2018 SUMMER RESEARCH SCHOLARS
Parallel GPU Based Simulations of Multilayer Neural Networks with MultiValued Neurons in a MATLAB environment By: James S. Abreu Mieses Advisor: Igor Aizenberg School of Science. Department of Computer Science • Problem: • Speeding up a process of complex-valued neural network (MLMVN) training by the use of GPU devices
x1 x2
O1
• Objective: • The investigation of a GPU based simulation of MLMVN on specific problems to analyze possible bottle necks and the learning process of the neural network. • A MLMVN (Multi-Layer Neural Network with Multi-Valued Neurons) is a feedforward neural network which learning rules is based on the error correction rule.
• Approach: • Using MATLAB Parallel Programming Tool Box, the neural network was translated to be able to execute on a CUDA enabled GPU. • CUDA is an API (Application Programming Interface) developed by Nvidia that allows for usage of a GPU for general purpose programming (GPGPU) and a GPU (Graphics Processing Unit) is a device designed to quickly process memory(RAM) for the creation of images intended to be shown on a screen. The processing power of the GPU is what make it useful in large data processing. Serial time sec Serial - # of iter Serial 1 iter (average) sec Neuron Network Size 7.73 6 1.29 2-65536-1 105.19 44 2.39 2-131072-1 49.22 14 3.52 2-262144-1 109.4 16 6.84 2-524,288-1 43 3 14.33 2-1,048,576-1 148.02 5 29.60 2-2,097,152-1 298.19 5 59.64 2-4,194,304-1 Parallel time sec Parallel - # of iter Parallel 1 iter (average) sec GPU Speed Up Per Iteration 3.8 4 0.95 1.36 10.97 6 1.83 1.31 16.99 4 4.25 0.83 29.27 15 1.95 3.50 53.12 13 4.09 3.51 138.71 19 7.30 4.06 326.51 19 17.18 3.47
• Conclusion: • As the size of the neural network increase also did the processing speed of the GPU. Experimental results show that the GPU implementation of the neural network could yield a 1.5~3.5�� speedup compared to the CPU optimized implementation of the same averaging to a speedup result of 3.15�� between iterations.
Theoretical and `Experimental Design of Efficient Polycyclic Aromatic Hydrocarbons Adsorbents Jeovanna Badson
Department of Chemistry/Biochemistry Manhattan College INTRODUCTION
METHODS AND MATERIALS
Polycyclic Aromatic Hydrocarbons (PAHs) are organic compounds containing only carbon and hydrogen atoms and are comprised of multiple aromatic rings. Aromatic rings are organic rings in which the electrons are delocalized. PAHs are uncharged, non-polar molecules. They are known ubiquitous carcinogenic molecules [1-4] created primarily from the incomplete combustion of organic matter. They are highly lipid soluble and is therefore easily absorbed in the gastrointestinal tract of mammals. PAHs are often found in the surrounding air in gas phase and as sorbet in aerosols. The simplest of such chemicals are naphthalene, which has 2 aromatic rings and anthracene and phenanthrene, each containing 3 rings. Our goal of this research is two fold: first, to correlate our calculated reaction energies with experimental absorptions efficiencies and then to suggest means of designing new adsorbent moieties.
Design and calculation of all molecules were done on Spartan ’16. The quantum chemical calculations of each molecule were executed using the PM3 semiempirical Hamiltonian set of parameters, of ionization energies incorporated in the Spartan ‘16 software suite. For a step-by-step procedure for creating and calculating the energies of the molecules refer to Spartan’16 for Windows, Macintosh and Linux Tutorial and User’s Guide. [5]
Figure 4: Linoleic Acid Interacting with a Phenanthrene Molecule
Figure 5: Electrostatic Potential of Linoleic Acid Interacting with a Phenanthrene Molecule
ACKNOWLEDGEMENTS
Figure 1: Template Chemical Reaction Figure 6: Diphenylacetic Acid Interacting with a Phenanthrene Molecule
RESULTS No.
Lipophilic Carboxylic Acids
1
3,3 - Diphenylpropionic Acid
Energy of Carboxylic Acids (kJ/mol)
Structural Formulas
1 Acid : 1 Phenanthrene Energy of Erxn Product (kJ/mol) (kJ/mol)
Figure 7: Electrostatic Potential of Diphenylacetic Acid Interacting with a Phenanthrene Molecule
PAH Capacity (mg PAH/ g SiO2)
-200.19
25.93
-4.11
0.189 ±0.0567
-328.95
-108.27
-9.55
0.1266 ± 0.0016
-598.17
-392.74
-24.8
0.2807 ± 0.0301
-304.99
-83.99
-9.23
0.1332 ± 0.0199
-190.73
49.96
10.46
0.2144 ± 0.0211
-603.52
-382.09
-8.8
0.1043 ± 0.0182
-520.06
-317.76
-27.93
0.1291 ± 0.00198
-807.59
-610.05
-32.69
0.1419 ± 0.0061
-354.12
-134.34
-10.45
0.1650 ± 0.0914
-228.42
26.68
24.87
0.1472 ± 0.0934
2
Phenylpropionic Acid
3
Linoleic Acid
4
Phenylacetic Acid
Figure 7 demonstrates using the electrostatic potential the limited interaction these molecules have. Table 1 displays the theoretical calculations and their respective experimental values. This can also be seen in Figure 12 which shows the graphical representation of the theoretical reaction values in comparison to the experimental PAH capacity of the same lipophilic carboxylic acids. The discrepancy seen in both figures could be due to solvent effects, entropy effects and the difficulty of the experiment. Despite these difference, these calculations and experimental data are a precursor to more precise density functional calculations.
I would like to thank the Michael J. Kakos fund, and Dean Theodosiou of the School of Science for generous financial support. I also like to thank Dr. Joseph Capitani, Dr. John Regan and Dr. Jianwei Fan for all the helpful discussions and their continuous guidance and support throughout this research project.
5
Diphenylacetic Acid
6
Decanoic Acid
7
Cyclohexaneacetic Acid
8
Stearic Acid
9
α - Phenylcyclopentylacetic Acid
10
1 - Naphthaleneacetic Acid
Figure 8: Comparison of the Theoretical Erxn (kJ/mol) vs the Experimental PAH Capacity (mg PAH/ g SiO2)
Table 1: Semi-Empirical Calculations and Experimental PAH Capacities of the Lipophilic Carboxylic Acids with Phenanthrene
Figure 2: Stearic Acid Interacting with a Phenanthrene Molecule
CONTACT Jeovanna Badson Manhattan College Email: jbadson01@manhattan.edu Figure 3: Electrostatic Potential of Stearic Acid Interacting with a Phenanthrene Molecule
DISCUSSION Figure 2 illustrates the interaction between Stearic Acid and Phenanthrene. It has one of the strongest theoretical reaction energy. Figure 3 further illustrates the hydrophobic interaction of these two molecules using the electrostatic potential. Figure 4 shows Linoleic Acid as it also interacts with phenanthrene. In comparison, stearic acid interacts more strongly with phenanthrene based on the Spartan[5] calculations. This could be due to the “kinks” in the linoleic structure that reduces the surface area that the phenanthrene is able to interact with. In figure 5, the electrostatic potential of this interaction is seen. This image indicates that the linoleic acid creates a pocket of sorts for which the phenanthrene can insert itself. Figure 6 demonstrates diphenylacetic acid’s interaction with phenanthrene. Based on the calculations it was the least stable interaction with the phenanthrene molecule.
REFERENCES 1. Meyers, P. A.; Ishiwatari, R. (1993). "Lacustrine organic geochemistry—an overview of indicators of organic matter sources and diagenesis in lake sediments". Organic Geochemistry. 20 (7): 867–900. doi:10.1016/0146-6380(93)90100-P. ISSN 0146-6380. Retrieved 2015-02-04. 2. Wakeham, S. G.; Schaffner, C.; Giger, W. (1980). "Poly cyclic aromatic hydrocarbons in Recent lake sediments—II. Compounds derived from biogenic precursors during early diagenesis". Geochimica et Cosmochimica Acta. 44 (3): 415–429. Bibcode:1980GeCoA..44..415W. doi:10.1016/0016-7037(80)900411. ISSN 0016-7037. Retrieved 2015-02-04. 3. Bostrom, C.-E.; Gerde, P.; Hanberg, A.; Jernstrom, B.; Johansson, C.; Kyrklund, T.; Rannug, A.; Tornqvist, M.; Victorin, K.; Westerholm, R. (2002). "Cancer risk assessment, indicators, and guidelines for polycyclic aromatic hydrocarbons in the ambient air". Environmental Health Perspectives. 110 (Suppl 3): 451–488. doi:10.1289/ehp.02110s3451. ISSN 0091-6765. PMC 1241197 Freely accessible. PMID 12060843. 4. Loeb, L. A.; Harris, C. C. (2008). "Advances in Chemical Carcinogenesis: A Historical Review and Prospective". Cancer Research. 68 (17): 6863–6872. doi:10.1158/0008-5472.CAN-082852. ISSN 0008-5472. PMC 2583449 Freely accessible. PMID 18757397. 5. Spartan’ 16, Spartan’16 for Windows, Macintosh and Linux Tutorial and User’s Guide [Pdf]. (2016). Irvine, CA: Wavefunction, Inc.
Body Size Variation and Sexual Size Dimorphism of Ratsnakes from the American Museum of Natural History Presenter: Alexander Constantine – Advisor: Dr. Gerardo Carfagno The School of Science Summer Research Scholars Program
Introduction
•Patterns of geographical variation in body size within species is important because size affects nearly all life-history traits of an organism [1]. •Biologist Carl Bergmann ruled that body size is directly proportional to changes in latitude [2]. •An exception to this rule exists in ectotherms, in this case ratsnakes, where body size declines with latitude [3] where they have been found to be larger around core latitudes in the Midwest and smaller near latitudes further north and south of the Midwest. •My study observed the differences in various body measurements of both sexes of ratsnakes archived in the AMNH in NYC. •Hypotheses and Predictions: My goal was to measure and compare the head length, head width, snout vent length (SVL), tail length, total length, and sexual size dimorphism (SSDI) of male and female ratsnakes taken from various states ranging from Texas to New York to Florida. •I hypothesized males would be larger than females and that snakes in the most northern and southern states would be smaller than those near the central states. •I predicted that males would be larger because ratsnakes are known to engage in fights with one another over a female, ergo males are larger than females in these populations as larger males seize the reproductive advantage [4].
Materials and methods
•Sampling: March-August 2018 for a total of 26 days, all specimen were provided by the archives of the Herpetology Department of the AMNH in New York City. •The snakes were preserved in jars of 70% ethanol. •For analyses, we included only data from snakes greater than 80 cm in SVL as this is the smallest reported adult size [5]. •Data Collection: Twine (fig. 1) was used to measure the SVL and the tail length of each snake, stretched along the dorsal side of the snake from its snout down to its vent and then measured with measuring tape. •Electronic Calipers (fig. 2) were used to measure the head length and width of each snake.
Figures 1.2.3: Twine, Electronic Calipers, Data Collection set-up, respectively.
Discussion
Results
Graph 1. Average Tail Length between all males vs. females. Males have longer tails than the females.
Graph 4. Average Head length between all males and females per state.
•The northern most state, RI, has ratsnakes with longer heads than the southern most state, FL (Graph4). •LA might be influenced by small sample sizes. • There is no consistent pattern in head length between these states and there is not a large variation either as the heads range from 3.5-4 cm. (Graph4).
Graph 2. Average Total Length between all males vs. females per state. Males are larger than females in every state and largest in the northern most state of RI.
Graph 3. SSDI (-M/F)+1) using SVL of males and females per state. A negative value indicates the male being larger. The farther the value is from 0, the stronger the degree of SSDI.
•Degree of SSDI is fairly consistent in the Northern states, but progressively becomes more varied from TN to FL. Future statistical analysis could provide evidence of a significant interaction between sex and population location. (Graph3)
•Males have longer tails than females, showing males are larger, despite possibly damaging their tails in combat. •Higher latitudes would tend to yield smaller sized ectotherms due to shorter growing seasons and a greater surface area to absorb sunlight during colder months. [3] •The body size of ratsnakes varied geographically, but not latitudinally. •Lack of latitudinal SSDI, total length, head length and width variation suggests ratsnakes’ size is instead driven more by local resources. •Depending on the local resources of a ratsnake in a given state, their head length and head width will vary in size. •The direction of SSDI supports the prediction that male-male combat drives SSDI in snakes. •However in NC females have a larger SSDI. •The data may have been compromised due to small sample sizes limited to the museums archival collection. •The data may also have been skewed since museums tend to capture the largest specimen in the wild as they are the easiest to find and study. •However, further study is needed as the presented data is purely preliminary and statistical analyses will be used to determine significance at a later date.
Literature cited
[1] Brown, J.H., Gillooly, J.F., Allen, A.P., Savage, V.M., and West, G.B. 2004. Toward a metabolic theory of ecology. Ecol. 85: 1771-1789. [2] Bergmann, C. 1847. U”ber die Verha”ltnisse der Wa”rmeo”konomie der Thiere zu ihrer Gro”sse. Go”ttinger Studien. 3: 595-708 [3] Ashton, K.G., and Feldman, C.R. 2003. Bergmann’s rule in nonavian reptiles: turtles follow it, lizards and snakes reverse it. Evolution 57: 1151-1163. [4] Gillingham, J.C. 1980. Communication and combat behavior of the black rat snake (Elaphe obsolete). Herpetologica 1: 120-127. [5] Ernst, C.H., and Ernst, E.M. 2003. Snakes of the United States and Canada. Smithsonian Books, Washington, DC, USA.
Graph 5. Average Head width between all males and females per state.
•The northern most state, RI, has ratsnakes with longer heads than the southern most state, FL (Graph5). •LA might be influenced by small sample sizes. • There is no consistent pattern in head width between these states and there is not a large variation either as the heads range from 1.66-2.03 cm (Graph5).
Acknowledgments
We thank Manhattan College, the Dean of the School of Science, Dr. Constantine Theodosiou, and Linda and Dennis Fenton ’73 Endowed Biology Research Fund for financial support, and the American Museum of Natural History for logistic support as well as specimen sampling.
For further information
Please contact aconstantine01@manhattan.edu for further questions.
SURFACE FUNCTIONALIZED ION EXCHANGE RESINS AS CHEMICAL REDUCING AGENTS: APPLICATIONS FOR CHROMIUM(VI) REMOVAL NICHOLAS DUSHAJ DEPARTMENT OF CHEMISTRY AND BIOCHEMISTRY BACKGROUND
•
Hexavalent chromium, or Cr(VI), is a well-known significant health risk in drinking water
•
The US Environmental Protection Agency (EPA) has set a standard of 0.8đ?œ‡đ?œ‡M of chromium ions for drinking water
•
Sodium borohydride (NaBH4) is a reducing reagent used in organic synthesis but can also serve as an electron donor in buffered homogeneous solutions of Cr(VI).
O
O O
Cr O O
Cr(VI)
• • •
Identify environmentally-friendly buffers
MP-Borohydride
• •
MP-BH4: Borohydride ion attached to a macroporous polystyrene-supported cationic ion exchange resin Stable in slightly alkaline environment
Experimental Procedure •
To 20mL of 200đ?œ‡đ?œ‡M Potassium dichromate (Cr(VI)) and 25đ?œ‡đ?œ‡M buffer was added 20mg of MP-BH4 Reaction stirred for 2 hours and Cr(VI) concentration measured by UV spectroscopy at 373 nm
OH
Na BH4 H2O (+ 3 e-)
Use an insoluble solid-supported electron donating reducing agent that’s easy to remove thereby minimizing further water contamination
MATERIALS & METHODS
•
General Reduction Reaction
O
•
Some Cr(VI) elimination strategies rely on chemical transformation of Cr(VI) to Cr(III), a dietary supplement, by the addition of three electrons.
•
O Cr
•
GOALS
HO
Cr OH + Na
B(OH)4
ISSUES
RESULTS
+ H2(gas)
Cr(III)
NaBH4 contributes to pollution profile of wastewater Sodium borate as a buffer in high concentration also contributes to pollution profile Unable to easily remove byproducts of reaction
2. CHANGES IN BORATE CONCENTRATION (200đ?œ‡đ?œ‡M Cr(VI) and 15 molar equivalents of MP-BH4)
Cr(VI) Molar Ratio Reduction Volume (mL) MP-BH4: Cr(VI) 10
30
100%
20
15
100%
30
10
97%
40
7
79%
50
6
75%
Conclusion MP-BH4 reduces Cr(VI) at low molar ratios.
Reduction 100%
100
100%
50
97%
25
94%
0
25%
Conclusion Reduction of Cr(VI) at lower concentrations of sodium borate is possible. Borohydride ion degrades without a buffered solution.
3. DIFFERENT BUFFERS (25đ?œ‡đ?œ‡M Buffer Solutions and 200đ?œ‡đ?œ‡M Cr(VI) with 15 molar equivalents of MP-BH4) Buffer Name
Reduction
1
Sodium Borate
94%
2
Sodium Bicarbonate
91%
3
Glycine
97%
4
AMP
93%
Entry
1. MOLAR RATIOS OF MP-BH4 TO CR(VI)
(Different volumes of 200ÂľM Cr(VI) with MP-BH4 in 1000 ÂľM sodium borate)
[borate] đ?œ‡đ?œ‡M 1000
5
Structure
2-CHES
97%
Conclusions • Chemical diversity of buffer solutions provide high levels of Cr(VI) reduction • Sodium bicarbonate and glycine are the most environmentally friendly
4. KINETICS OF STIRRING ENVIRONMENT (Change of stirring speed with 200đ?œ‡đ?œ‡M Cr(VI) and 15 molar equivalents of MP-BH4)
Conclusion • Complete Cr(VI) reduction occurs at 60 minutes • 300 rpm and 500 rpm are sufficient stirring speeds to reduce Cr(VI)
CONCLUSIONS • MP-Borohydride (i) reduces Cr(VI) from wastewater using low molar ratios, (ii) is easily removed by filtration and (iii) does not contribute to water pollution • Glycine and sodium bicarbonate, environmentally-friendly buffers, are effective in low concentrations
ACKNOWLEDGEMENTS I would like to thank The School of Science for financial support. I would like to express my gratitude to my mentor, John Regan, for guiding my research, and for giving me the opportunity to gain research experience. I would also like to thank my parents for constant support in every step of the way.
XYLEM CONDUCTIVITY OF PRIMARY, SECONDARY AND TERTIARY VEINS OF PLANT LEAVES MAYA CARVALHO-EVANS LABORATORY OF PLANT MORPHOGENESIS BIOLOGY DEPARTMENT, MANHATTAN COLLEGE BACKGROUND
Leaf Vein
H₂O
HYPOTHESIS Larger leaf areas will have larger xylem conductivities
Leaf Vein • Water in plants is only conducted in xylem cells • Xylem conductivity is used to estimate water conduction
Primary Vein
Secondary Vein
Tertiary Vein
Xylem vessel/conduits diameters • Randomly measured using ImageJ
Xylem = Conductivity
π · number of cells · average radius of cells (cm)⁴ 8 · viscosity of water Conductivity units are g·cm·MPa-1·s-1.
23 Percurrent Species
Primary Vein
0
50
100
150 200 Leaf Area (cm²)
y = 0.044x - 1.98 R² = 0.83
250
Secondary Veins
0.6
Average xylem conductivity (g·cm·MPa¯¹·s¯¹)
Xylem Cells
14 12 10 8 6 4 2 0
300
350
y = 0.031x - 0.046 R² = 0.68
0.5 0.4 0.3 0.2 0.1 0
Average xylem conductivity (g·cm·MPa¯¹·s¯¹)
Morus rubra
Average xylem conductivity (g·cm·MPa¯¹·s¯¹)
METHODS
0
5 10 Secondary Area (cm²)
Tertiary Veins
0.03
15 y = 0.0085x - 0.0018 R² = 0.82
0.025 0.02
0.015 0.01
0.005 0
0
0.5
1
1.5 2 Tertiary Area (cm²)
2.5
3
Predicting Bark Rates of Saguaro Cacti
George
1Department
1 Kennedy ,
Dr. Ehsan
1 Atefi
and Dr. Lance
of Mechanical Engineering, Manhattan College, 2Department of Biology, Manhattan College
gkennedy01@manhattan.edu
Epidermal browning or bark coverage occurs on saguaro cacti native to Tucson, Arizona due to sunlight exposure. Using the database of cactus bark coverages, it is possible to predict epidermal bark rates in future years. The Classification Learner App in Matlab was used for its supervised machine learning to train models relevant to the database of bark coverages.
The Saguaro cacti have vertical ribs with crests (protrusion) and are separated by convex troughs. For each crest and trough, a surface eight cm long was evaluated at 1.75 meters from the ground. The percent bark coverage for each surfaces was then estimated visually. The percentages were compiled in an excel spreadsheet in a format compatible with the machine learning application.
Materials and Methods
Machine Learning Inputs
Introduction
2 Evans
Saguaro cacti were studied in Tucson Mountain Park. In 1994, 50 permanent plots with 1149 cacti were randomly selected. The selected cacti were all taller than 4 meters. Cactus morphological features of cacti, characteristics of nearby vegetation, topographical features and GPS data were used to identify cacti for each field evaluation.
Machine Learning Results
The above decision tree was an output of the classification application in Matlab. It shows the cacti being classified by the percentage of bark on their North Right Trough.
The figure above is a scatter plot of data to predict classes 1 through 5 based upon data from 1994 with an accuracy of 99.6%. The classification app generated a scatter plot for the model being trained. The predictor surfaces can also be changed in order to look at different relationships between surfaces and bark coverages.
This confusion matrix is a representation of the accuracy with which the application classifies cacti based on bark coverage.
The authors would like to show appreciation to Dr. Lance Evans for collecting and providing the data and providing the funds to conduct this study, without which, this study could not have been possible.
Investigating the Role of the Med Protein in Biofilm Formation Juan Lara-Garcia & Dr. Sarah Wacker Department of Chemistry and Biochemistry, Manhattan College, Riverdale, NY Jasper Research Scholars
Background •
The bacteria Bacillus subtilis forms biofilms on tomato roots. The pathway through which biofilm formation occurs begins with five histidine kinases and results in the activation of Spo0A which goes on to increase expression of genes that create biofilm matrix.
Figure 1: Simplified pathway of sporulation kinases and phosphorelay signal transduction pathway of B. subtilis.1 It is believed that Med acts in the same pathway as KinD.
Wt
Luciferase Reporter & Biofilm Assays
Δmed
•
• •
Root exudates stimulate for the formation of matrix genes. B. subtilis’s response to root exudates depends on the histidine kinase KinD1. Different small molecules have been shown to stimulate biofilm formation in B. subtilis such as malic acid and pyruvate. Biofilm formation through these molecules has also been reported to require KinD. The protein Med has been determined to be in the same pathway as the the protein kinase KinD2. Med is a membrane-anchored protein with a predicted small-molecule binding domain which is extracellular3.
3610+LB 3610+LB 3610+LB
Wt
Δmed
ΔKinD
sdpAlux reporter
sdpAlux reporter
tapAlux reporter
tapAlux reporter Wt
Δmed
ΔKinD
Figure 2: Biofilm assay with bacteria in LB. Top (left to right) CY136, TT26, and CY137. Bottom (left to right) ALM91, JLG02a, and RL5313. Labels above and below describe mutations and labels on side describe luciferase reporter in strain. Effect of
Malate •
ΔKinD
3610+LBGM
3610+LBGM
3610+LBGM
Figure 3: Biofilm assay depicting 3610 wild type B. subtilis without luciferase reporter replicated 3 times on top in LB media and 3 times on the bottom in LBGM (LB supplemented with 1% glycerol and 0.1 mM manganese sulfate). Malate on sdpA Expression
Wt
Δmed
Pyruvate
Figure 4: Biofilm assay with same strains as Figure 2, but in LB supplemented with 5mM malate + 2.5mM pyruvate
Effect of Malate on tapA Expression*
Effect of Pyruvate on tapA Expression
Purpose The purpose of this study is to determine what role, if any, the protein Med plays in the formation of biofilm in Bacillus subtilis. We hypothesize that Med plays a role in the ability of KinD to sense small molecules.
Methods In order to investigate the role of Med in biofilm formation, we used a two part strategy: 1) Examine whether small molecules that have been reported to stimulate B. subtilis biofilm formation are affected by a Δmed mutation. To test these I created B. subtilis containing Δmed mutation using phage transduction and then tested these strains in direct biofilm assays and indirect luciferase reporter assays. 2) Clone and purify His-tagged recombinant Med protein in order to determine whether it directly binds KinD. As Med is lipid-anchored, we worked with two constructs of Med, the full-length protein and a version that was missing the first 28 amino acids and thus would not have a lipid anchor.
Figure 6: Biofilm assay with same strains as Figure 2, but in LB + 2.5mM pyruvate
Root Extracts
Figure 7: Biofilm assay with same strains as Figure 2, but in LB +1% tomato root extract wash ** Data for Wt (sdpA) and tapA reporter strains excluded as data showed same trends as data shown
Effect of Root Extract Samples on ΔMed (sdpA)*
1 2 3 4
Effect of Root Extract Samples on ΔKinD (sdpA)*
5 6 7 8 9 10
Med protein
ΔKinD
Figure 5: Biofilm assay with same strains as Figure 2, but in LB + 5mM malate Effect of Pyruvate on sdpA Expression
Expression of Med and Purification Figure 8: Gel for the expression and
1) 2)
affinity purification of His-tagged, Nterminal-shortened Med (pJLG02) 1. Ladder 2. Pre-IPTG 3. Post-IPTG 4. Supernatant 1 5. Flowthrough 6. Pellet sample 7. Wash 8. Elution 1 9. Elution 2 10. Elution 3
Transform BL21 comptetent cells w/ pJLG01/pJLG02 plasmid Inoculate six cultures of LB (w/ 100 ug/mL of ampicillin) with the transformed BL21 cells and shake overnight at 37C 3) Transfer 7mL of combined pJLG01 or pJLG02 cultures to two 500 mL flasks containing LB and and 100 ug/mL of ampicillin and shake until culture have an OD600=0.5 4) Add IPTG to flasks, after 4 hours of shaking, remove the flasks and centrifuge at 4000 rpm 5) Store cells at -20C 6) The following day, resuspend in Bugbuster Extraction Reagent (40mL) by pipetting up and down at room temperature 7) Rotate cells in Bugbuster Reagent at room temperature for 30 minutes 8) Transfer supernatant to 2 centrifuge tubes and centrifuge at 20,500 rpm for 45 minutes at 4C to separate membranes. 9) Combine the soluble protein fraction (supernatant) with equilibrated Ni-NTA for 2 hours at 4C. 10) Pour supernatant with Ni-NTA resin through a column, and then wash with 200mL of His wash buffer 11) After all buffer has run out of the column, elute the protein w/ 8mL of elution buffer in multiple fractions
Discussion
Luciferase assays suggest that malate at a concentration of 5mM stimulate luciferase activity in both Δmed and ΔkinD mutants, indicating neither protein is important for malate signaling. Pyruvate stimulates Δmed mutants but not ΔkinD mutants, suggesting that Med isn’t necessary for the detection of pyruvate, while KinD is. More testing of different combinations of small molecules is needed as a biofilm assay of mutants with both pyruvate and malate yield different biofilm phenotypes than either molecule alone. Root extract samples yield similar results in wild type strains and mutants, suggesting that there may be other factors that play a role in stimulating biofilm formation more so than the Med protein.
References
1. Chen, Y. et al. (2012). A Bacillus subtilis sensor kinase involved in triggering biofilm formation on the roots of tomato plants. Mol Microbiol 85, 418-430. 2. Banse, A. V., Hobbs, E. C. & Losick, R. (2011). Phosphorylation of Spo0A by the histidine kinase KinD requires the lipoprotein med in Bacillus subtilis. J Bacteriol 193, 3949-3955. 3. Wu, R. et al. (2013). Insight into the sporulation phosphorelay: crystal structure of the sensor domain of Bacillus subtilis histidine kinase, KinD. Protein Sci 22, 564-576.
Predicting Bark Coverage on Saguaro Cactus Plants (Carnegiea gigantea) Marissa LoCastro, Dr. Lance Evans, Biology Department The author is grateful to the Catherine and Robert Fenton Endowed Chair to Dr. L.S. Evans financial support for this research.
Background Saguaro cacti (Carnegiea gigantea) are native to Tucson, Arizona. Sun exposure results in bark formation on the ribbed surfaces of the cacti plants. Barking begins on the south facing surfaces and makes its way around the stem of the plant, increasing the destruction of the cactus’ health as bark accumulates. Bark coverage prevents gas exchange, resulting in premature death.
Healthy
Purpose The purpose of this research is to use Machine Learning Programs to understand bark accumulation on the major predictive surface of cactus death: north right trough. This will lead to an understanding of the bark dynamics leading to cactus death.
Unhealthy
No Bark
Bark Formation
Method
Bark Coverage
• Percent bark data was collected for 1149 cacti in Tucson Mountain Park. • Percentages of bark on 12 cactus surfaces were determined in 1994, 2002, 2010 and 2017. • Data were entered into WEKA 3.8, and Validate Model program to predict bark percentages over 8-year intervals.
Results
Conclusion 2 Conclusion 1 Weka 3.8 can predict bark coverages with an accuracy >90%. The path for making predictions consistently uses trough surfaces .
MATLAB Program Validate Model and WEKA 3.8 confirm that trough surfaces are better predictors of bark coverage then crest surfaces. Validate Model had lower standard deviation and standard error when using troughs to make a prediction. WEKA 3.8 was always able to make a predictive path using troughs, but never could make a path using crests.
Conclusion 3 The sum of east and west-facing troughs can be used to split the data into three groups (p<0.05).
Conclusion 4
Conclusion 5
The sum of the east and west-facing trough surfaces are not used to make predictions using all data.
The sum of the east and westfacing trough surfaces is used to make predictions when the added surfaces are removed.
Bark and Spines of Neobuxbaumia mezcalaensis and Pachycereus hollianus Catherine McDonough, Laboratory of Plant Morphogenesis, Biology Department, Manhattan College Hypotheses
Tehuacan-Cuicatlan Biosphere Reserve, San Juan Raya, Puebla, Mexico 18° N, 97° W
1. More bark coverage on South-facing surfaces than on other surfaces 2. More bark coverage à lower number of spines
Class I/A 0 - 24%
Neobuxbaumia mezcalaensis
Class II/B 25 - 50%
Class III/C 51 - 74%
Class IV/D 75 - 100%
Pachycereus hollianus
Neobuxbaumia mezcalaensis Bark Data Class/ Coverage Class I (0-24%) Class II (25-49%) Class III (50-74%) Class IV (75-100%)
South Crests
10
East Crests
North Crests
10
9
West Crests
Very young Cactus 10 Spines
Old cactus no bark 5-7 Spines
Old cactus with bark 0-4 Spines
Spine Data Class/ Coverage
Apical Spines
Central Spines
Radial Spines
Total Spines
Optimum
2
1
7
10
0.60
0.82
5.92
7.31
0.27
0.50
3.09
3.86
0.26
0.22
3.17
3.60
0.17
0.18
2.94
3.27
10
Class A (0-24%) Class B
33
30
60
34
66
59
57
40
93
88
77
66
(25-49%) Class C (50-74%) Class D (75-100%)
Neobuxbaumia mezcalaensis Conclusions
1. Neobuxbaumia mezcalaensis loses about half its spines after 25% bark coverage 2. On average there is more bark coverage on the south-facing surfaces than other surfaces.
Pachycereus hollianus Bark Data Class/ Coverage Class I (0-24%) Class II (25-49%) Class III (50-74%) Class IV (75-100%)
South Crests
East Crests
North Crests
West Crests
8
20
15
9
33
55
25
15
62
53
44
27
95
76
55
39
Very young cactus 17 Spines
Old cactus no bark 15-16 Spines
Old cactus with bark 14 Spines
Spine Data Class/ Coverage
Apical Spines
Central Spines
Radial Spines
Total Spines
Optimum
2
3
12
17
1.89
2.92
11.1
15.9
1.82
2.91
9.95
14.8
1.77
2.80
10.2
15.0
1.82
2.90
9.49
14.2
Class A (0-24%) Class B (25-49%) Class C (50-74%) Class D (75-100%)
Future research will be to analyze the bark and spines of the other three species found in the Tehuacan-Cuicatlan Biosphere Reserve.
Pachycereus hollianus Conclusions
1. Pachycereus hollianus does not lose many spines with bark coverage. 2. On average, there is more bark coverage on the south-facing surfaces than other surfaces.
The author is indebted to the Catherine and Robert Fenton Endowed Chair to Lance S. Evans for financial support for this research.
Why Now Factor
HOW WRITE A SCIENTIFIC Before Writing RESEARCH Writing PAPER • Introduction
• Materials and Methods
By Lilli McHale Under the advisement of Dr. • Lance Evans • With funding provided by the Catherine and Robert • Fenton and Linda and Denis • Fenton Endowments
Results section Tables and Figures Discussion Work cited
• Abstract • Suggestions • Writing references • Sources to consult for formatting help
Formal Writing: creating the first draft • Writing Style for Science Publication • Grammar • Sentence Structure • Paragraph Structure • Font size • Title • Symbol and Number Use • Acknowledgements • Keywords • Following Existing Design
Editing drafts Second drafts Final draft and proofreading Borja, Angel. “11 Steps to Structuring a Science Paper Editors Will Take Seriously.” 1st Edition, Butterworth-Heinemann, 24 June 2014. Evans, Lance. Appendix A. Manhattan College, Date? Finkelstein, Leo. Pocket Book of Technical Writing For Engineers and Scientists. MCGRAWHILL, 2007. Greene, Anne E. Writing Science in Plain English. Univ. of Chicago Press, 2013. Pechenik, Jan A. A Short Guide to Writing about Biology. 9th ed., Pearson/Prentice Hall, 2015. Rodrigues, Velany. “Tips on Effective Use of Tables and Figures in Research Papers.” Editage Insights. Editage Insights, 4 Nov. 2013. Turbeck, Sheela P., et al. “Scientific Writing Made Easy: A Step-By-Step Guide to Undergraduate Writing in the Biological Sciences.” Bulletin of the Ecological Society of America, vol 97, no. 4, Oct 2016, pp. 417-426. EBSCOhost.
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Reconstruction of Atmospheric CO and the Stable Isotopes 8,000 Years and Comparison to Present-Day Abundance Presenter: Sophia Louise Misiakiewicz
Advisor: Dr. Alicia Mullaley
School of Science Summer 2018 Research Scholar
Analytical Methods
Introduction
Isotopic Composition
The isotopic composition of CO allows for us to determine sources for CO.
Atmospheric carbon monoxide (CO) is an important indicator of atmospheric composition due to its spatial variance and influences on atmospheric chemistry interactions.
The main sources of CO are methane oxidation, biomass burning, and non-methane hydrocarbons.
Methane derived Carbon Monoxide is calculated based on the [đ??śđ??śđ??śđ??ś) ]and models. The residual [CO] is determined from an Isotopic Mass Balance Analysis.
CO is a source for tropospheric ozone formation, which can lead to serious health issues. The ice cores were processed through a cryogenic vacuum extraction. This method allowed for the trace gas impurities such as water vapor, đ??śđ??śđ??śđ??ś, , and đ?&#x2018; đ?&#x2018; , đ?&#x2018;&#x201A;đ?&#x2018;&#x201A; to be condensed out of the sample at -200 degrees Celsius. The remaining CO is then oxidized to đ??śđ??śđ??śđ??ś, and processed through a MAT 253 (Mullaley, 2016).
Relative Sources of CO (minimum)
70
Source contribution (ppm)
This exploratory study calculated concentration and isotopic composition of carbon monoxide from South Pole ice cores from the last 8,000 years.
50 40 30 20 10 0
-10
Gas Age (years) Methane Derived CO ppb (modeled)
CarbonCarbon Monoxide Concentrations [CO] Monoxide Concentrations
[CO] Biomass Burning (minimum)
[CO] NMHC (minimum)
120
Prior to the extraction of CO, the core must be dated.
110
The South Pole has a low accumulation rates, coupled with low temperatures, which allows for ice to be compacted more slowly over time as compared to other sites.
100
Conclusions
90
Tracing back carbon monoxide concentrations and sources further into the past, allows a deeper understanding of the carbon cycle. This data demonstrates that carbon monoxide concentrations were consistent, up until the late Holocene. The sources of CO were varied over time. Due to Carbon Monoxideâ&#x20AC;&#x2122;s part in the oxidation of of đ??śđ??śđ??śđ??ś, and tropospheric đ?&#x2018;&#x201A;đ?&#x2018;&#x201A;2 , the exploration of itâ&#x20AC;&#x2122;s sources can open up new insight to the composition of the atmosphere.
80
[CO] (ppbv)
70
60
50
40
30
20
10
The ice is dated to be older than the gas, with a difference of approximately 1000 years.
60
-20
Results
Characterization of Sample
The layers that form as accumulation occurs have distinct characteristics. As the ice compacts, the gases within the Firn layer, and the atmosphere mix. It is not until the density of the ice reaches 0.80 đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;/đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?2 to 0.83 đ?&#x2018;&#x201D;đ?&#x2018;&#x201D;/đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?đ?&#x2018;?2 that gases are trapped within the ice.
Over the Last
-7000
-6000
South Pole (Wang et al., 2010)
D47 (Wang et al., 2010)
-5000
-4000
Vostok (Haan and Raynaud, 1998)
0
-3000 Law Dome (Ferretti et al., 2005)
-2000
Year (AD)
WAIS D (Mullaley, 2016)
References 0
-1000 D47 (Haan et al., 1996)
Assonov et al., 2007
1000 Modern CO Scott Base
South Pole (this study)
The carbon monoxide concentrations showed to be relatively stable throughout the past 5,000 years. More recent data collected illustrated a shift in atmospheric carbon monoxide, closer to present day.
2000
Mullaley, A.R. Reconstruction of Atmospheric [CO] and Stable CO Isotopes δ13C and δ18O Over the Last 250 Years (doctoral dissertation). Stony Brook University, 2016.
Development of Bio-Battery via Glucose Oxidase Affinity at Gold Nanowire Monique Ng and Bryan Wilkins Manhattan College
Introduction
Project Design
Conclusions
Energy has become a necessity to sustain our society and to further its advancement. The depletion of fossil fuels and the need for clean electricity production has called attention to biofuel cells which convert chemical energy into electrical energy by enzymatic reactions. This source of energy is sustainable, renewable, and does not emit CO2. Conventional fuel cells are generally cost-ineffective in regards to energy production.
Mutant variants of Aspergillus niger GOx were genetically engineered to design a biofuel cell that has increased activity and affinity to gold nanowire. Four GOx derivatives were engineered: GOx-Wt, GOx-cys, GOx-4mut (mutant), GOx-4mut-cys. The quadruple GOx mutant (GOx-mutant) was engineered for increased stability, efficiency and directly electron shuttling to anode.
Unfortunately, the expression and isolation of GOx encountered many hurdles due to technical errors and unexpected results, which diverged from the results Holland, et al paper reported. However, they did their expression in yeast, which is a eukaryotic system, the same as Aspergillus niger. Taking this into account, it is not a complete surprise that the protein was unstable in a prokaryotic system when overexpressed. The work of Witt, S. et al highlighted similar problems with GOx expression in E. coli, having to refold the insoluble protein from a denaturing solution. We tried several expression temperatures in E. coli, hoping that a reduced expression time might stabilize the protein, but we were unsuccessful. To that end, we managed to extract the protein from the insoluble fraction and refold GOx into an active enzyme. The protein is not in a pure state, and needs further isolation to obtain great characterization of the protein.
Glucose as a source for powering biofuel cells has held much promise. As a resource, glucose is energy dense, cost-efficient, and readily abundant. It also represents a clean source of power. The redox enzymes used to power biofuel cells are renewable and less expensive compared to the precious metal catalysts used in conventional fuel cells. In addition, these enzymes are optimized in neutral pH buffers, making them an attractive candidate to power ultralow power consuming implantable devices.
The mutations in the 4mut variants have been shown to increase stability and catalytic activity of the enzyme, however these variants have yet to be tested in a biofuel assay. Each coding sequence was codon optimized for expression in E. coli. The above gene cassettes were cloned into an expression vector under control of the T7lac promoter and then isolated under native conditions. The main goal of the project was to express and isolate each of the Gox variants to investigate how the cys-tagged and mutant versions of the enzyme might facilitate greater cell potential and effectiveness of a gold nanowire anode.
Test Expressions of GOx
Glucose oxidase (GOx) is one of the most well studied enzymes for use in biofuel cells because they catalyze an oxidation reaction that generates electrons at the anode. GOx is a flavoprotein oxoreductase that oxidizes glucose by using oxygen as the electron acceptor to produce glucono-βlactone (glucanolactone) and hydrogen peroxide.
Results & Discussion
~70 kDa
Protein of interest, GOx, is roughly 65 kDa. Following coomassie staining, a band for GOx can be seen progressively getting darker towards 6h. Overnight expressions yield a lower production level (data not shown). It is presumed that the cell is producing peak amounts of GOx 6h.
We aimed to create a more efficient bioanode through improved GOx activity, utilizing mutant variants of the Aspergillus niger GOx that were reported to have increased stability and activity. Additionally we designed the expression of our protein to genetically introduce a cysteine tag to the GOx variants as a way to sequester them to a gold anode. It is well established that thiols have high affinity for gold and we believe that the cysteine thiol tags will sequester the GOx to the anode, improving electron transfer to the anode.
GOx refolding
Insoluble GOx was refolded in the presence of FAD and allowed to refold for a week at 10 C. Refolded protein was concentrated and visualized by SDS-PAGE coomassie staining and western blotting against the 6 x HIS tagged of the protein.
GOx Solubility
GOx migrated to the insoluble fraction under all conditions that we tried (different buffers, temps, etc.). Literature revealed that other groups had the same problem and we followed an extraction/refolding protocol that allowed us to successfully isolate functional GOx. We washed the insoluble fraction with 2M urea and then extracted GOx from the fraction using 8M urea. This was optimized at higher temperatures of extraction.
GOx enzymatic assay Amplex Red Assay Overview of Molecular Probes Amplex® Red Glucose/Glucose Oxidase Assay Kit (A22189) which allows for a one-step detection of glucose oxidase activity by coupling the production of H2O2 to the activity of horseradish peroxidase (HRP). H2O2 reacts with Amplex® Red (a colorless molecule), in the presence of HRP, to yield resorufin (red-fluorescent product). This colorimetric assay directly couples the activity of glucose oxidase to the production of resorufin. This kit was used according to the Amplex Red manufacturer’s protocol.
References • Holland, T.J., Harper, J.C., Dolan, P.L., Manginell, M.M., Arango, D.C., Rawlings, J.A., Apblett, C.A., and Brozik, S. Rational redesign of glucose oxidase for improved catalytic function and stability. PLoS ONE, 2012, vol. 7 (6), pp. 37924-10. • Witt, S., Mahavir, S., and Kalisz, H.K. Structural and kinetic properties of nonglycosylated recombinant Penicillium amagasakiense glucose oxidase expressed in Escherichia coli. Applied and Environmental Microbiology, 1998, vol. 64 (4), pp. 1405-1411. • Cosnier, S., Gross, A. J., Le Goff, A. and Holzinger, M. Recent advances on enzymatic glucose/oxygen and hydrogen/oxygen biofuel cells: Achievements and limitations. Journal of Power Sources, 2016, vol 325, pp. 252–263. • Holland, J. T., Lau, C., Brozik, S., Atanassov, P. and Banta, S. Engineering of glucose oxidase for direct electron transfer via site-specific gold nanoparticle conjugation. J. Am. Chem. Soc., 2011, vol 133, pp. 19262–19265.
While, most of our effort was placed on expressing and isolating the wild type version of the protein, we now have a protocol established to move forward with the other variants. These protein expressions have already been performed at large scale (4 L each) and are ready to be isolated.
While we believe we have successfully expressed and refolded the GOx enzyme from an E. coli expression we must now address purity and kinetic activity of the enzyme. The enzyme, once characterized, will be delivered to Dr. Santulli’s group and they will test its affinity and activity at a gold nanowire anode. Dr. Santulli’s group is synthesizing the gold anode.
Future Direction We are directly interested in the following future steps: 1. Isolating GOx to a higher purity. We will pass our extract, refolded samples over metal affinity columns and potentially use ion exchange, and/or size exclusion chromatography to obtain pure sample. 2. Characterize the enzymatic activity of each mutant using the Amplex Red System. Using a set of standards we can assess activity rates. 3. Clone the GOx gene into a yeast expression vector. We may be able to bypass the extraction and refolding of GOx if the protein is expressed in a eukaryotic system. 4. Study the binding of the protein to gold and assess its effectiveness at the anode of a biological fuel cell.
Acknowledgment Acknowledgments
I would like to express my gratitude to Dr. Bryan Wilkins for mentoring me throughout this research. Thank you to School of Sciences Dean ,Constantine Theodosiou, for financial support, the Department of Chemistry and Biochemistry, for resources and equipment, and Dr. Rani for her continued effort and support, especially with summer housing arrangements during the research stay.
INTELLIGENT EDGE DETECTION USING A MLMVN JOSH PERSAUD | ADVISOR - IGOR AIZENBERG
Manhattan College - Department of Computer Science - School of Science Research Scholar
Introduction Edge detection in images is important for image segmentation and distinguishing image details. While there are many methods of edge detection, which work for clean (noise-free) images, there is a lack of methods, which should be suitable for noisy images. All devices which record real world data, are susceptible to noise. This includes both analog and digital devices (cameras, microphones, radar sensors etc.). This noise is usually caused by electrons randomly straying from its intended path and other uncontrollable factors such as heat. Gaussian noise can be thought of as noise that has static. Gaussian noise (created from poor lighting, temperature etc.) in images can be reduced using various filters. Removing Gaussian noise properly is much more difficult than removing impulsive noise. The problem with using any spatial domain filter to reduce the noise in an image is that, while we reduce the noise, we simultaneously smooth out the image causing blurring of the imageâ&#x20AC;&#x2122;s edges and details. The objects in a heavily noisy image will not have sharp edges and will lose important details. As a result of that, any classical edge detection algorithm applied after noise filtering usually fails to detect many edges, especially the ones related to small details, which can be smoothed and distorted by a filter.
Objective In this research project, the goal is to detect edges in noisy images while keeping as much image data as possible and ignoring noise. This will be done using a Multilayer Neural Network with Multi-Valued Neurons (MLMVN). We will create all software simulators needed for this work in the MATLAB environment. Images that are corrupted by Gaussian noise will be used in the experiment. The MLMVN will learn to detect edges in clean images and noisy images while ignoring a noisy texture. Our goal is to detect useful edges in noisy images, not detecting edges caused by noise and not smoothing an image by a filter before edge detection.
Results
Methods Clean Images In order to test if the MLMVN can preform Edge Detection on noisy images, we must first test if the MLMVN can preform Edge Detection on clean images. 1. Create a learning sample from clean images and images where the edges were detected. We used the Sobel operator to detect the edges of the clean images first before using them as an output for the MLMVN. The learning sample was created from 400 images. We took 100 samples from each image to create a total of 40,000 samples for the learning set. 2. Train the network using the learning matrix from step 1. We used various patch sizes with different RMSE deviations to find the best results. 3. Once training of the network was complete, we used 10 clean images to test the performance of the network. The 10 test images used in our test were not included in the learning process so It would be the first time the network would see the images. Gaussian Corrupted Images After testing clean images, we moved on to images corrupted with artificial Gaussian noise. The images used for this test were the same as the images used in the clean image test, except these had noise added to them. 1. Create a learning sample from Gaussian corrupted images and images where the edges were detected (detected from clean images). Again, the Sobel operator was used to detect the edges of the clean images, which was then used as our output for the MLMVN. The learning sample was created from 400 images. We took 100 samples from each image to create a total of 40,000 samples for the learning set. 2. Again, we trained the network using the learning matrix created in step 1. We used different RMSE deviations and patch sizes to attempt to get the best result. 3. Once we trained the network, we used the same 10 test images used in the Clean Image test. The only difference was that the 10 test images used were corrupted by Gaussian noise.
Clean Images
Figure 1a. Original image (wasnâ&#x20AC;&#x2122;t used in learning set)
Figure 2a. Original image (wasnâ&#x20AC;&#x2122;t used in learning set)
Figure 1b. Edge detected image using Sobel operator.
Figure 2b. Edge detected image using Sobel operator.
Gaussian Corrupted Images
Figure 3a. Original image (wasnâ&#x20AC;&#x2122;t used in learning set) corrupted by Gaussian noise with standard deviation of đ?&#x153;&#x17D;đ?&#x153;&#x17D;"#$%& = 0.2đ?&#x153;&#x17D;đ?&#x153;&#x17D;
Figure 4a. Original image (wasnâ&#x20AC;&#x2122;t used in learning set) corrupted by Gaussian noise with standard deviation of đ?&#x153;&#x17D;đ?&#x153;&#x17D;"#$%& = 0.2đ?&#x153;&#x17D;đ?&#x153;&#x17D;
Conclusion
Figure 1b. Edge detected image using Sobel operator.
Figure 2b. Edge detected image using Sobel operator.
Figure 1c. Edge detected image using MLMVN with 2048 hidden neurons. PSNR = 12.55, RMSE = 60.08
Figure 2c. Edge detected image using MLMVN with 2048 hidden neurons. PSNR = 12.18, RMSE = 62.73
Figure 3c. Edge detected image using MLMVN with 2048 hidden neurons. PSNR = 13.81, RMSE = 51.98
Figure 4c. Edge detected image using MLMVN with 2048 hidden neurons. PSNR = 11.11, RMSE = 70.89
The MLMVN can be successfully used to both, detect the edges of clean images, and as an intelligent filter while preforming edge detection on noisy images. In our clean image edge detection trials, the MLMVN was not only able to detect the edges in the image accurately, it was also able to perform image segmentation where it detected edges of similar textures in the image and distinguished them along with the edges. The normal Sobel operator was unable to preform image segmentation. This makes the MLMVN a better option when being used for edge detection. In our noisy image edge detection trials, the MLMVN was able to successfully detect the edges in the image and perform decent noise reduction. It is possible with the use of more learning samples that the MLMVN will be able to better reduce noise while performing edge detection.
Enhancing Enzymatic Fuel Cells with Nanotechnology Presenter: Seth Serrano – Advisor: Dr. Alexander Santulli Manhattan College Summer Research Scholars
Introduction
Conclusion
Enzymatic fuel cells are a good alternative energy source because they are ecofriendly. Unfortunately, however, the current densities they produce are relatively small.
The next steps for this project include making arrays with the nanowires and using a more accurate method to test if the amino acids adhered to the metals. Also, the development of genetically modified glucose oxidase that has a greater affinity for the amino acids or the metals would assist in the progression of this project.
Acknowledgments Fig 1. Shown above is a general representation of how a typical enzymatic fuel cell works. It has been established that increased current densities can be produced from electrodes with greater surface area, so we worked towards modifying the anode chamber electrode to have nanowires
Objective To increase the current density of enzymatic fuel cells by increasing the surface area of the anode using nanowires.
Methods
Fig 2. Above are three U-tubes. In the membrane Ni is precipitating.
Results
The nickel and gold nanowires were successfully made using the U-tube technique. Unmodified glucose oxidase did not adhere to the metals and, after several experiments, it became apparent that IR could reveal the presence of the amino acids it was not the most effective tool to determine if the amino acids had adhered to the nanowires. (a)
(d)
(b)
(e)
A special thanks to Dr. Wilkins and his team for their assistance and insight with the glucose oxidase. We would also like to express our gratitude to the Dean of Science, Dr. Theodosiou.
Sources
(c)
Kumar, R.; Singh, L.; Zularisam, A. W.; Hai, F. I. Microbial Fuel Cell Is Emerging as a Versatile Technology: A Review on Its Possible Applications, Challenges and Strategies to Improve the Performances. Int. J. Energy Res. 2018, 42 (2), 369–394.
(f)
Sakimoto, K. K.; Liu, C.; Lim, J.; Yang, P. Salt-Induced SelfAssembly of Bacteria on Nanowire Arrays. Nano Lett. 2014, 14 (9), 5471–5476.
Nickel and gold nanowires were created using the U-tube method and assessed using Scanning Electron Microscopy (g) (SEM). Once their purity and shape were confirmed, they were tested to see if glucose oxidase would adhere to them. We also tested if certain amino acids (histidine for nickel and cysteine for gold) would adhere to the nanowires since glucose oxidase can be modified to increase its affinity to Fig 3. Gold and nickel nanowires made in polycarbonate these amino acid. Infrared Spectroscopy (IR) was used to membranes with pores of different diameter using the U-tube determine if the glucose or the amino acids adhered to the method. (a) 100 nm gold nanowires (b) 15 nm gold nanowires metals (c) 50 nm gold wires (d) 200 nm gold wires (e) 50 nm nickel wires (f) 15 nm nickel wires (g) 200 nm nickel wires.
Koenigsmann, C.; Santulli, A. C.; Sutter, E.; Wong, S. S. Ambient Surfactantless Synthesis, Growth Mechanism, and Size-Dependent Electrocatalytic Behavior of High-Quality, Single Crystalline Palladium Nanowires. ACS Nano 2011, 5 (9), 7471–7487. Pharmatutor.org. (2018). RECENT ADVANCES OF LACCASE ENZYME IN INDUSTRIAL BIOTECHNOLOGY : A REVIEW | Page 5 | PharmaTutor. [online] Available at: https://www.pharmatutor.org/articles/advances-laccaseenzyme-industrial-biotechnology-review?page=4
Methylammonium Lead Iodide (MALI) Nanowires for Solar Cells Francisca Villar, Chemistry Department What are Nanowires?
Figure 1: Ideal image of Methylammonium Lead Iodide nanowires
Nanowires are thin nanometer sized material that transport electricity. These wires are produced from metals and are semiconductors. Due to their low cost and one dimensional structure, scientist have been studying these wires in hopes to make them efficient enough to be used in electronics.
Background Efficiently creating and preserving energy has been a challenge for scientists and engineers for several years. Even to this day, the most common way energy is produced is by the burning of fossil fuels. Solar panels, however, are an innovative alternative that helps convert sunlight into useful electricity to power daily necessities. One huge issue with this innovation is that only about 20% of the absorbed energy is being used as electricity. The environmental and functional limitations of solar panels have lead scientists to make new discoveries using nanotechnology to transport energy more efficiently within solar cells.
Method/ Technique The method that was taken when conducting this research was to test different concentrations and pore sizes for the cell membrane in a U- tube. As for the solar cell, depositing the layers on to the glass in an even film was the biggest challenge. Different speeds on the spin coater were tested in order to find the speed that formed an even thin layer onto Figure 2: Image of the glass plate. 200 nm cell â&#x2014;? 50 nm, 100 nm , and 200 nm sized membrane in Utube. Cell membranes were tested â&#x2014;? 0.15 M, 0.2 M, 0.25 M and 0.4 M concentrations of Methylammonium Iodide â&#x2014;? Solar cell speeds were tested using a Variac as well as the spin coater itself for different lengths of time in order to get the most even film onto the glass template.
Bad Nanowires vs Good Nanowires
Figure 3: Good and bad wires
Perovskite Solar Cell
Purpose The purpose of this research was to create nanowires that would allow solar panels to absorb and retain much more energy to power daily needs. Nanomaterial is the next best alternative to powering solar cells for its low cost compared to burning fossil fuels, simple process of growing in cell membranes, and a lot more pleasant for the environment. For these reasons my research was conducted on Methylammonium Lead Iodide wires.
The left side image has two rows the top being the one that displays ideal rod shaped nanowires. The bottom row displays nanomaterial that did not morph into wires properly. The right side image displays the successful wires that were Figure 4: SEM of my formed when conducting my MALI wires research.
Figure 5: Perovskite solar cell
In my research a Perovskite Solar cell was also developed. This device consists of several layers including TiO2 layer, MALI layer and a Gold layer. The glass plate was already coated with a TiO2 layer which helps separating electrons. Two more layers of Lead Iodide in Dimethyl Fluoride and Chlorobenzene were spin coated onto the glass plate. Several templates were tested at different speeds and longevities until an ideal speed and time was determined.
References http://news.mit.edu/2013/explained-nanowires-and-nanotubes-0411 http://www.hwnanomaterial.com/copper-nanowires-pvp-coating-used-for-new-solarcells_p176.html
Results/ Discussion Just like most research the best results come from trial and error. From changing the pore sizes of the cell membranes, to different concentrations of the chemicals used, and even finding how the solar cells were spun and coated were all altered in hopes to get better results.After several tests the ideal time for the cell membrane to morph in the U-tube was 2 hours and then washed to remove bulk. In the Figure 6: MALI in conversion process of Lead Iodide to MALI, I Methylene Chloride determined that I got the best results when the cell membranes sat in the solution of Methyl ammonium Iodide for 24 hours. As for the solar cells the Variac machine ended up being of very little. The spin coater spun at a constant enough speed that was manageable and allowed for the glass plate to get an even film. The ideal time to spin the cell was 20 seconds.
Conclusion After several weeks of developing this research I found that in the case of the cell membrane, the pore size that worked most efficiently in growing the MALI wires was the 200 nm polycarbonate cell membrane. The ideal time for the cell membrane to morph in the two solutions of 0.05 M Lead Nitrate and 0.1 M Potassium Iodide was 2 hours. In the conversion from Lead Iodide to Methylammonium Lead Iodide, the cell membrane had to rest in a Methylammonium Iodide solution with Isopropyl Alcohol for 24 hours. The conversion was very obvious since lead Iodide is a bright yellow and when placed into the MAI solution it became a reddish brown which are the wires.The cell membranes were washed with Methylene Chloride and ran under a nitrogen apparatus to avoid any form of moisture destroying my the wires. The wires were ran through Xray and Figure 4 displays successful results. After making the nanowires the Perovskite solar cell device was tested using a light beam and a current was found positive using a digital voltmeter.
Acknowledgments Huge thanks to Dr. Alexander Santulli for the opportunity to collaborate with him on this research project. As well as the Department of Chemistry allowing students like myself to conduct research on upcoming and new innovations that will help students succeed in their future careers.
Creating Lead-Free Perovskite Materials for a Cleaner, Greener Future Amanda Zimnoch and Dr. Alexander Santulli Department of Chemistry and Biochemistry, Manhattan College
Introduction Perovskite materials have the general formula ABX3, where A and B are cations, and X is an anion. They are arranged as if eight octahedra of BX6 surround one central A atom on the corners of a cube (see image). Organic-inorganic hybrid perovskite materials can be used to convert solar energy into electrical energy. Over the past few years the improvement in the performance of perovskite solar cells has been rapid, rising from 9% to over 20%. These materials have high optical absorption properties combined with balanced charge transport properties and long carrier diffusion lengths, making them excellent candidates for photovoltaic devices. Perovskite materials show a lot of promise for becoming the new standard in solar cells. First generation solar cells used silicon A perovskite structure crystals, which are expensive but effective. Second generation solar cells used amorphous silicon, which is thinner and cheaper, but less effective. Third generation solar cells include organic, dye-sensitized, polymer, copper tin zinc sulfide, nanocrystals, micromorphs, quantum dots, and perovskite solar devices.4 Perovskite materials fall into the third generation of solar cells. Compared to traditional silicon solar cells, perovskite cells are simpler and cheaper to manufacture. Silicon solar cells demand costly, multistep processes that are performed at high temperatures (over 1000° Celsius) in a highly evacuated chamber, whereas the organic-inorganic perovskite substance is fabricated by simple wet chemistry methods in a non-evacuated ambient surrounding.4 Perovskite materials also exhibit a high optical absorptivity, meaning they can be made thinner (500 nanometers) as opposed to the typical silicon solar cell (2 micrometers).4 The material that has been the major focus in perovskite research is methylammonium lead iodide (MALI). In this material, the two cations are lead (II) and the organic ion How solar cells produce electricity
methylammonium. While this compound has shown efficiencies exceeding 20% there are several issues that must be addressed to make these materials better.1 Two of these issues are the requirement of lead in the material, and its degradation in moist environments.2 Our research project focused on removing the lead (II) from the perovskite material to mitigate these effects. Several reports have shown that there is a good number of variations that can be made with the perovskite material to fine tune the properties of the material.3 We focused on cobalt replacing lead because previous studies had found that among all transition metals, cobalt showed the most promise and highest efficiency3. To characterize the materials synthesized, we measured the optical, electronic, and structural properties. To explore the structural component of the material, we used a D2 Phaser powder X-Ray How x-ray diffraction works diffractometer. X-ray diffraction is a common technique used to study crystal structures and atomic spacing. It is based on constructive interference of X-rays and a crystalline sample.5 These X-rays are generated by a cathode ray tube and directed toward the sample. The interaction of the incident rays with the sample produces constructive interference (and a diffracted ray) when conditions satisfy Bragg's Law (nλ=2d sin θ).5 This law relates the wavelength of electromagnetic radiation to the diffraction angle and the lattice spacing in a crystalline sample. These diffracted X-rays are then detected, processed and counted. By scanning the sample through a range of 2θangles, all possible diffraction directions of the lattice should be attained due to the random orientation of the powdered material.5 Conversion of the diffraction peaks to d-spacings allows identification of the mineral because each mineral has a set of unique d-spacings. This is typically achieved by comparing the d-spacings with standard reference patterns.5
Atom copper cobalt nickel iodide lead cesium methylammonium (CH6N+)
Size 135 pm 135 pm 135 pm 140 pm 180 pm 260 pm Large
Methods
Results
Perovskite Attempted A (large cation)
B X3 (small (anion) cation)
cesium
copper
iodide
cesium
lead
iodide
cobalt
lead
iodide
copper
lead
iodide
methylammonium
copper
Synthesis 1) One part of 0.1 M cesium acetate in H2O and one part of 1 M copper acetate were mixed. The solution was then added to two parts of 1 M potassium iodide. 2) Copper iodide solid was heated in boiling 0.1 M cesium acetate in H2O. 3) A 100nm template of copper iodide nanowires was prepared in a U-tube using 0.5 M potassium iodide and 0.5 M copper acetate. The template was then submerged in 1 M cesium acetate in H2O and heated. 4) Solid copper iodide and solid cesium acetate were ground together using a mortar and pestle and the product was heated. A 100nm template of lead iodide nanowires was prepared in a U-tube using lead acetate and potassium iodide. The template was then submerged in cesium acetate in isopropyl alcohol. One part of 0.1 M lead nitrate and one part 1 M cobalt nitrate were mixed. The solution was then added to two parts of 0.1 M potassium iodide. One part of 1 M copper acetate was mixed with a few drops of 0.1 M lead nitrate. The solution was then added to one part of 0.1 M potassium iodide.
iodide Solid copper iodide was heated in boiling methylammonium iodide.
methylammonium
lead
methylammonium
leadcobalt
iodide
methylammonium
nickel
iodide
methylammonium
cesiumlead
iodide
methylammonium
cobalt
iodide
nickel
lead
iodide
iodide
0.05 M lead nitrate in H2O, acidified with a few drops of concentrated acetic acid, was mixed with 0.1 M potassium iodide in H2O to make lead iodide. Prepared in nanowire morphology by using a 100nm polystyrene template in a U-tube. The template was submerged in 0.1 M methylammonium iodide and then dissolved in methylene chloride to give methylammonium lead iodide nanowires. One part of 1 M cobalt acetate was mixed with a few drops of 0.1 M lead nitrate. The solution was then added to one part of 0.1 M potassium iodide. 0.88 M nickel iodide and 0.88 M methylammonium iodide were prepared in the same 5mL solution of DMF (dimethylformamide). Solids were filtered out and excess DMF was evaporated off of the product. A 100nm template of methylammonium lead iodide nanowires was prepared. The template was submerged in 0.1 M cesium acetate in isopropyl alcohol before being dissolved in methylene chloride. 0.44 M cobalt iodide and 0.88 M methylammonium iodide were prepared in the same 5mL solution of DMF. Solids were filtered out and excess DMF was evaporated off of the product at either 150°C or 250°C. One part of 1 M nickel nitrate was mixed with a few drops of 0.1 M lead nitrate. The solution was then added to one part of 0.1 M potassium iodide.
Left, a U-tube set up. A 100nm polystyrene template separates the two halves of the Utube. The yellow (left) side contains potassium iodide and the blue (right) side contains a mixture of cobalt acetate and cesium acetate. Right, a thin and flexible perovskite solar cell. Scanning Electron Microscope image of synthesized Copper Iodide nanowires
Conclusion & Future Research Many different perovskite materials were attempted. We performed x-ray powder diffraction on all the samples, but more analytical techniques such as scanning electron microscope images and UV/Vis spectra will be performed in the future to further identify the products. The syntheses of the samples will be repeated to test their reproducibility. When a product is found to be reproducible and have high absorption properties, we will fabricate thin film solar cells, via a spin coating technique, using the product as the light absorbing material. We will then measure the solar cell performance using a solar simulator at Fordham University in collaboration with Dr. Christopher Koenigsmann.
References (1) Manser, J. S.; Saidaminov, M. I.; Christians, J. A.; Bakr, O. M.; Kamat, P. V. Making and Breaking of Lead Halide Perovskites. Acc. Chem. Res.2016, 49 (2), 330–338. (2) Hailegnaw, B.; Kirmayer, S.; Edri, E.; Hodes, G.; Cahen, D. Rain on Methylammonium Lead Iodide Based Perovskites: Possible Environmental Effects of Perovskite Solar Cells. J. Phys. Chem. Lett.2015, 6 (9), 1543–1547. (3) Boix, P. P.; Agarwala, S.; Koh, T. M.; Mathews, N.; Mhaisalkar, S. G. Perovskite Solar Cells: Beyond Methylammonium Lead Iodide. J. Phys. Chem. Lett.2015, 6 (5), 898–907. (4) Ansari, M. I. H.; Qurashi, A.; Nazeeruddin, M. K. Frontiers, opportunities, and challenges in perovskite solar cells: A critical review. Journal of Photochemistry and Photobiology C: Photochemistry Reviews.2018, 35, 1-24. (5) Dutrow, B. L., & Clark, C. M. X-ray Powder Diffraction (XRD). 2018. Retrieved from https://serc.carleton.edu/research_education/geochemsheets/techniques/XRD.html
Acknowledgements I would like to acknowledge the Dean of the School of Science, Dr. Constantine Theodosiou, for providing funding for this research.