BiI₃ is a nontoxic and relatively abundant material with a bandgap of ~1.8eV that displays promising photovoltaic properties. However, BiI₃ solar cells have not reached efficiencies of greater than 1%. One of the major problems is the existence of deep defects in the materials which serve as trap states and increase non-radiative recombination. Here, defect engineering by both isoelectronic and non-isoelectronic doping of BiI₃ is studied to reduce the concentration of deep defects and increase the concentration of free charge carriers, aiming to improve the photovoltaic properties of BiI₃ solar cells. To evaluate the effect of dopants on defect passivation in BiI₃ films, photoluminescence and photoconductivity measurement are applied to determine quasi-fermi level splitting (QFLS) and carrier diffusion lengths, respectively. Additionally, dark photoconductivity measurements are used to determine carrier concentration. Preliminary results have shown an increase in QFLS under an iodine rich environment, though not significantly enough to drastically impact BiI₃ solar cell performance. There is much room for further exploration of dopants and their effect on BiI₃. With every dopant tested we learn more about the properties of BiI₃. This work will deepen our understanding of optoelectronic physics and defect chemistry of BiI₃ solar cell materials, optimize the quality of BiI₃ thin films, and increase the efficiency of BiI₃ solar cells.

As the global burden of neurological diseases continues to grow each year, there exists a need for drug delivery vehicles that can overcome barriers specific to the brain. The heterogeneous brain extracellular matrix (ECM) is an understudied barrier to effective therapeutic delivery, particularly in the presence of disease states. We utilize a novel multiple particle tracking (MPT) approach to characterize microstructural changes in perineuronal nets (PNNs), a key structural mediator of plasticity, in the developing brain. Our overarching goal is to develop a combined approach of MPT, statistical analysis using Python-based software packages, immunohistochemistry (IHC), and mRNA expression profiles to monitor changes in PNN structure and function through development and in the presence of neuroinflammation and oxidative stress processes. For this, we cultured 300 μm-thick organotypic whole hemisphere (OWH) brain slices prepared from postnatal day 35 (P35) rats. We treated slices with 100 ng/mL lipopolysaccharide (LPS, induced-inflammation model) or 100mM glutamate (MSG, induced-excitotoxicity model) for 3 h, removed the toxin and fixed slices at 1 h, 6 h, and 24 h after toxin removal. Fixed slices were stained using Wisteria floribunda agglutinin (WFA) and PNN counts were quantified using the ImageJ software package. In LPS and MSG-treated slices, 100-nm particles were added to live slices stained with WFA and MPT was performed in WFA+ regions, followed by statistical analysis of individual nanoparticle trajectories. From the IHC imaging and PNN count quantification, we observed differences in the number and morphology of PNNs present in the MSG and LPS models when compared to healthy controls. Characterizing structural changes in PNNs in living tissue furthers our understanding of the impact of neuroinflammation and oxidative stress on neuronal plasticity, and the subsequent impact on progression of neuropsychiatric diseases.

The most effective semiconductors used as absorber layers for solar cells have concerns regarding high capital expenditure (CapEx) for new manufacturing facilities, earth abundance, toxicity, or cost-volatility of the materials. Solution processing is a low cost, low temperature development method leading to lower CapEx. The exploration of new photovoltaic materials seeks to develop an earth abundant, non toxic semiconductor via solution processing with efficiencies comparable to materials like silicon or CdTe. Bismuth rudorffites (chemical formula A_aBi_bX_a+3b) are an interesting category of new materials, proven to be solution processable, to have high absorption, and to be capable of cell efficiencies over 4%. My project seeks to optimize the thin-film morphology and the open circuit voltage (V_oc) of bismuth rudorffite layers, both of which are crucial to achieving high efficiencies. A good morphology will be phase-pure and densely packed, with large grains. By determining the effects of each parameter of the thin-film deposition process (spin-coating, in our case) through Scanning-Electron Microscope imaging and X-Ray Diffractometry, I have determined an optimized deposition procedure leading to good morphology. The utilization of Absolute Intensity Photoluminescence techniques (AIPL) allows for prediction of the V_oc to a high degree of precision without building an entire solar cell, instead only measuring the absorber layer. By illuminating the absorber and detecting the re-emitted light, models can determine the density of "radiative recombinations" of electrons and holes, which correspond to electrons that would be capable of generating a voltage and providing electrical power. By using this method and by building an understanding of rudorffite crystal growth, I have attempted to reduce "non-radiative recombinations," increasing the PL and hence increasing the capacity for high V_oc in rudorffite cells. Here is presented current data, results, and recommended experiments necessary for rudorffites to be a successful photovoltaic material.

Developing treatments for complex disease is hindered by a variety of obstacles including clearance by the reticuloendothelial system, degradative proteases of blood and tissue microenvironments, and overall limited tissue penetration. Nanoparticles hold potential as drug delivery platforms for overcoming these problems, where they can be used to encapsulate and protect enzyme therapeutics and improve delivery to specific organs of the body. Using poly(lactic-co-glycolic acid) (PLGA) polymeric nanoparticles with a poly(ethylene glycol) (PEG) coating and catalase as a model enzyme, we compared two methods of nanoparticle formulations for enzyme encapsulation: nanoprecipitation and double emulsion. We tested the effects of F127 (Pluronic F127), P80 (polysorbate 80), and PVA (poly(vinyl alcohol)) surfactants on the size, polydispersity index (PdI), zeta potential, and enzymatic activity of nanoparticles formulated via nanoprecipitation. We determined PVA to be the ideal surfactant with the greatest enzyme loading for nanoprecipitation (~3.6%), similar to the activity obtained from double emulsion (~3.7%). For double emulsion, we determined similarly that PVA worked the best out of the surfactants tested. However, the nanoprecipitation method provided little to no enzyme protection in the presence of a protease with complete deactivation within 4 hours, while double emulsion nanoparticles extended activity through 24 hours (~6.6% of original activity). We further optimized the double emulsion method by altering the sonication times, determining that a sonication time of 15 seconds yielded particles with an activity of ~3.5% while maintaining similar size, zeta potential, and PdI. We also found that sonication conditions significantly affected enzyme deactivation. With a modified bicinchoninic acid assay, we measured total protein concentration within the particles, allowing us to calculate enzyme percent deactivation from formulation processes. Altogether, our work shows that enzyme-loaded nanoparticles made via the double emulsion method achieve high encapsulation and protection of enzymatic cargo for drug delivery.

Computer vision models are used to help analyze biomedical images for diagnosis and treatment through looking for differences between images by a comparison to a template image. For instance, optical coherence tomography (OCT) is used to diagnose and treat retinal issues. When looking at the brain, injury, cellular uptake and characteristic features vary across regions, therefore images are often segmented into established brain regions to determine how the brain is impacted in a particular study. Current models fail to work in segmenting brain regions because each brain has variation in local microstructure, making it difficult to compare one brain to another. Furthermore, when brains are sliced, the exact location within the brain can be difficult to pinpoint, particularly in regard to depth, because the regions vary slice to slice. Therefore, my research addresses the increasing need for a method of analysis to align and compare images from brain regions across slices from a single brain, and from brain to brain. Using scikit-image analysis tools, I extracted information from cell images and videos of nanoparticles obtained in brain slices and determined trends within various regions. My program extracted cell density, shape, and death, then analyzed the uptake of nanoparticles to determine where a small segment of an image is most likely located within the brain. Iterating over the entire image generated a rough map of the regions within the brain which is refined using mapping descriptions detailed in literature. This research resulted in a systematic program that uses image analysis tools to extract features of defined brain regions. This program allows for quick, accurate and consistent analysis of regional differences of cellular features, nanoparticle distribution, toxicity, and other important measures.

Biological mineralization is the formation of minerals in hard tissues guided by proteins. Unique aspects of these minerals include the molecular control of hierarchical structure, intricate architectures, and multifunctional properties for inspiration in bionanotechnology and nanomedicine applications. Numerous biomineralization strategies have been developed in hard tissue regeneration therapies. However, there is currently no in-depth understanding of how proteins regulate the synthesis of these inorganics or the physiological formation of the minerals. The ability to control mineral formation for biomedical applications, therefore, is still limited to the use of a few mineral-directing proteins extracted from tissues. Biomineralization can also be controlled using short peptide domains derived from natural proteins known to have a regulatory role in mineralization. Our laboratory has designed peptides derived from amelogenin (ADPs), the key protein in tooth formation, using combinatorial selection and computational design, whose utility in rebuilding hydroxyapatite (HAp) mineral on tooth has been demonstrated in numerous case studies. The goal here is to understand the fundamental mechanisms of biomineralization guided by ADP5 and develop a methodology to form HAp with exclusive control of its growth kinetics and mineral crystallography. We designed mutants of ADP5 to investigate changes in mineralization kinetics, nucleation, and morphology. In the current study, we are establishing the conditions for ion-peptide interactions on the onset pH for mineral nucleation using calcium/phosphate and mutant ADPs. The goal is to gain insights into the correlation between sequence domains and biomineralization outcomes eventually facilitating greater control over the reaction and further optimize remineralization approach. The developed method has a high potential to develop non-invasive oral health care materials and methods by restoring mineral loss, the root cause of dental ailments and, eventually, help bring clinical and over-the-counter dental products into the market with preventive, restorative, therapeutic, and cosmetic characteristics. Sponsored by SoD Spencer Funds.

Electric propulsion is becoming an increasingly important field due to the rise of microsatellites in education, research, and industry. Because chemical propulsion is impractical for these small-scale satellites, the need for an efficient, long-term electric propulsion solution has become apparent. In response to this, the Dielectric Barrier Discharge (DBD) Thruster was developed. The DBD is a novel electric propulsion system that uses a low power input to ionize Argon gas and accelerate ions to produce thrust. For initial characterization, thrust values and plasma characterization parameters were collected in the far field of the plasma using electric propulsion diagnosics. The experiment was repeated for multiple operational spaces, and data was collected, compiled, and analyzed. Preliminary results indicate thrust values and plasma characteristics comparable to those of other propulsion systems at similar electrical power levels. Further testing with different high voltage parameters as well as implementing additional diagnostic equipment will help fully characterize the DBD system and assess its potential usefulness in a satellite architecture. To increase the versatility of the DBD system, research will be done on developing multi-DBD arrays and incorporating a nanoparticle injection system for demonstrating future space resource utilization.

Omega-3 fatty acids provide numerous health related benefits, and are commercially available in the form of supplements. However, these fatty acids have the tendency to oxidize over time and can have potentially harmful effects on the human body. Additionally, neutrophil oxidation of fatty acids in vitro has been shown to be a marker for oxidative stress. Oils oxidized with a biologically relevant concentration of sodium hypochlorite were used to simulate the behavior of neutrophils in the human body and these results were analyzed and compared to control samples. In order to characterize the oxidation byproducts of common fatty acids, the oxidation products and control samples were treated with acidified methanol in toluene to perform a fatty acid methyl esterification (FAMEs). This facilitated the analysis by gas chromatography and mass spectrometry (GC-MS) and provided proof-of-concept for the fatty acid analysis method. This methodology was applied to 0.29 mmols of each sample of oil oxidized with .05 mMols of sodium hypochlorite to evaluate the products of this reaction to simulate the behavior of neutrophils in the human body. GC-MS data for the composition of the FAMEs and oxidation derivatives detected will be presented as well as their relative amounts.

Organic solar cells (OSCs), particularly all-polymer solar cells, have risen as an exciting alternative to standard inorganic solar cells. Their low-cost synthesis, easily tunable properties, and solution-processable fabrication enable facile scale-up for high-throughput production. For commercial viability OSCs will need to have power conversion efficiency (PCE), which is determined by the material characteristics and energetic properties, comparable to their inorganic counterparts (>20%). The question this research aimed to address is: How do processing conditions govern the electronic properties of the photovoltaic layer? Two primary methods were used to evaluate the energy levels: Cyclic Voltammetry (CV) for the highest and lowest occupied molecular orbitals (HOMO/LUMO) and Ultraviolet-Visible Spectroscopy (UV-Vis) for the optical bandgap. A binary blend comprised of a polymer donor, known as PSEHTT, and a polymer acceptor, N2200, was used as a proxy model. Blends of compositions ranging from 0 to 100 weight % PSEHTT were coated on platinum wires for CV and glass substrates for UV-Vis measurements. The collected data behaved as expected, exhibiting a decreasing trend in HOMO level from -5.29 eV to -4.94 eV as the weight % PSEHTT increased. The evolution of the LUMO level with blend compositions was rather challenging to obtain due to possible photoexcitation in the blend leading to free charge available for continuous current extraction. To verify, the samples were isolated from light over the course of the CV measurements to prevent interference. This should result in more accurate LUMO level approximations which are expected to display a similar trend to the HOMO level. Elucidating the relationship between blend composition and blend electronic properties enables a precise and facile device optimization process for highly efficient OSCs.

Solar steam generation has received considerable attention as one of the most promising solar energy harvesting technologies for applications in water desalination, sterilization, distillation, and power generation. Recent research suggests that plasmonic metal nanocrystals can be used as efficient light-to-heat transducers in solar-to-steam applications due to their strong localized surface plasmon resonances; however, large-scale implementation of solar stream generation based on plasmonic metal nanostructures is restricted due to their high cost, structural stability, and low recycling rates. I propose to develop earth-abundant copper iron sulfide nanocrystals as alternative photothermal transducers and apply them in solar steam generation. These nanocrystals can be engineered for efficient light-to-heat conversion and strong, broadband solar absorption. Moreover, they not only consist of earth abundant elements but also have high recycling rates. I plan to work towards enabling large-scale solar stream generation for the sustainable development of our society.