Despite widespread implementation of solar energy, there are still issues with efficiency in varying conditions. For example, photovoltaic (PV) arrays function optimally at a specific temperature and have decreasing efficiencies at higher temperatures. Other factors that also impact the power production of a PV array include the amount of direct sunlight, the distribution of incident light, and the intensity of incident light. As solar energy installations continue to increase worldwide, proper modeling of PV arrays is critical for potential asset owners and power system operators. Effective simulations of a PV array’s power production require models that effectively consider the uncertain external factors that vary by geographical region and climate. In this research project, a realistic PV cell model is developed in the programming language Python. This model explores the sensitivity of a PV cell’s power production to different external variables, including ambient temperature, solar irradiance, and other weather conditions. Additionally, this PV cell model is extensible, allowing power production from PV modules and arrays to easily be considered. This model can be seamlessly integrated with other energy asset models, including those for energy storage and flexible demand resources, allowing for complex scenarios to be simulated. To display this functionality, a cost-minimizing consumer with a behind-the-meter PV array and battery is simulated.

The purpose of this study is to provide an overview of supply chain challenges and proposed vaccination programs for COVID-19 pandemic. The goal is to provide a broad framework of COVID-19 impacts from both the economic and health perspectives. In the economic perspective, the study includes an overview of: 1) PPE and essentials pricing under demand and supply uncertainties; 2) current supply chain challenges from the economic and manufacturing standpoints. In the health prospective, I propose a vaccine allocation strategy and a vaccination program for COVID-19 pandemic, and present a vaccination scheduling system that may help vaccinating the population.Supply chain challenges during the COVID-19 pandemic have resulted in negative economic and health impacts. As the number of infections increases, so does the demand for essential items, such as hospital equipment, personal protective equipment (PPE), hand sanitizers and toilet paper. The supply can no longer keep up with needs. Without a stable and secure supply chain, the prices of essentials have surged. Hospitals and urgent care facilities are facing financial and healthcare challenges. The supply chain issues, the inadequacies of essential resources, and the lack of capacity to meet increasing healthcare demands were exposed by COVID-19 and have prompted us to review our current strategy in fighting and preparing for a pandemic.I proposed a vaccine allocation strategy based on theories and previous H1N1 vaccination experience that may help mitigating the infection rate of COVID-19 pandemic, and also proposed a vaccine scheduling system that may help vaccinating the population with the goal of having low leftovers and wasting vaccines.Both vaccine allocation strategy and vaccine scheduling system are proposed based on theories, and will need to be further examined by running an agent-based simulation model. 

The study of cell morphology is important and prevalent in understanding normal and pathological conditions in the brain. Brain cells are common targets for treatment for brain diseases. Specifically, microglia – the brain’s resident immune cells – undergo a range of morphological changes in response to injury and are targets of many mitigating treatments. Image processing has been a valuable tool to assess microglial cell morphology via the analysis of microglial shape features and there continue to be additional opportunities for further investigation. Prior research has indicated a connection between features such as solidity and extent, two shape features that measure the ratio of cell areas. In this study, we examine three shape features of fluorescently labeled microglia: Euler number, extent, and solidity, in the context of ischemic injury. Ischemic injury was modeled using oxygen-glucose deprivation (OGD) in cultured whole hemisphere brain slices. Using python, images were thresholded with the Otsu threshold. Shape features were extracted from the binarized images. These shape features were analyzed based on brain region (cortex, hippocampus, thalamus), generalized treatment type (non-treated, injured, injured with treatment), and specific treatment type (OGD 0.5 hour, 1.5 hours, 3 hours, 1.5 hours with azithromycin treatment, 3 hours with superoxide dismutase treatment) and visualized using seaborn. The results verified trends in effects of injury and recovery after treatment on extent and solidity. Both findings support the expected shift from a circular shape of microglia in the injured state to more branched in the healthier state. The Otsu thresholding is limited in its accuracy, and, hence, these results provide an opportunity to optimize cell segmentation protocol for higher quality thresholded images. The results of this work have the potential to be applied to various forms of injury and cell types.

Wireless power through magnetic resonance between coils of wire has enabled a new charging paradigm in a variety of domains, from robotics to biomedical implants. As wireless power systems move from simplistic to more perfomant architectures comprising of many coils, the design complexity scales very quickly. This is due to the difficulty in simulating and modeling the magnetic fields that form the backbone of the wireless power transfer, as in the multi-coil case the computational complexity quickly exceeds the capacity of even high end servers. To enable the development of next generation wireless power devices, we developed the Mostly Printed Field Characterization System (MPFCS), a robotic scanner that collects high-fidelity, high-resolution magnetic field data. However, while the system creates useful visualizations for wireless power, it does not provide a mathematical model that would allow for the precise optimization and rigorous understanding of the fields that engineers often need. Addressing that, we present physics-driven machine learning methods that combine electromagnetic theory with data collected from the MPFCS to build simplified mathematical models for these magnetic fields. We provide, for the first time, a characterization of fields for systems that were previously too complex to analyze effectively by hand or through computation. Preliminary evaluation of the data shows that there is very little error compared to simulated values. Based on the algorithm's performance on similar problems, this suggests promising final results. This work provides a deeper understanding and design tool to build and iterate on next generation devices, leading to both accelerated prototyping and novel research directions. 

The self-assembly behavior of peptides on inorganic surfaces such as highly oriented pyrolytic graphite (HOPG) remains a highly studied field with numerous applications. Some of these applications include the integration of engineered devices for biomedical purposes. Overall, it is important to understand the types of interactions occurring at protein-surface interfaces, and which of these interactions govern the primary self-assembly behavior of the molecules. A number of experimental methods may be employed to better understand the behavior at these interfaces. Atomic force microscopy (AFM) is a well-established method, and was used to image self-assembly samples of charged peptides on graphite. Fixed concentrations of negatively charged peptides were placed in varying concentrations of KCl solutions to determine the effect of salt concentration on surface coverage and ordering of peptides on HOPG. Additionally, other factors were investigated, including the effect of step edges and incubation time on ordering. AFM images of the peptides were analyzed using Gwyddion to extract the surface coverage and degree of ordering. With increasing salt concentration, it was found that peptide assembly on graphite generally increased in KCl ranges of 1μM to 1mM. Additionally, the degree of peptide ordering increased with this range of salt concentration, displaying that charged peptides are able to pack closer together in the presence of an electrolyte. The electrostatic screening effect can explain this close-packing behavior, in which positively charged K+ ions are able to surround a negatively charged peptide and decrease the effective coulombic force between any two adjacent peptides. Overall, several factors may influence the assembly and ordering of peptides on HOPG, including electrolyte concentration, density of step-edges in the graphite, and charge of the peptide in question. This research has broader implications in the bio-medicial field with applications to engineered devices, and may prove important for future research.

Single amino acid conformational preferences on a substrate are invaluable to our understanding of how conformational propensities are dictated by a peptide’s sequence. The efficiency of understanding SAP/SLAM (self-assembling peptides on single layer atomic materials) interfaces is of high importance. An efficient way of performing computational modeling of a peptide’s free energy landscapes is needed to predict the folded structures on solid surfaces towards designing bio/nano interfaces, a key to bioelectronics and biosensor developments. Graphene, single-atomic layer graphite, is an ideal substrate for a dodecapeptide to bind and spontaneously self-organize to form ordered biomolecular structures on the surface. For the purpose of shortening computation times of peptides at graphene interfaces, sampling each amino acid’s free energy landscape in terms of the peptide’s natural torsional configuration, such as the phi and psi angles of the peptide backbone on graphene is studied. This is due to the fact that a predetermined natural starting point, such as the peptide’s lowest energy structure will allow for quicker convergence of the system and more accurate structure prediction. In an effort to retrieve this data, here we simulate each amino acid using the enhanced sampling computational technique Metadynamics. Using the computational modeling and random sampling of the lowest energy wells, we aim to aid in the determination of low energy preferences on conformational landscapes on graphene towards more predictive design of soft bio/nano interfaces for practical implementations.