self or others. ● Tripping over uneven surfaces.

● Clean up all spillages immediately. If the floor is wet, use appropriate signs to tell people that extra care is needed or use another route until the spillage or wetness is gone. ● Warn of risks at any change in surface, for example dry to wet, even to uneven, traffic routes, by using signs, mats or markings.

Working with electrical appliances

● Shocks from equipment can cause severe and permanent injuries. ● Faulty or overloaded equipment can lead to fires which can cause damage, injuries and loss of life.

● Check all equipment i​s in good working order. ● If you find or suspect a fault, stop using the equipment, disconnect from the electrical supply and label 'do not use'. ● Avoid overloading sockets by providing enough socket-outlets ● Where possible switch off all appliances at the mains at the end of the working day ● Switch off and unplug equipment before you clean it or make adjustments ● Ensure an accessible and clearly identified switch is located near fixed machinery to cut off power in an emergency.

Use of DCPIP

● May be harmful by inhalation, ingestion, or skin absorption. May cause eye, skin, or respiratory system irritation. ● DCPIP may be hazardous to the environment and cause harm.

● Ensure that safety equipment (safety goggles and lab coats) are worn. ● If DCPIP comes into direct contact with the skin or eyes, flush with water immediately for at least 15 minutes and remove any contaminated clothing. ● Do not ingest DCPIP. ● Waft any scent to avoid respiratory system irritation. ● Ensure that all solutions with DCPIP are collected in an organic waste collection and then disposed of safely .

Raw Results Table 4: Results table for standardisation of DCPIP Standardisation of DCPIP was carried out to determine the final masses of Vitamin C within each fruit as it’s concentration was initially unknown. This concentration was needed in the calculations later for vitamin C mass.

Standardisation of DCPIP

Volume of DCPIP (mL) (±0.05)

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Initial Volume

50.00

49.20

48.30

47.60

46.90

Final Volume

49.20

48.30

47.60

46.90

46.20

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Candidate Code: jmw082

Table 5: Results table for DCPIP volume required to titrate fruit solutions.

Type of Fruit or Vegetable Orange

Blueberries

Red Capsicum

Green Kiwi

Yellow Kiwi

Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Trial 1 Trial 2 Trial 3 Trial 4 Trial 5

Volume of DCPIP (mL) (±0.05)

Initial 46.20 45.60 44.90 44.30 43.70 50.00 48.60 47.40 46.20 45.00 50.00 48.20 46.90 45.70 44.80 42.80 42.30 41.70 41.10 40.60 37.40 36.40 35.70 35.00 34.40 Volume Final 45.60 44.90 44.30 43.70 43.00 48.60 47.40 46.20 45.00 43.70 48.20 46.90 45.70 44.80 43.90 42.30 41.70 41.10 40.60 40.00 36.40 35.70 35.00 34.40 33.90 Volume

Qualitative Data Table 6: Visual and Written Qualitative Data Depiction.

Oranges Initial solution was translucent and slightly yellow. As DCPIP was added, the solution turned a brown / purple colour.

Blueberries Initial solution was slightly opaque and red. As DCPIP was added, the solution turned a much deeper red, purple colour.

Red Capsicum Initial solution was translucent and bright orange. As DCPIP was added, the solution became a deeper orange, red colour.

Green Kiwi Initial solution was translucent and slightly green, yellow. As DCPIP was added, the solution became a brown, purple colour.

Yellow Kiwi Initial solution was translucent and yellow. As DCPIP was added, the solution turned a brown, purple colour.

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Candidate Code: jmw082

Processed Data Table 7: Processed Data Table.

Type of Fruit or Vegetable Orange

Volume of DCPIP (mL)

Difference (±0.10) Average Difference (±0.10)

Blueberries

Red Capsicum

Green Kiwi

Yellow Kiwi

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

0.60

0.70

0.60

0.60

0.70

1.40

1.20

1.20

1.20

1.30

1.20

1.30

1.20

1.10

1.10

0.50

0.60

0.60

0.50

0.60

1.00

0.70

0.70

0.60

0.50

0.64

1.26

1.18

0.56

0.70

15.63

7.94

8.47

17.86

14.29

Standard Deviation Error

0.05

0.08

0.07

0.05

0.17

Standard Deviation Error Percentage (%)

7.81

6.35

5.93

8.93

24.29

Mass of Ascorbic Acid (mg)

0.34

0.66

0.62

0.29

0.37

Percentage Uncertainty (%)

16.12

8.35

7.08

18.39

15.44

Propagated Uncertainty

0.055

0.055

0.044

0.053

0.057

Percentage Uncertainty of Measurement (%)

From these calculations it is clear that the percentage uncertainty of measurement (%) is larger than the standard deviation error in all cases other than in yellow kiwi. However due to the further calculations taken when calculating the mass of ascorbic acid (see appendix) in each fruit the propagated uncertainty was taken. These error bars are represented in graphs 1 and 2.

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Candidate Code: jmw082

Graph 1: Vitamin C Mass in Each Fruit with Propagated Uncertainty Error Bars.

Graph 2: Comparative Graph of Vitamin C Masses between Experimental and Literature Values.

Table 8: Percentage Error between Literature and Experimental Values of Vitamin C. Literature Value (Australian Food Composition Database, 2019) (mg)

Experimental Value (mg)

Percentage Error (%)

Orange

1

0.34

66.00

Blueberries

0

0.66

Red Capsicum

3

0.62

79.33

Green Kiwi

2

0.29

85.50

Yellow Kiwi

2

0.37

81.50

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Candidate Code: jmw082 Statistical Analysis Table 9: One-Way ANOVA Results ANOVA Results Details Source

SS

df

MS

Between-treatments

2.1304

4

0.5326

Within-treatments

0.224

20

0.0112

Total

2.3544

24

F = 47.55357

P-value is < .00001

Table 10: Student t-Test Results Orange : Blueberries

Orange : Red Capsicum

Blueberries : Green Kiwi

Blueberries : Yellow Kiwi

Red Capsicum : Green Kiwi

Red Capsicum : Yellow Kiwi

t-value

-13.21844

-12.07477

14.92405

6.03864

13.86362

5.23723

P-value

< 0.00001

< 0.00001

< 0.00001

< 0.000155

< 0.00001

< 0.000393

Significance

Significant

Significant

Significant

Significant

Significant

Significant

Analysis Overall there is no linear trend in the data as the experiment was categorical variables. However there were two distinct groups of high and low DCPIP volumes used to titrate the various fruits. The fruits grouped in high vitamin C content are; red capsicum and blueberries. With overlapping error bars, it could be concluded that these fruits have relatively similar vitamin C content that was only perpetuated by the various errors in the experiment. Moreover, the highest recorded mean value out of these fruits was blueberries with a mean DCPIP volume of 1.26mL and a vitamin C mass of 0.66 mg. In comparison, fruits with lower vitamin C content indicated by the volume of DCPIP required to titrate it, included green kiwifruit, yellow kiwifruit and oranges. These three fruits also contained an overlap of error bars on all occasions highlighting that the results collected by the experiment may be closer in vitamin C content than indicated.

Throughout this lower vitamin C content group the lowest recorded mean volume of DCPIP was 0.65mL with a vitamin C mass of 0.29mg, from green kiwifruit. The highest of this group was yellow kiwifruit with a mean DCPIP volume recorded of 0.70mL and a vitamin C mass of 0.37mg.

As indicated by the mean volume of DCPIP data and calculated vitamin C mass, blueberries have the highest vitamin c content and green kiwifruit have the lowest. Green kiwifruit’s volume of DCPIP required to titrate the solution was 48% less than that of blueberries which, through statistical analysis, is statistically significant. Through the statistical test of a One-way ANOVA, it was indicated that the overall experiment created a significant result. A One-way ANOVA compares the means of two or more independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different. Both the One-Way ANOVA and the Independent Samples t-Test can compare the means for two groups. However, only the One-Way ANOVA can compare the means across three or more groups. Based on the One-way ANOVA it was determined that there is a significant difference in the vitamin C mass of different fruits (see table 9). The One-way ANOVA indicates that there is a very significant difference between the vitamin C mass of different fruits as the p-value calculated was < 0.00001. In biology the significance level is when the p-value is <0.05. Therefore through the calculation, the result can be considered significant. Hence, this experiment can accept the alternative hypothesis of “there is a significant difference between the vitamin c masses of different fruits”.

Rejecting the null hypothesis that there is no significant difference between the vitamin C masses of different fruits.

After the calculation of the One-way ANOVA, a post hoc Tukey HSD test was performed to determine the internal significance between groups of variables. This concluded that 6 out of 10 of the tests could be considered significant. Including Orange: Blueberries, Orange: Red Capsicum, Blueberries: Green Kiwi, Blueberries: Yellow Kiwi, Red Capsicum: Green Kiwi and Red Capsicum: Yellow Kiwi. However, this test also calculated that Orange, Green Kiwi and Yellow Kiwi are variables that do not have a significant difference between each other. Moreover, blueberries and red capsicum were also categorical variables that did not have a significant difference in the volume of ascorbic acid between the pair. This statistical difference was also determined through the post hoc Tukey HSD. Therefore, for all significant

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Candidate Code: jmw082 tests calculated by the post hoc Tukey HSD a t-test was carried out. This determined the p-value for each of these tests and indicated the significance of these differences. These are represented in table 10. The t-test tells you how significant the differences between groups are; in other words it lets you know if those differences (measured in means) could have happened by chance. The t score is a ratio between the difference between two groups and the difference within the groups. The larger the t score, the more difference there is between groups. The smaller the t-score, the more similarity there is between groups. The t-test formula is depicted in figure 5 and the results in table 10.

Figure 5: t-test formula

The largest difference in the mean values of DCPIP required to titrate ascorbic acid exists between blueberries and green kiwifruit. With a difference of 0.70mL it is evident that there is a very large difference between green kiwifruit and blueberries' vitamin C masses. This is further emphasised through the calculation of a student t-test depicted in table 9. Through this calculation it was determined that the result was very significant with a p-value of <0.00001. Therefore highlighting the significant difference between vitamin C masses in fruits.

A standard deviation (or σ) is a measure of how dispersed repeated data is in relation to the mean. For this experimental data, standard deviation was below 0.10 for all categorical variables except for yellow kiwifruit This is due to the first trial of yellow kiwifruit being an outlier. This may be due to overshooting the volume of DCPIP needed to titrate the volume of ascorbic acid in the yellow kiwifruit. As it was the initial trial, it was difficult to see when the end point was reached However, percentage uncertainty of measurement was larger than percentage uncertainty based on standard deviation. As represented in the processed data table (table 8).

Conclusion Due to the unique structural aspects of each individual fruit, it was hypothesised that there would be a significant difference in vitamin C masses across the five fruits. Although correct, the hypoehtitses did not take into account the fact that there would also be insinginfanct results. For example, due to the very similar structure between yellow kiwifruit and green kiwifruit, the p-value was considered insignificant. This was calculated by the post hoc Tukey HSD statistical analysis. However, the structural differences between the fruit with the highest vitamin C content and the lowest vitamin C content was plausible. The blueberries and the green kiwifruit had very different structures and therefore supported the alternative hypothesis. Moreover, due to the high vitamin C concentrations in the cell wall of the fruit, the result of blueberries as the largest vitamin C value may have been due to it being the only fruit with the skin included in the solution which is generally higher in vitamin C. This is due to the close relationship between “ the regulation of ascorbate synthesis and the cell wall” (Fenech, Amaya, Valpuesta, & Botella, 2019) hence cell walls in plants typically concentrated on the exterior of fruits are high in vitamin C. Overall, through the various unique structures of the fruits, the alternative hypothesis was supported. Moreover, often vitamin C is found in higher concentrations within the natural, lipid aggregated vesicles called liposomes which are also present in the lining and phospholipid bilayers of various fruits.

As the liposomes contain concentrations of vitamin C mass, when they are in these fruit species cell walls they can be released and hence when the skin of the fruit was integrated into the solution, higher masses of vitamin C were present.

Evaluation Data collected through the experiment showed two separate groupings of fruit that required high volumes of DCPIP and those that required low volumes of DCPIP to be titrated. The fruits that required high volumes of DCPIP were red capsicum and blueberries. Based on the results collected it is clear that blueberries supposedly had the largest mass of vitamin C. The fruits that required lesser volumes of DCPIP to be titrated were oranges, green kiwi and yellow kiwi.

Further, the fruit that required the least volume of DCPIP was the green kiwifruit. These groups were determined by an overlap in error bars.

Throughout the experiment it was outlined that there was very high percentage uncertainty as outlined in table 6. Due to the % uncertainty being above the biology limit of < 5% it can be concluded that the experiment had low precision. To improve this in the future, using equipment with smaller graduations would have been beneficial. Due to the small values of DCPIP and solutions that were being titrated, more precise equipment could have had lower percentage uncertainty overall and therefore improve precision. For example, by having a burette with smaller graduations, the

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Candidate Code: jmw082 percentage uncertainty of the DCPIP that was expelled would have been lowered, hence, lowering the total percentage uncertainty.

The results collected were relatively reliable. With 80% of standard deviation calculations below 0.01, the spread over results over 5 trials was fairly consistent highlighting a reliable result. The largest standard deviation calculation was for the yellow kiwifruit with a value of 0.17. This value was derived due to the initial trial being overshot due to a lack of colour change and consistent mixing within this trial. Hence, the trial was severely overshot, leading to a larger standard deviation value. The smallest standard deviation value was equal between green kiwifruit and oranges, at 0.05. 0.17 is 240% larger than that of 0.05, therefore showing that the tests for green kiwifruit and oranges were much more reliable as they were overall closer to the mean. Overall, through the consistent results collected throughout the trials of the experiment, it can be considered relatively reliable.

Through this experiment it was determined that the accuracy of this experiment was very low. Through graph 3, it is clear that the literature values derived from the Australian government food data standards were much higher than experimental tests in all cases except blueberries and therefore impacted with the accuracy of results with the largest percentage error of 85.50%. Table 8, depicts the percentage error of the experiment depicting the inaccuracy. If this experiment was completed again, to improve the accuracy, the integration of a pH meter to determine when the end point was reached could be employed to ensure more accurate results. Low accuracy may have been due to poor end point determination through visual, qualitative interpretation. Due to discrepancies in colour when the end point was reached for different fruits, it was difficult to determine when the titration had finished and therefore all the results were undershot. A key example of this is with the blueberry sample. Due to the already red, purple nature of the mixture, it was very difficult to determine when the DCPIP had fully neutralized the ascorbic acid and therefore the result was undershot.

Overall, through graph 2, it is clear that the results of the experiment were not particularly accurate due to the large percentage errors, however, the majority of the fruits follow the same trend as their literature counterparts, with the exception of blueberries.

Table 11: Table of Errors Table of Errors Source of Error

Impact of Error

Type

Method to Improve Error

Completing the experiment on different days.

By completing the experiment over several days there was potential for the results to different depending on the environmental interactions.

Variations in the mixing of the reactants.

As mixing reactants can increase kinetic energy if the DCPIP and fruit solution weren’t mixed the same amount the titration could indicate a fake end point and therefore give an incorrect result of vitamin C mass.

End point determination.

As various fruits depicted different colours it was difficult to determine an endpoint. This may have impacted the results positively or negatively as the end point read may have been incorrect and therefore the measurement of DCPIP would be incorrect. Hence leading to incorrect vitamin C mass values.

To improve this, a pilot test of each fruit used could have been undertaken to identify when the end point was reached. Therefore, Systematic the end point determination in the final experiment would have been more accurate, hence, collected better results.

Difficult to determine when to stop running the DCPIP.

Due to the differences and discrepancies in colours between the fruit juices it was extremely difficult to determine the end point when adding the DCPIP.

The addition of a pH meter to determine the end point of the solution would be more accurate. The pH meter would identify when Systematic the end point is determined through quantifiable results rather than by qualitative interpretation. Therefore through the

Systematic

Random

In order to control this variable ensure the experiment is carried out on the same day.

In order to improve this error ensure that all trials are mixed the same amount to ensure consistency and therefore the same reaction of the reactants.

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addition of a pH meter, it would be easier to determine the end point of the titration and therefore making the experiment more accurate.

Lack of repeats.

The experiment only carried 5 repeats of each titration. Which was large enough to analyse but potentially too small for a general trend within a fruit species.

Implying that 1g of fruit correlated directly to 1cm3 of fruit juice.

This error inferred that in the solution of H3PO4 and fruit that 50g of fruit correlated directly to 50cm3. Therefore, when considering the 25cm3 sample titrated in each trial, only 1.92g of fruit was being titrated due to calculations through ratios. Hence, when trying to calculate the mass of ascorbic acid in the fruit this may have been incorrect as 1g of the fruit may not produce more or less than 1cm3 of fruit juice. Therefore, the masses of ascorbic acid calculated may have been higher or lower if this was more closely monitored.

Implying that 1.92g of fruit was being titrated in every 25cm3 sample.

This error implied that in every 25cm3 sample that was titrated, 1.92g of fruit was being titrated. This error may have interfered with the final vitamin C values either increasing or decreasing this value as more or less fruit may have been present in the sample. 1.92g was only chosen as the value as in a ratio between the chemicals and fruit in the solution 1.92g was the calculated mass.

Literature Values were only to the nearest whole number.

This error impacted the processed and analysed data for accuracy. As the literature values were only to the closest whole number, it was difficult to gage how close the experimental results were to the literature value. Especially for the blueberry value which gave a result of 0mg, making it difficult to gage a percentage increase or decrease between literature and experimental value.

To improve this more repetitions of the experiment could provide more representative results. Perhaps, further Systematic repetitions would provide a better calculated value for the mass of vitamin C in the sample that was titrated in comparison to the much lower results that were collected.

To improve this error, ensure that when blending the fruit in the food processor that it is weighed directly after and the mass is Systematic recorded so that it can be calculated what ratio of fruit juice there is in the solution being titrated.

Random

To improve this error, make smaller samples of fruit solution to titrate so that it is easier to recognise and calculate how much fruit is present in each trial. Hence, making calculations for vitamin C content easier.

Random

To improve this error, find literature values that are not rounded to the closest whole number.

Possible Extensions For this experiment there are various possible extensions. Firstly, by testing more species of fruit, the experimental results could depict a greater spread of results and therefore more successfully depict whether there is a statistical difference in vitamin C content between species of fruit. Further, through the integration of a pH meter, the results could be more accurate as the end point is not determined by visual, qualitative data. Moreover, potentially comparing the vitamin C content between fruits and vegetables may have been interesting to determine which food group is more effective in providing vitamin C.

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Candidate Code: jmw082 Furthermore, other experiments could test how vitamin C mass in a single species of fruit differs as it is exposed to heat. By applying various temperatures of heat to these fruit juices it could be determined at what temperature it is optimum to eat or drink fruit to ingest the most vitamin C in an individual's diet. Hence, another extension of this experiment is using varying temperatures of heat applied to a fruit and determining how this impacts vitamin c mass overall.

Reference List Britannica, T. Editors of Encyclopaedia (2018). Vitamin C. Encyclopedia Britannica. https://www.britannica.com/science/vitamin-C. Brown, C,. & Ford, M. (2014). Pearson Baccalaureate Chemistry Higher Level 2nd edition. Harlow: Pearson Education Limited.

Bylikin, S., Horner, G., Murphy, B., & Tarcy, D. (2014). IB Chemistry Course Book: Oxford IB Diploma Programme. Oxford: Oxford University Press Cayman Chemical. (2019). DCPIP Assay Reagent. Retrieved March 14th 2021, from https://www.caymanchem.com/msdss/700942m.pdf Fenech, M., Amaya, I., Valpuesta, V., & Botella, M. A. (2019). Vitamin C Content in Fruits: Biosynthesis and Regulation. Frontiers in plant science, 9, 2006. https://doi.org/10.3389/fpls.2018.02006 Food Details. (2019). Retrieved April 21st 2021, from https://www.foodstandards.gov.au/science/monitoringnutrients/afcd/Pages/fooddetails.aspx?PFKID=F006277 Food Details. (2019). Retrieved April 21st 2021, from https://www.foodstandards.gov.au/science/monitoringnutrients/afcd/Pages/fooddetails.aspx?PFKID=F004815 Food Details. (2019). Retrieved April 21st 2021, from https://www.foodstandards.gov.au/science/monitoringnutrients/afcd/Pages/fooddetails.aspx?PFKID=F006277 Food Details. (2019). Retrieved April 21st 2021, from https://www.foodstandards.gov.au/science/monitoringnutrients/afcd/Pages/fooddetails.aspx?PFKID=F002246 Food Details. (2019). Retrieved April 21st 2021, from https://www.foodstandards.gov.au/science/monitoringnutrients/afcd/Pages/fooddetails.aspx?PFKID=F001290 Nicholas Smirnoff & Glen L. Wheeler (2000) Ascorbic Acid in Plants: Biosynthesis and Function, Critical Reviews in Biochemistry and Molecular Biology. Retrieved March 14th 2021, from https://www.tandfonline.com/doi/abs/10.1080/10409230008984166

Appendix

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Biology SL Internal Assessment Investigating the Effect of Leaf Foliage Density on Radiant Heat RESEARCH QUESTION What is the effect of increased leaf foliage density (0, 1 and 2 layers) on radiant heat below the leaf canopy, measured by black globe temperature increase over 10 minutes (℃ ±0.1)?

INTRODUCTION After learning about the cooling capabilities of shade trees, I became interested in an investigation into how trees can be used effectively in urban areas to provide cooling, which is an increasingly relevant area of scientific research, considering the continuous development and expansion of cities across the globe and rising temperatures due to climate change. The use of trees in urban areas is especially relevant because not only are they able to provide shade and cooling outdoors, such as in parks or urban forests, but they can also block solar radiation from penetrating the windows and heating up the walls of large, urban buildings. A specific example of the importance of urban cooling is the increasing likelihood of heatwaves and record-breaking hot weather, as well as generally higher temperatures as a result of global warming. I think it is especially interesting to consider the use of trees to provide cooling in cities that not only experience warm weather, but are also less economically developed, where luxuries such as air conditioning are not as common. Conversely, the prevalence of air conditioning in more developed urban areas, whilst cooling the interiors of buildings, has the opposite effect, heating the air outside, so the strategic planting of trees to potentially mitigate this is also a relevant area of research. Another interesting facet of this investigation is the existence of the “urban heat island” (Ennos, 2015). During the day, dark surfaces such as brick and asphalt, which are extensively present in urban environments, efficiently absorb the sun’s radiant heat energy. Unlike the leaves of trees that have natural cooling mechanisms, these surfaces release this energy as heat at night, meaning urban areas can be up to 7 degrees Celsius warmer than the surrounding countryside (Ennos, 2015).

Thus, in light of these potential practical applications, this investigation seeks to determine the cooling capacities of trees’ shade canopy.

BACKGROUND RESEARCH Leaves have various radiative properties which characterise their radiant heat energy exchange with the surrounding environment (Arkebauer, 2005). Leaf temperature is closely associated with leaf radiant energy exchange, as there must be a balance between energy gained through net radiation and energy lost through various processes (Arkebauer, 2005).

The primary source of incoming radiation on a leaf is solar radiant heat. This is then either reflected, absorbed or transmitted (Arkebauer, 2005). Energy absorbed by the leaf itself causes a temperature gain and an associated reradiation from the leaf surfaces. For this investigation, the solar radiation transmitted through leaves and the heat reradiated from leaves in the direction of the ground is the heat source of interest, as this defines the perceptible radiant heat load under the canopy.

The energy transferred between a leaf and its surroundings can be described by the following equation, for net radiation transfer from incoming radiation and reradiation (Arkebauer, 2005):

Where Rn = Net Radiation, Rn’ = Incoming Radiation, εleaf = leaf absorptivity, σ = Stefan-Boltzmann constant, and T leaf = leaf temperature.

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Candidate code: hzy524 This shows how the reradiation term depends strongly on leaf temperature (varying with T ).

4

Considering the energy budget of the leaf, the net radiation can be simplified from Rn ≈ H + λE + S + P, where S is the energy stored by the leaf, and P is the energy used for photosynthesis, to: Rn ≈ H + λE, where H is the sensible heat flux (conductive and convective heat transfer) and λE refers to the latent heat flux (heat lost from transpiration), and terms S and P are neglected as small (Arkebauer, 2005).

As found by Lin, Chen, Zhang, Fu, & Fan (2017), transpiration is the predominant mechanism of leaf cooling.

In consideration of the above relationships, the heat radiation below the leaf canopy is highly dependent on leaf temperature, which is in turn largely dependent on cooling from transpiration.

Transpiration is the process by which water in leaf tissues is lost to the atmosphere through the stomata (Bureau of Meteorology, 2021), pores in the epidermis of the leaf (Allott & Mindorff, 2014, p.404). Transpiration is necessary for the process of photosynthesis, through which carbohydrates are synthesised using light energy, because it requires carbon dioxide to produce glucose (Allott & Mindorff, 2014, p.404). To sustain photosynthesis, carbon dioxide must be exchanged with oxygen, which is produced as a waste product (Allott & Mindorff, 2014, p.404).

Carbon dioxide is absorbed through the stomata, and in order for this to occur, water vapour must be released from the leaf and stem in transpiration (Allott & Mindorff, 2014, p.404). Plants also have roots into the soil to draw water and nutrients into the stems and leaves (USGS, n.d.). Because of water’s cohesive property, due to the hydrogen bonds between water molecules, and its adhesive property, where water molecules adhere to polar and hydrophilic surfaces, water can be transported from the roots of trees to the leaves through xylem vessels (Allott, 2014, p.19).

Transpiration involves evaporation, which is an endothermic cooling process. Energy is absorbed when water evaporates and is converted from a liquid to a vapour (Sterling, 2004). This energy is transferred from the leaf to the water molecules, which become highly energetic vapour molecules and are released into the atmosphere, and therefore cooling the plant (Stirling, 2004). Up to 99% of the water in leaves is lost in the process of gas exchange (Babu, 2020).

Another, more general aspect of cooling from shade trees is that trees block solar radiation as well as increase air moisture (as detailed above through transpiration). During the day, areas that are exposed to sunlight absorb heat energy, which is later released during the night, warming the ambient temperature of the area. Shaded areas, such as those covered by tree canopy, experience reduced heat radiation during the day and hence do not release the absorbed heat at night, and therefore have a cooler average ambient temperature (AskNature, 2017).

HYPOTHESIS H1: There is a significant difference in the radiant heat provided by 0, 1 and 2 layers of leaf foliage.

H0: There is no significant difference in the radiant heat provided by 0, 1 and 2 layers of leaf foliage.

VARIABLES Independent variable: Leaf foliage density (0, 1 and 2 layers) Dependent variable: Increase in radiant heat below the leaf canopy (temperature/time ℃ ±0.1) Controlled variables: 128

Candidate code: hzy524 Controlled variable

Method to control the variable

Light source

The same halogen lamp was used for each trial.

Position of light source

The light source was fixed in place above the ground by being attached to the tripod stand. The tripod was adjusted so that the light was always 360mm above the floor.

Temperature collection method

The same black globe radiant heat thermometer was used to record temperature during each trial.

Ambient temperature

An ambient temperature of 25℃ was maintained for each trial.

Location of thermometer in each trial

The thermometer was located on the same position on the cardboard flooring, which was drawn on with a permanent marker.

Source of leaves

All leaves were sourced from the same species of tree.

Cardboard flooring

The same piece of cardboard flooring was used for each trial.

Orientation of black bulb thermometer

The black globe radiant heat thermometer used for measurement of temperature was marked to indicate which side of the bulb should rest on the floor, so that it was always oriented the same way when placed underneath the foliage layer.

Measurement intervals

For each trial, the temperature below the foliage layer was measured after ten minutes.

Leaf layer percentage coverage

A colour analysis app was used to analyse the percentage coverage of each leaf layer to minimise differences in coverage as each new layer was constructed.

EQUIPMENT Equipment

Justification

1x Tripod

A tripod was used to position the halogen lamp above the leaf layers.

1x Halogen flood lamp (240V, 500W)

Figure 1: Blackbody radiation at various temperatures (Fritzsche, 2016)

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Figure 2: Leaf reflectance and transmittance (Arkebauer, 2005) The halogen lamp is used to simulate solar radiation with a high intensity artificial light/heat source. Due to the different temperature of the halogen element to the sun, a different frequency spectrum is produced (see Figure 1 above) that has a greater percentage of energy at long Infrared wavelengths.

As leaf energy absorption varies with wavelength (see Figure 2 above), this source does not fully replicate natural light/heat conditions, but is the best available alternative.

Energy density of sunlight at earth’s surface: 1000W/m2 Therefore, in order to make the 500W light’s intensity roughly equal to sunlight, the light must be at a distance from the “ground” so that the light spreads over an area of 0.5m2. After conducting measurements, it was calculated that to get an area of approximately 0.5m2, the light should be 360mm (±1mm) from the floor.

1x metal rack (approximately 200mm x 270mm in size) with a height of approximately 60mm 1x metal rack (approximately 200mm x 270mm in size) with a height of approximately 120mm

The experimental design, with the metal rack acting as the base for the foliage layers, was chosen after preliminary testing. Using the racks was the most effective way to simulate multiple layers of foliage, whilst still leaving space below for the thermometer. This design was reached after initially attempting to create a tree-like structure, by placing leaves into a cardboard “trunk”, which was unsuccessful.

60x Leaves

60 is approximately the correct number of leaves needed to create two leaf layers on racks with the dimensions listed above.

1x Black globe radiant heat thermometer ±0.1℃

A black globe thermometer is a thermometer inside a black sphere which absorbs radiant heat. The temperature reading obtained equates to the radiant heat being applied.

30cm of wire

This was used to secure the lamp in place on the tripod.

1x A3 sheet of cardboard or paper

This was used as a consistent flooring upon which the leaf foliage would be created.

1x Permanent marker

A permanent marker was used to make markings on the cardboard flooring to ensure the position of the racks were kept consistent.

1x Phone Camera

A phone camera was used to photograph each layer of leaves, to be put onto a colour analysis app for the percentage coverage to be calculated.

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Candidate code: hzy524 1x Colour Analysis App

This app was used to ensure that approximately the same percentage of leaf cover was achieved each time the leaf layers were created without overlapping the leaves.

1x Tape Measure (±1mm)

A tape measure was used to measure the distance of the halogen lamp from the ground.

RISK ASSESSMENT Risk

Hazard

Precaution

Burns to skin

Intense heat of light source

Light was handled with caution, attached handles were used and hands were kept away from the direct heat of the light. A prominent safety warning sign was also installed on the light.

Ethical risk

Unethical sourcing of leaves

Leaves were sourced purposely from a non-endangered and non-native plant with lots of foliage.

Environmental risk

Incorrect disposal of leaves

All leaves used in the experiment were disposed of after use into a garden organics bin.

METHOD 1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

Gather equipment.

Remove around 20 leaves from the chosen plant and bring back to the lab.

Place an A3 sheet of paper/cardboard onto the floor of the lab.

Trace using a permanent marker the shape of the metal rack onto the cardboard piece, aligning the middle of the rack’s position with the position of the lamp.

Trace also the position of the black globe thermometer to be placed below the rack.

Set-up the tripod over the cardboard sheet and carefully attach the light source (halogen lamp) after measuring height with a tape measure, using wire to secure it in place 360mm above the paper flooring.

Place black globe thermometer on its designated position on the floor.

Turn on the lamp and start the stopwatch simultaneously.

Measure and record the radiant temperature after every minute for 10 minutes when the thermometer is exposed to direct light and there is zero foliage.

Repeat steps 8 and 9 four times after the thermometer has returned to room temperature (around 25℃).

Place a layer of leaves over the shorter metal rack so that its surface is entirely covered.

Using a phone or camera, take a photo of the rack of leaves.

In the photo’s settings, turn the exposure to 100, the contrast to 80 and the saturation to -100.

Using a colour analysis application, upload the edited photo of the leaves, cropping the image until only the rack is visible.

Record the percentage of colours that correspond to the leaves in the photo.

Gently move the metal rack with one leaf layer to its designated position above the thermometer.

Once the thermometer has returned to its initial temperature (25℃), turn on the lamp and start the stopwatch simultaneously.

Measure and record the radiant temperature after every minute for 10 minutes.

Repeat steps 17 and 18 four more times after the thermometer has returned to initial temperature (25℃).

Repeat step 11, instead placing leaves over the taller rack.

Repeat steps 12 to 15 with the 2nd foliage layer.

Carefully position the taller rack with a 2nd foliage layer over the original rack (1st foliage layer).

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Candidate code: hzy524 23. Repeat steps 17 and 18 five times with the two foliage layers, each time after the thermometer has returned to its initial temperature (25℃).

Photograph of set-up:

RESULTS Raw data Layers of Leaves

Change from start temperature to final temperature (℃ ±0.1) Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

0

6.6

6.9

5.6

6.5

6.1

1

4.6

4.8

4.1

3.5

2.7

2

1.2

0.9

1.4

1.7

1.4

Processed data Layers of leaves

Average change in temperature (℃ ±0.1)

Standard deviation

% uncertainty of measurement

% uncertainty of repeats

0

6.3

0.450

1.59

7.14

1

3.9

0.766

2.56

19.64

2

1.3

0.264

7.69

20.31

Qualitative Data Because the experiment was conducted over different days, and the leaves had to be fresh each time, a colour analysis app was used to calculate the percentage cover of leaves for each layer that was created, attempting to reach a similar level of coverage each time to maintain consistency. Each photo was edited with high exposure and contrast and zero saturation to make it clearer for the colour app to distinguish between leaves and non-leave substances such as gaps between leaves. Exposure was set to 100, contrast was set to 80, and saturation was set to 132

Candidate code: hzy524 -100. The following edited photos show each leaf layer, a total of six, which was created for the different tests.

Percentage coverage values ranged from 73.92%-79.16%, with an average of 77.47% coverage.

Graph A column graph was used to display the data collected in this experiment, to reflect the categorical nature of the independent variable.

Statistical test A t-test for two independent means was conducted, to establish if the results are statistically significant, by determining the confidence level in the hypothesis. This t-test was used because this experiment involves the comparison of 3 separate, independent samples - 0, 1 and 2 leaf layers.

T-test equation:

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Candidate code: hzy524 T-test for 0 layers and 1 layer: Mean difference in temperature: 2.4℃ s2p = ((df1/(df1 + df2)) * s21) + ((df2/(df2 + df2)) * s22) = ((4/8) * 0.25) + ((4/8) * 0.73) = 0.49 s2M1 = s2p/N1 = 0.49/5 = 0.1 s2M2 = s2p/N2 = 0.49/5 = 0.1 t = (M1 - M2)/√(s2M1 + s2M2) = 2.4/√0.2 = 5.4 The t-value is 5.40453. The p-value is .000321. Therefore, the result is significant (p < .05).

T-test for 0 layers and 2 layers: Mean difference in temperature: 5.0℃ s2p = ((df1/(df1 + df2)) * s21) + ((df2/(df2 + df2)) * s22) = ((4/8) * 0.25) + ((4/8) * 0.09) = 0.17 s2M1 = s2p/N1 = 0.17/5 = 0.03 s2M2 = s2p/N2 = 0.17/5 = 0.03 t = (M1 - M2)/√(s2M1 + s2M2) = 5.02/√0.07 = 19.25 The t-value is 19.25082. The p-value is < .00001. Therefore, the result is significant (p < .05).

T-test for 1 and 2 layers: Mean difference in temperature: 2.6℃ s2p = ((df1/(df1 + df2)) * s21) + ((df2/(df2 + df2)) * s22) = ((4/8) * 0.73) + ((4/8) * 0.09) = 0.41 s2M1 = s2p/N1 = 0.41/5 = 0.08 s2M2 = s2p/N2 = 0.41/5 = 0.08 t = (M1 - M2)/√(s2M1 + s2M2) = 2.62/√0.16 = 6.47 The -value is 6.46963. The p-value is .000097. Therefore, the result is significant (p < .05).

The 3 t-tests each have a p value of less than 0.05, so there is a significant difference in the temperature change between each increased layer of leaves. Therefore, the null hypothesis is rejected in favour of the alternative hypothesis that there is a significant difference in the reduced radiant heat transmission provided by a leaf foliage shade, with a very high confidence level. Furthermore, this test reveals that not only is 1 leaf layer beneficial in reducing temperature, but so are 2 leaf layers, as the p value for the t-test between 1 and 2 layers is also significant, leading to the conclusion that not only does foliage density reduce radiant heat transmission, but increased foliage enhances this effect.

DISCUSSION Analysis: Description of results As the number of leaf layers increased, the radiant heat inversely decreased, as measured by the decreasing black globe temperature rise. For the control of 0 layers, the average black globe temperature rise was 6.3℃, but decreased to a 3.9℃ rise for 1 layer, and again to a 1.3℃ rise for 2 layers. The heat radiation was reduced by 2.4℃ from 0 to 1 layers, and by 2.6℃ from 1 to 2 layers, and a total reduction of 5℃ from 0 to 2 layers. Evaluating the change on a proportional basis, from 0 to 1 layers, there was a 38% reduction in temperature rise, from 1 to 2 layers, there was a 66% reduction and there was an overall reduction, from 0 to 2 layers, of 79%. This shows that the addition of 1 layer of leaves has a pronounced effect on radiant heat, but the effect of an added second layer is even greater. The total reduction in radiant heat achieved with 2 layers, of almost 80%, is considerable.

The graph’s vertical error bars show the standard deviation for each set of values, the largest raw error for the experiment. For 0 leaf layers, the standard deviation was 0.450. For 1 leaf layer, the standard deviation was higher at 0.766. 2 leaf layers had the smallest standard deviation of 0.264. The error bars do not overlap, which increases confidence in the results.

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Candidate code: hzy524 The percentage uncertainty of repeats was also calculated, by dividing the standard deviation for each variable by the data value and then multiplying it by 100. For 0 layers, there was 7.14% uncertainty, for 1 layer there was 19.64% uncertainty, and for 2 layers there was 20.31% uncertainty. These values are all greater than 5% and therefore the error involved in repeats can be considered large, especially for 1 and 2 leaf layers.

Explanation of results The results of this experiment show black globe temperature rise over 10 minutes for 0, 1 and 2 leaf layers, representing the change in radiant heat below the leaf canopy. The large reduction in temperature increase between each successive layer is indicative of the use of a shade canopy being very effective in reducing radiant heat load, and therefore cooling the space and surfaces underneath. Furthermore, it can be seen from the results that increased foliage density considerably enhances this effect, therefore indicating that a denser, multi-layered canopy is most effective.

The background research identified that the radiant heat is a combination of transmission and reradiation by the leaves of the incoming radiant energy less the sensible and latent heat losses, of which the dominant cooling mechanism is latent heat loss due to transpiration. In considering the effect of the second leaf layer, it is proposed that the incoming radiation to that layer is reduced by the measured reduction of one layer, 38%, but the transpiration heat loss is of a similar magnitude in each layer, evidenced by similar ΔT, therefore producing a greater proportional reduction in the second layer than the first layer.

The contribution of direct transmittance of radiation through the leaf layer is also a consideration. Whilst transmittance through individual leaves is assumed to be low (like most opaque materials), the average layer coverage of around 77% allows 23% transmittance of heat radiation. Assuming random gap distribution, the transmittance through 2 layers is reduced to 5% (0.23 x 0.23 = 0.053), so transmitted radiation is also greatly reduced as a heat source.

The process of transpiration contributes to the cooling effect, as water in leaf tissues is converted into a vapour and lost to the atmosphere through the stomata. This cools the area around the foliage because the loss of energy involved in transpiration is greater than the energy used in the conversion of water from a liquid to a vapour.

Preliminary testing indicated that adding a 3rd layer of leaves would not have a significantly different reduction in radiant heat transmission compared to 2 layers. Whilst this was not tested explicitly in this investigation, these results show that 2 leaf layers provide largely reduced radiant heat transmission (-79%) compared to 0 leaf layers, but there likely would not be a significant difference with 3 layers compared to 2, as 79% of the original heat radiation has already been prevented, and the small remaining heat radiation is likely to include other paths such as surrounding reflections, due to the experiment design.

Evaluation: The percentage uncertainty of measurement reveals the precision of the equipment used in the experiment. This uncertainty was below 5% for both 0 and 1 leaf layers, but increased to 7.69% for 2 leaf layers. Overall, there was a low error of measurement and so the data can be considered precise, although the 2 leaf layers were the least precise of the three.

The internal reliability of the experiment is given by the percentage uncertainty of repeats. For 0 layers, there is 7.14% uncertainty, for 1 layer there is 19.64% uncertainty, and for 2 layers there is 20.31% uncertainty. Each of these values are greater than 5%, and the uncertainty for 1 and 2 layers is particularly high. The experiment therefore has somewhat low reliability. The experimental data was also limited due to the fact that only 5 trials were conducted for each test, and so a bigger data pool with more trials would have increased the internal reliability.

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Candidate code: hzy524 Error

Detail

Effect on Experiment

Method to Minimise Impact

Systematic

Use of black globe thermometer with readings to 1 decimal place

Whilst there was no evidence of systematic error, it is possible that the thermometer used, providing temperatures to only 1 decimal place, could have increased error.

The use of a more precise digital temperature probe able to provide temperature readings with more decimal places would reduce error and likely yield better results.

Random

Intrinsic variability

Intrinsic variability was one random error, as the experiment was conducted over different days and in slightly different locations, which could have increased the variability of results. Variability was also present in the use of leaves, as new leaves had to be used for each separate day of testing, and natural variation was present amongst leaf sizes, shapes and surface area.

Whilst attempts were made to mitigate this variability by calculating the percentage coverage of every new layer and trying to ensure each layer had a similar percentage, much of this random error is difficult to control. That being said, it is useful to be aware of the potential impact on results of using organic materials such as leaves. This inherent variability could also be reduced by conducting further testing over a longer period of time.

Random

Variation in heat from the light source

Because the lamp was switched on and off for ten minute periods repeatedly, it is possible that there was some inherent variation in the heat provided.

Multiple trials were conducted to reduce this effect. A longer time between trials would give more consistent thermal conditions for each trial. Input conditions including actual live voltage could also be measured.

Random

Variation in height of The lamp had to be re-positioned the lamp used as a light for each new day of testing and source therefore could be positioned at a slightly different height from the setup each time

Whilst it was impractical for this experiment, ideally, the tests would all be conducted during the same time period to avoid errors in setting up. A more precise holding method and measurement tool could also be used for measuring the height of the lamp each time.

The overall accuracy of the data is relatively high though, as there is no overt systematic error and a generally high precision. The t-test for independent means showed that there was a significant difference in results for each of the leaf layer tests, further indicating high accuracy. The overall trends of the results, showing very clear reductions in temperature rise between 0 and 1 leaf layers, and 1 and 2 leaf layers, are also indications of accuracy, as these correspond strongly to what was predicted.

The experiment’s validity is generally quite high, as the results respond effectively to the experiment’s aim and both precision and accuracy are quite high. The statistical test also showed that the results are significant between both 0 and 1 layers, 1 and 2 layers, and 0 and 2 layers. Furthermore, the stated variables were controlled effectively, as 136

Candidate code: hzy524 detailed in the table outlining method to control variables, and no other confounding variables were identified, further contributing to the validity of the results.

There were some limitations to this experiment which could be improved with some extensions. Firstly, the scope of the experiment could be broadened to explore more thoroughly the general impact of shade structures on cooling. For example, comparative testing could be conducted using non-organic shade structures instead of leaves, such as layers of paper or cardboard, to determine more clearly if the cooling effect was the result of the particular use of leaves, or is limited just to shade structures in general. Comparisons could also be made to non-organic shade structures with evaporative cooling like leaves, to determine if this has any effect on cooling properties. Tests could also be conducted with a variety of different types of leaves (plant species) to explore the differences in results with leaves of different colours, shapes, sizes, thicknesses or textures. Another possible improvement would be to also measure the surface temperature of the leaves at each layer, to determine if there is a true correlation between leaf temperature in each layer and reradiation, as was indicated in the background research by the equation for net radiation transfer. Overall, the majority of these suggested extensions reflect the potential benefits of deepening the investigation to explore the wider cooling capabilities of shade structures, different types of leaves, and to better understand the relationship between leaf temperature and reradiation.

In terms of how this research could be improved with regards to real research, perhaps experiments could be conducted with actual trees in the natural environment, using real sunlight instead of just a model, as the lamp was limited as a simulation of sunlight. In terms of methodology, improvements could be made with the concept of increasing leaf layers, to better and less artificially represent the different amounts of foliage provided by trees.

Ultimately, this experiment does provide interesting insight into the impact of leaf foliage on reducing radiant heat below the canopy and is relevant for a consideration of the real-world benefits of incorporating shade trees in urban areas to increase cooling.

REFERENCES Allott, A. (2014). Biology - Study Guide. Oxford: Oxford University Press.

Allott, A., & Mindorff, D. (2014). Biology - Course Companion. Oxford: Oxford University Press.

Arkebauer, T. (2005). Leaf Radiative Properties and the Leaf Energy Budget. Agronomy & Horticulture -- Faculty Publications, 693, 93-103.

AskNature Team. (2017). Leaf color and shape enhance cooling effect. Retrieved January 31, 2021 from https://asknature.org/strategy/leaf-color-and-shape-enhance-cooling-effect/ Babu, S. (2020). Why is it cooler around trees? Retrieved January 27, 2021 from https://eco-intelligent.com/2020/05/23/why-is-it-cooler-around-trees/ Bureau of Meteorology. (2021). About Evapotranspiration. Retrieved April 10, 2021 from http://www.bom.gov.au/watl/eto/about.shtml#:~:text=Evapotranspiration%20is%20the%20term%20used,of %20both%20transpiration%20and%20evaporation.

Ennos, R. (2015). Can trees really cool our cities down? Retrieved January 27, 2021 from https://theconversation.com/can-trees-really-cool-our-cities-down-44099 Fritzsche, H. (2016). Wien’s law. Retrieved March 2, 2021 from https://www.britannica.com/science/Wiens-law/additional-info#history 137

Candidate code: hzy524 Lin, H., Chen, Y., Zhang, H., Fu, P., Fan, Z. (2017). Stronger cooling effects of transpiration and leaf physical traits of plants from a hot dry habitat than from a hot wet habitat. Functional Ecology, 31(12). Retrieved May 23, 2021 from https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2435.12923 Stirling, T. (2004). Transpiration – Water Movement through Plants [PDF]. Retrieved May 15, 2021 from https://www.sciencemag.org/site/feature/misc/webfeat/vis2005/show/transpiration.pdf USGS. (n.d.). Evapotranspiration and the Water Cycle. Retrieved May 22, 2021 from https://www.usgs.gov/special-topic/water-science-school/science/evapotranspiration-and-water-cycle?qt-sci ence_center_objects=0#qt-science_center_objects

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Mona Talks Corona: The Automated Chatbot for Adolescents

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Abstract: The identified problem within the area of communicating health is the challenge for adolescents to keep up with the dynamic health restrictions and regulations that surround the COVID-19 pandemic.

All means of communication in the research which we have conducted is targeted to media trends of adults and their specific wellbeing needs associated with finance, job security, marital relationships etc. With our functioning technological prototype ​Mona Talks Corona​, our goal is to fill the gap in the market and provide adolescents with instantaneous responses to their health queries as well as connect them to wellbeing resources appropriately matched to their requests. An additional benefit of this system is it’s multilingual facet which is inclusive to all cultural demographics within the adolescent target market as well as the use of voice to text and text to voice functions to aid communication to all people.

Acknowledgements: We would like to thank Mr Phillip Poronnick, our mentor, for giving us useful advice, suggestions and critiques for this research project. We would also like to thank Ms Rugiero, our science teacher and Gifted and Talented Coordinator, for providing us with continuous support and encouragement throughout this BIOTech journey.

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Table of Contents Cover page …………………………………………...………………………………………...pg1 Abstract …………………………………………...……………………………………………pg2 Acknowledgements …………………………………………………………………………….pg2 List of figures, tables and abbreviations ……………………………………………………….pg4 Literature Review ……………………………………………………………………………..pg5 Logbook.…………………………………………...…………………………………………...pg7 Challenges and Future Work.…………………………………………...………………………pg10 Conclusion…………………………………………………………………………….………..pg10 References…………………………………………………………………………….………..pg11

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List of figures, tables and abbreviations These are from a survey conducted on adolescents 13-18 in Sydney, testing their COVID-19 knowledge and questioning what they would most like to know about.

Table

Observed Trends

a. ● ●

Measures of Central Tendency ○ Average was 37.81/100 ○ Median was 40/100 Both are below 50% → most failed with their awareness of socialising regulations.

b. ●

32% selected incorrectly ○ Thus there is a gap which ​Mona Talks Corona​ can fulfil.

●

Only 6.3% were correct → a positive note is that 68.8% underestimated this number.

Might suggest at this time adolescents were socialising with a number of people lower than the recommended → reducing the probability of transmission.

c. ●

d.

●

18.8% were correct → 81.2% underestimated the regulations ○ Potentially demonstrating ( at the time of the survey ) socialisation could have been slightly increased to support the mental wellbeing of our consumers, another factor to be considered in conjunction to maintaining physical health.

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e. ●

The most wanted answers were in regards to the latest COVID-19 socialising restrictions. ( 86.6% ) ○ Followed by ‘What should I do when I get a COVID test?’ at 33.3%. This is extremely pertinent considering that a misunderstanding of the requirements or complete disregard is what led to the Northern Beaches cluster.

Literature Review Developing a research topic Communicating in health: Finding an effective and engaging way to spread important health information about COVID-19 to adolescents.

Key words - Target audience: Adolescents - Are often unaware of the situation - Communication types: apps, advertisements, social media, websites, chatbot.

- Mental health relating to COVID-19.

- Surveying adolescents -> seeing if they are aware of the changing rules and restrictions regarding COVID-19.

- Messages about what is wrong or illegal in current situations.

- Different vaccines and new research to help with the coronavirus.

- Which restrictions are most relevant for adolescents.

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Flowchart of how different concepts fit within that topic

Reviewing other literature: Leveraging media and health communication strategies to overcome the COVID-19 infodemic (​https://link.springer.com/article/10.1057/s41271-020-00247-w​) ● This article discussed how the COVID-19 pandemic was equally an ‘infodemic’ ○ confusion through the role social media played with informing adolescents → contributing to the creation of conspiracy theories. ○ What this dictates is that the effectiveness and validity of the information communicated is equally important.

Effective health communication​ (​https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180027/​) ● Provides clear instructions on what makes effective health communication + the importance of learning from this pandemic so that there will be systems in place for next time. ● Possibly a disaster template could be considered where the features of the health communication are specific to a ‘new disease’.

Reviewing existing advertisements: ●

German government coronavirus advertisement → message that couch potatoes are the ‘heroes’ of the COVID-19 pandemic. ○ effective as it is humorous, engages the youth and helps encourage people to act responsibly.

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Drawbacks with existing solutions: Advertisements - This solution might not be as effective for adolescents particularly because many of them don’t watch live terrestrial television and watch netflix or other streaming services.

Sam - The ‘chatbot’ available by the Department of health is tailored to the challenges and confusions experienced by predominantly adults.

Research question: There is a serious need for communicating in health due to the lack of platforms available for adolescents and thus influencing their approach to keeping safe and well during the pandemic. The primary focus of this project will be on the proposed innovative design for a chatbot to solve the struggle of communicating health to the specified consumers.

Logbook At least 3 designs, including your final design for a solution to your problem

Idea

Positive

Minus

Interesting/Improvement

TV series set in the future looking back on our present with the aim to boost morale so that adolescents are more inclined to follow the health restrictions that are communicated to them.

●

Demonstrating there will be a positive outcome to this pandemic has the possibility to incentivise our target market to contribute, knowing this is a challenge our society can collectively overcome.

●

Alternatively this video can be viewed in the light that we are so close to the solution ○ A) why bother continuing to follow the health recommen dations ○ B) our target market of adolescent s don’t believe their contributio n was necessary.

●

This idea was inspired by the Ad which Germany created. Since the target market is highly engaged by The Office related humour a satire tv show, featuring humour about the unprecedented times could potentially focus the attention of adolescents towards the communication of health.

An interactive instagram page that is regularly updated with communicated health messages direct from the department of health.

●

Not creating a new platform will ensure that the new service will be easily accessible to our consumers.

●

Unfortunately our hashtags and interactive activities would be subject to the trends and whims of social media to maintain the

●

Would be a continuation of the ‘#stayathome’ movement that lost momentum during the quarantine period.

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interest of adolescents. ○ This could prove to be a significant struggle.

An automated chatbot system available on popular social media sites, tailored to the queries of adolescents during COVID-19. → Mona Talks Corona

●

Responses are immediate and constantly updated so they align with the most recent and current communicated health messages.

●

Can develop a functioning prototype with the technological skill we have.

●

The limitations of the software itself is present in terms of the questions that can be asked and answered. The technology might not be prepared to answer all types of questions.

●

A way in which this negative has been improved in our prototype development has been through the use of abbreviations so a wider variety of inputs will be accepted.

Justification of our selected solution Mona Talks Corona​ effectively would address the challenge for adolescents keeping up to date with all the health restrictions, regulations and recommendations. The identified gap in the market is the need to address the health and wellbeing concerns of our consumers. Our survey notes which can be found in the section ​Tables, Figures and Abbreviations ​demonstrates the confusion and lack of knowledge adolescents have in regards to the current health climate. Currently the methods of health communication are not successful with our market.

The positive facets of this design are listed in the positive column above, with an additional direct improvement we have employed that removes the negative we had listed. Out of all the designs we have discussed in group meetings this not only was decided the most probable to work in regards to our identified problem but the most feasible for us to create with the skills we have available.

Reflections of our progress and meetings Meeting 1 - Wednesday 11/11/20 3:30pm - 4:00pm This was our first meeting with our mentor for the challenge, Philip Poronnik. We discussed some possible areas we could look into in our chosen topic area. Philip let us know about a few resources we could utilise when working with him and he also shared some sources to aid with our research.

During the meeting we discussed the target audience, leaning towards a more youthful audience, and we started brainstorming the problem we would like to combat.

Throughout the week, we each carried out a little bit more research, including laws regarding COVID-19 safety in workplaces and in public, as well as reading a few articles and watching some of the video sources we were sent.

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Meeting 2 - Wednesday 18/11/20 3:30pm - 4:15pm We further discussed areas for us to research and more interesting issues related to COVID-19 that haven’t already been overly discussed this year. After talking to Philip again, we developed an interest in how the excessive use of antibacterial products such as hand sanitiser and disinfectant spray may negatively affect our immune systems. We want to understand how prolonged exposure to antibacterials affects our microbial communities and ways to prevent this as well as ways to communicate the prevention methods. In our research doc, we set goals to achieve by our next meeting with Philip. We discussed looking to the past to see how people overcame past pandemics such as bubonic plague and Spanish flu. Further, we looked at ways to create a more positive product that would motivate people to make good change.

Meeting 3 - Wednesday 02/12/20 3:30pm - 4:00pm Through the two weeks between meetings, we had looked into possible products to develop and we’ve decided to create a chatbot. This would be a good method of communication with people of different backgrounds, particularly regarding health and safety. Between this session and our next in three week’s time, we plan to continue our research on chatbots and ways we can communicate with others.

We are also going to survey others regarding use experience and how we could improve on what already exists and create something innovative.

Meeting 4 - Wednesday 23/12/20 3:30pm - 4:00pm, onwards We made a good start on the chatbots, having made a few prototypes. We also created a poster, detailing a few statistics and the purpose of our chatbot. A survey had been sent out to students aged 13-18 and we quizzed them on their knowledge of current COVID-19 restrictions. Using our results, we were able to start a data analysis. The design for the mascot of our chatbot, Mona, was also created. We made a good start on our report and were well on track with the project.

We continued to work on our project and consulted with our mentor for some more advice. We created our slide and made several variations of the same slide with different colours to match the template’s colour scheme. When we were finally happy with our slide, we moved on to finalising the report. The chatbot was completed and we compiled all the clips of it working into one video.

OUR FUNCTIONING PROTOTYPE LINK: https://youtu.be/uTAXS1nijfU The AmazonLex software was used to code and create the functioning chatbot. After attending an Amazon enrichment session during school term, the coding skills developed were then able to be directly applied to the communication of health projects. With a growing prevalence of technology in the lives of youth, creating a chatbot using this software was ideal for meeting the needs of the target market.

The four main coding components of the chatbot were that of: 1. Intent- Having an intent was vital to creating a chatbot. It highlights the topic area or goal in which the users queries are to concern. A key example of an intent would be ‘bookatest’ which explores the queries around COVID-19 testing and assists in initiating booking test times.

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2. Utterance- A statement or question that is said by the user relates to the intent. I.e ​I would like to book a covid test in ​{place}​​ on ​{date}​​ at ​{time}​​ place​. This then has embedded slots that will assist the program in asking more questions to gather more information in regards to the booking.

3. Slot-types- Are created for each intent and are used to define how data in the slot is recognized and handled. For the bookatest analogy slot-types included placeslot (to gain insight into the user’s state), dateslot (for the preferred testing day) and timeslot (for the time of day preferred). Under each slot types there are the potential options that the user may say.l i.e. date- monday, tuesday etc.

4. Slots-define any information the bot carries out in an intent and are linked to the slot-type.

They are embedded into the utterance so that the bot is able to ask more questions and answer the query based on the responses found within the slot-type. I.e. I would like to book a covid test on ​{monday}​ (the day was a listed option under the slot-type) The prototype is able to directly meet the needs of the target market with options for voice to text and text to voice. This enables clear communication of rules, restrictions and answers to other queries answered for all people. Additionally, multiple language options are available with code available for both italian and english (US and AUS) speakers.

Challenges and Future Work ●

In future, our chatbot ​Mona Talks Coron​a could be tailored to answer questions on a broader scale concerning COVID-19. In the transitional period, between now and the new normal new questions and updated information will be added. (i.e bacterial resistance due to our increased hygiene practices)

●

Potential issues that may come up in the production of our design would be the difficulty in engineering an effective and easy-to-use chatbot feature. We designed our own prototype on AWS Educate by Amazon and its user interface is flawed. In future we might want to work more from scratch. ○ We can utilise artificial intelligence technology to answer a wider range of questions

Conclusion In conclusion, we’ve discovered a lack of knowledge concerning current COVID-19 health messages and restrictions. In order to combat this issue, we have come up with the solution of a chatbot, named Mona Talks Corona,​ to assist everyday individuals on COVID-19-related information on the platform of their routinely visited social media sites. The identical service across the Department of Health website and social media sites will mean that the same information will be easily accessible to wherever our consumers of adolescents wish to find it.

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References Australian Government Department of Health. (2021). ​Coronavirus (COVID-19) current situation and case numbers. ​Retrieved from January 4, 2021, from

https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/coronavir

us-covid-19-current-situation-and-case-numbers Australia Government Department of Health. (2021).​ Sam the chatbot.​ Retrieved November 5, 2021, from ​https://headtohealth.gov.au/sam-the-chatbot

Fernando, G. (2020, November 11).​ This German coronavirus ad is urging young people to be the heroes of 2020 - by staying at home on the couch.​ SBS NEWS. Retrieved on December 28, 2020, from

https://www.sbs.com.au/news/this-german-coronavirus-ad-is-urging-young-people-to-be-the-heroe s-of-2020-by-staying-at-home-on-the-couch Finset, A. (2020). Effective health communication – a key factor in fighting the COVID-19 pandemic.

Elsevier Public Health Emergency Collection​. Retrieved on November 24, 2021, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7180027/

J, Andrews, L, Foulkes, & S, Blakemore. (2020). Vol.24, No.8 pg.(585-586). Trends in Cognitive Sciences. [PDF]. Retrieved on Jan 7, 2021.

Mheidy, N., & Fares, J. (2020). Leveraging media and health communication strategies to overcome the COVID-19 infodemic.​ Journal of Public Health Policy.​ Retrieved on November 24, 2021, from ​https://link.springer.com/article/10.1057/s41271-020-00247-w

NSW Government. (2021). ​COVID-19 (Coronavirus). ​Retrieved from January 2, 2021, from https://www.health.nsw.gov.au/

NSW Government. (2021). ​Latest COVID-19 news and updates. ​Retrieved December 31, 2020, from https://www.nsw.gov.au/covid-19/latest-news-and-updates

Reuters. (2020, November 16)​ 'Be lazy, save lives,' Germans urged in COVID video.​ [Video file]. https://www.youtube.com/watch?v=FS1DDn2eklU

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World Health Organisation. (2021).​ Coronavirus disease (COVID-19) pandemic.​ World Health Organisation. Retrieved on December 2, 2021, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019

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Mona Talks Corona J. Benedict, S. Jayatillake, V. Kim, L. Leonard, S. Witting Academic Advisor: Ms Rachele Rugiero Mentor: Philip Poronnik

MG018

BIOTECH FUTURE

2021

Contact Information

Team Leader: jayats@ssc.nsw.edu.au Academic Advisor: r.rugiero@ssc.nsw.edu.au