Two-dimensional (2D) materials are layered materials made up of either a single or very few atomic layers. The properties they exhibit are fundamentally different from those of three-dimensional crystals, and they provide us with high levels of control to design novel systems in order to study new physical phenomena. 2D dielectrics are used in electrostatic gating to control the displacement field and carrier doping being applied to a 2D sample, making the properties and performance of 2D dielectrics very important. In 2D heterostructure devices, the standard dielectric used is hexagonal boron nitride (hBN). However, a recently published paper  claims that the novel 2D dielectric Bi₂SeO₅ is readily exfoliated, transferrable, and has a dielectric constant of 15, significantly higher than that of hBN, which ranges between 3-4. In this project I aimed to determine the dielectric constant and breakdown characteristics of Bi₂SeO₅ in hopes of finding a more effective substitute for hBN. I successfully measured the dielectric constant of Bi₂SeO₅ by fabricating a 2D graphene heterostrcuture device and taking graphene transport measurements comparing hBN and Bi₂SeO₅. From the data obtained I found the dielectric constant to be 14.3, which agrees with literary values. I fabricated a Bi₂SeO₅ backgate using a dry transfer technique in order to perform characterize the breakdown characteristics of the dielectric. By applying a voltage to each contact, grounding the gates, and then floating all other contacts, I measured the current passing through the dielectric until we began to see an exponential trend in order to measure breakdown. This is of interest because if Bi₂SeO₅ is proven to be more effective than hBN, it would let us apply a higher displacement field and doping to samples which would let us access new, exotic phases in 2D materials such as WTe₂.

DNA-binding proteins (DBPs) capable of targeting specific DNA sequences play key roles in genetic regulation and manipulation in both natural and synthetic contexts. Aided by advances in machine learning and protein engineering, the design of entirely new DBPs is now possible. However, early attempts yielded small, single-chain proteins that were unable to induce changes in genetic expression despite successfully binding to their target DNA sequence.  It is thought that contacts made by these early de novo DBPs are not sufficient to maintain binding in the presence of other proteins involved in DNA transcription, preventing the DBPs from altering gene expression. In contrast, many natural DBPs consist of protein complexes composed of two identical subunits called homodimers. These complexes have increased intermolecular contacts with DNA, enhancing their DNA-binding affinity. In hopes of improving the affinity of current de novo DBPs, machine learning tools like RFdiffusion, ProteinMPNN, and AlphaFold2, were invoked to engineer homodimerization domains onto these monomers. Promising designs were filtered, ordered, synthesized, and tested for efficacy as genetic inhibitors in E. coli. Homodimers that consistently and specifically bind to their target DNA sequences and induce genetic repression in E. coli have promise as tools for genetic engineering and manipulation. In future research, successful designs could also have applications in synthetic gene circuits and serve as foundations for de novo allosteric transcription factors for use as biosensors.

DNA-Binding Proteins (DBPs) hold a strong affinity to interact with the major grooves of DNA for the purposes of transcription, translation, and repair. Although DBPs are found in nature, their specificity is difficult to predict and their production expends excessive resources. Therefore, our project’s goal is to efficiently generate DBPs that allow us to enable exact processes to occur. This is promising for future use in treating genetic diseases. In our research, we studied point mutations in the hexosaminidase subunit alpha (HEXA) gene and adenosine deaminase (ADA) gene, which can lead to Tay-Sachs disease (TSD) and Severe Combined Immunodeficiency (SCID), respectively. TSD is a rare genetic disease that affects infants and ultimately leads to brain damage, resulting in these children not making it to grade school age. Similarly, SCID is genetic, where children lack a strong immune system. This increases their susceptibility to infections, especially during their first year of life. Targeting the HEXA and ADA genes, we first developed designs utilizing computational software like RosettaFold, RFdiffusion, x3DNA, and LigandMPNN, followed by rigorous filtering via RosettaFold Nucleic Acid. Afterwards, we tested the final designs using yeast cultures and fluorescence-activated cell sorting (FACS) in the laboratory to determine which bind best to their generated DNA template sequences. Overall, we expect to find a few specific DBPs that bind effectively as predicted during the computational pipeline. These successful designs can be utilized as genome-editing proteins, correcting their target DNA sequences and restoring normal function.

DNA-binding proteins contain DNA-binding domains with specific or general affinity to DNA strands and play a crucial role in multiple cellular functions, including regulation of gene expression and DNA repair. Their ability to interact with specific DNA sequences makes them attractive engineering targets for therapeutic intervention in various genetic diseases. In this project, we employed deep learning models to design novel DNA-binding proteins tailored to recognize and bind to four specific DNA sequences associated with three different genetic diseases: neurofibromatosis, sickle cell disease, and Caffey disease. These DNA sequences served as input for RFDiffusion and LigandMPNN to generate a diverse set of theoretical DNA-binding protein designs, which were then filtered with the goal of having high specificity and affinity for the target DNA sequences. These optimal designs will be expressed using yeast surface display and assayed for binding in the presence of their DNA targets, which assists in quantifying the efficacy of the deep learning models. This project aims to evaluate deep learning algorithms and to further optimize these models, as well as characterize the proteins generated with the protein design pipeline. Our designs demonstrate evidence of the feasibility of protein design through deep learning models, providing valuable data for future refinement of the algorithm. Moreover, the de novo design of DNA-binding proteins targeting DNA sequences related to genetic diseases has transformative implications for novel therapeutic interventions.

Lewy Bodies (LB) are ɑ-synuclein protein aggregates that form when the protein is falsely over-expressed. This inappropriate cell behavior is a symptom of neurodegenerative ailments including Parkinson’s Disease. GATA-1 is a transcription factor that binds to a conserved region in the SNCA gene and promotes transcription of ɑ-synuclein mRNA, which could be a contributing factor to disease presentation. DNA binding proteins serve a host of functions, primarily pertaining to regulation of gene expression. Through the Institute of Protein Design and their computational design pipeline, I designed proteins that bind to the GATA-1 binding site in the SNCA gene with the intention of inhibiting GATA-1 and hyper-expression observed in Parkinson’s patients. First I modeled the DNA target sequence using Chemnet software to best approximate the B form DNA structure that the final optimized proteins bind to. RoseTTAFold Nucleic Acid Diffusion generates protein backbones that have strong shape complementarity for the grooves of the target DNA. I then used LigandMPNN software to produce amino acid sequences for each of the backbone structures generated in the previous step. Designs are refined and diversified before the most plausible binders are selected via statistical metrics, AlphaFold2, and RoseTTAFold. I yielded roughly 5000 DNA binder models that were ordered as DNA libraries for high-throughput testing. Proteins are then expressed using yeast surface display and sorted via flow cytometry, where fluorescence indicates a binding event between the protein and DNA target. I had a robust sample of design outputs and intend on analyzing the successful protein models to quantify the importance of different structural features in DNA binding. Successful binders not only concurrently optimize and evaluate the efficacy of these deep learning models, but demonstrate the impact de-novo protein design can have on disease intervention via therapeutics research and development.

The use of opioid drugs for pain management in postoperative settings has been widespread since the 1860s. However, the risk factor for developing an opioid use disorder (OUD) has increased substantially with its continued use, with addiction rates of more than 10% for those taking opioids. An obstacle to abstinence in opioid addiction are the adverse side effects that occur during cessation of drug use once dependence has formed, including nausea, anxiety, vomiting, and depression. The Nucleus Accumbens (NAc) is part of the mesocorticolimbic reward pathway. Decades of pharmacological studies demonstrate that nearly all abused drugs evoke dopamine release within the NAc, thus altering innate systems for how reward is processed. The activity of NAc neurons is strongly regulated by efferent excitatory input from numerous brain regions. The paraventricular thalamus (PVT) a relatively understudied brain region, regulates behavioral responses to reward and aversive stimuli as well as to drugs of abuse such as morphine. Our preliminary data demonstrate that the activity of these projections is highly regulated by the Cannabinoid 1 Receptor (CB1), which mediates the primary psychoactive effect of cannabis. This is particularly relevant on account of recent clinical findings demonstrating that activation of CB1 can ameliorate the aversive effects of opiate withdrawal. Using fiber photometry (which uses fluorescence emission of the calcium sensitive fluorophore, GCaMP, as a proxy measurement for neural activity), I have shown that this circuit is activated by aversive stimuli and inhibited by rewarding stimuli. Furthermore, treatment with morphine can attenuate the pain-induced activation of this circuit. However, whether cannabinoids can influence this circuit's activity to reduce withdrawal symptoms remains untested. Our research will contribute to our understanding of the neurophysiological basis for opiate withdrawal and how cannabinoids could represent a novel class of therapeutics for the treatment of opiate use disorder.

Addiction is characterized by the compulsive use of substances despite adverse consequences, a process closely linked to dopamine-induced changes in the Nucleus Accumbens (NAc) and its role as the brain's "reward center." The NAc integrates information from various brain regions, including the Paraventricular Thalamus (PVT), to produce motivated behaviors. Recent studies have identified the PVT, especially its anterior segment (aPVT), as a critical hub in addiction neurocircuitry, but findings have been inconsistent, likely due to the PVT's heterogeneity and the specific neurochemical and anatomical properties of its connections to the NAc. Prior research has shown that aPVT neurons, identifiable by neurotensin expression, send excitatory projections to the NAc, which are modulated by endogenous cannabinoids (eCBs). These interactions suggest a complex regulatory mechanism. Preliminary experiments used techniques including transsynaptic viral tracing and in vivo calcium imaging, to study the activity dynamics of NAc neurons, particularly those expressing Proenkephalin (PENK) and receiving aPVT inputs, during reward-seeking tasks. I propose to extend these findings by employing a multidisciplinary approach that combines experimental neuroscience with sophisticated computational analysis. By applying dimensionality reduction techniques, clustering algorithms, and machine learning models to neural and behavioral data, I aim to map the functional connectivity within the NAc and elucidate the roles of specific neuronal ensembles in reward-seeking behavior. This comprehensive analysis will not only clarify the neurobiological underpinnings of addiction but also contribute to the development of targeted therapies for addiction and related disorders, leveraging the unique intersection of computational neuroscience and behavioral analysis.

DNA-binding proteins play crucial roles in many biological processes. They can regulate gene expression by binding to specific DNA sequences and either promote or inhibit transcription, or they can serve as gene editing technology. This research focuses on the design of de-novo proteins with potential therapeutic applications in gene editing and transcriptional modulation. For our designs, we selected four short DNA sequences associated with disorders. Two of these sequences have point mutations that are associated with sickle cell anemia and hypertrophic cardiomyopathy. The other two sequences are promoter regions; one linked to the INS gene featuring a cc dinucleotide sequence that may contribute to hyperglycemia and one on the oncogene MDM2 in the MDM2p-52 pathway which has the potential to cause cancer. We then used the multi-step computational design pipeline developed at the Institute for Protein Design, which utilizes deep-learning models including RFdiffusion, LigandMPNN, and RosettaFold Nucleic Acid, to generate protein binders to the target DNA sequences. After filtering for the best theoretical designs, we would test them through yeast cell display, which visualizes the binding of the protein to the DNA. We then can analyze the binding pattern of these protein designs which vary in strength and specificity of binding to the targeted DNA sequences. Since the computational design pathway strictly filters thousands of designs to narrow our selection to the best protein designs, we expect that a few of these proteins will have significantly strong binding to their respective DNA sequences. For the designs that demonstrate strong and specific binding to the targeted sequences, we may continue to improve their structure. The success of this computational design pipeline could result in breakthroughs in protein design, and has the potential to drive medical advancement and produce treatment for genetic disorders in the future.

Major Histocompatibility Class I (MHC) Molecules serve as a window into the cell, whereby T-cells can use their T-cell receptor to recognize foreign peptides presented on the MHC of a cell and induce apoptosis. Unfortunately, for diseases such as cancer, cancerous mutations may only be a single residue different from the native protein, resulting in similar mutant and wild-type peptide-MHCs. This small difference often results in negative selection of T-cell receptors that recognize mutant peptide-MHCs, leading to an absence of T-cells that can target cancer cells. This issue motivated us to use de novo protein design to generate binders with a high level of specificity between mutant and native peptide-MHCs. Using deep learning based protein design methods such as RFdiffusion and ProteinMPNN, we have generated promising in silico designs against a range of target peptides and MHC alleles. After selecting our top designs, we tested them using yeast surface display against our target MHC molecules with peptides loaded from KRas, PIK3CA, MAGE, and TP53 mutants. We observed binding events to all targets; some designs also had specificity to their respective mutant peptide-MHC over the wild-type peptide-MHC. After further work we have shown that the same design scaffold can bind to multiple peptide-MHC targets after slight redesign, similar to native T-cell receptors, holding promise that we could easily and quickly repurpose these scaffolds for new targets. Following these results we will incorporate our binders into T-cells as chimeric receptors and test for the ability of our binders to activate T-cell signaling and cell killing. This method of targeting peptide-MHC molecules is promising as a novel and rapid way to target cancer.

The Campanian stage of the Cretaceous (~84–72 million years [Ma]) was the zenith of dinosaur diversity. Western North America is highly fossiliferous and preserves Campanian-age rock units throughout the Western Interior Basin. Most studies that investigated dinosaur diversity from this interval used data obtained from macrosites (e.g., skeletons), whereas few have investigated vertebrate microfossil sites. Vertebrate microfossil sites are a rich source of data on biodiversity (e.g., taxon richness, relative abundance) and how it changes through time. The Judith River Formation of north-central Montana is rich in vertebrate microfossil sites, preserving 4 million years of the Campanian (~79–74 Ma). Here we aim to observe patterns of dinosaur diversity in the Judith River Formation by quantifying dinosaur taxon richness and relative abundances based on dinosaur teeth from two stratigraphically and temporally separated microfossil sites. These sites are the lower Makela-French 1 (~77 Ma) and the upper Clamfetti (~75 Ma). Presently, we have 300 specimens out of a planned 400. We hypothesize that changes in diversity and abudance occurred between these two sites. Our preliminary results reveal a change in dinosaur diversity between Makela-French 1 and Clamfetti. Hadrosaurs and ceratopsians are present and relatively abundance at both sites, whereas ankylosaurs decrease in abundance from Makela-French 1 to Clamfetti. Small herbivores like pachycephalosaurs and hypsilophodonts are rare at both sites. Theropods show similar patterns to the herbivore’s trends. Tyrannosaurs and dromaeosaurs are common at both sites, whereas troodontids are absent from Makela-French 1. These preliminary findings reflect diversity patterns that are not easily observable solely through the collection of dinosaur macrofossils. Our continued collection of fossils from Makela-French 1, Clamfetti, and additional sites will increase our sample size and provide better fine-scale resolution of dinosaur diversity patterns during this crucial interval in their evolution.

Physics concepts dealing with charges, particles, and electricity are difficult to conceptualize, yet foundational for students' scientific understanding. As an undergraduate researcher at the Novel Observations in Mixed Reality (NOMR) lab, I work on developing virtual reality applications for introductory physics lab courses at UW. To develop tools and scenarios for the labs, we use C# in Unity. One of the tools that I worked on developing was the Charge Tool, a tool that allows students to change particle charges and experiment with the relationship between charge and force according to Coulomb's Law. We conducted usability testing by taking participants with different levels of familiarity with VR, using the “think aloud” method. Through this, we identified user pain points for ease of use and ensuring an easier learning curve for students who are new to using VR. Based on key insights, we improved the particle colors to being more intuitive and color-blind friendly, as well as redesigned the tool to make it more functional and easier to use. I also work on a decay particle project that focuses on the principle of conservation of charge, momentum, and mass-energy, and a tutorial project which allows students to access instructions and lab manuals inside the VR lab scene. Since virtual reality is still pretty new in the field of education, research in it becomes important as it allows students to interact with physics concepts in a way that they never have before.

The nucleolus is an essential subnuclear organelle that performs central regulatory roles in cellular metabolism, epigenetic programming, and stress signaling. In mammals, nucleoli are disassembled and rebuilt de novo with each cell division, through an elaborate assembly mechanism that has long eluded molecular characterization. This assembly process is spatiotemporally controlled by a long noncoding RNA termed the 47S pre-ribosomal RNA (47S pre-rRNA), which initiates nucleolar assembly at the site of its transcription, and for which continued expression is required to maintain nucleolar integrity. Yet, while the 47S’ roles in nucleating and scaffolding nucleolar architecture are well established cytologically (they were first observed nearly a century ago), the structural elements on the 47S that enable these architectural functions remain unknown. I hypothesize that an RNA domain within the 47S, termed the 5´–External Transcribed Spacer (5´–ETS), harbors the long-sought structural scaffolds of the nucleolus. To test this, I am implementing a live-cell reporter assay that will monitor, in real time, if transcripts derived from the 5´–ETS drive nucleolar localization and architecture. My approach leverages recent advancements in artificial gene synthesis and live-cell RNA imaging. A novel drug-inducible promoter will enable me to temporally control expression of 5´–ETS sequence variants in live cells. I will monitor the kinetics and efficiency with which these transcripts localize into the nucleolus by two-color live cell imaging, using the newly discovered fluorescent RNA aptamer RhoBAST, and a fluorescently tagged nucleolar marker protein. To design our negative controls, I implemented a bioinformatic pipeline that generates scrambles of long, low-complexity RNA sequences—ablating primary structure but preserving dinucleotide content. This allows us to investigate whether nucleotide composition or sequence affects nucleolar formation. We anticipate that this powerful system will set the stage for detailed molecular characterization studies, revealing the long-elusive molecular interactions that control nucleolar architecture in health and disease.

Solar energy is a promising form of renewable energy that will play a major role in reducing carbon emissions. Perovskite-based solar cells have attracted significant attention due to their high power conversion efficiency (PCE), which reached 26.1% this year, surpassing commercial silicon’s (23.3%). High PCE, low cost of materials, and ability to be solution processed make perovskite solar cells a prime candidate to replace silicon. However, efficiencies are still below the theoretical limit and these materials suffer from limited operational stability. To tackle these problems, scientists have focused on minimizing active layer and interfacial defects which act as barriers for charge extraction in a solar cell, lowering the device efficiencies. Defects also electronically dope the perovskite layer, changing the recombination kinetics in the sample. The goal of this project is to quantify how the electronic doping and defect concentration of the perovskite sample is affected by surface passivation treatements via fluence dependent photoluminescence (PL) and time resolved photoluminescence (TRPL) spectroscopy. By solving the kinetic equations at the basis of charge recombination, we can extract the rate constants that correspond to different charge recombination pathways. We pioneer a global fitting analysis to simultaneously fit TRPL and PL measurements for robust determination of these kinetic constants that are subsequently used to determine the doping density of films before and after passivation. We show that the electronic doping density is higher than previously reported in literature, and that this doping is reduced with a surface passivation treatment. We collaborate with the University of Arizona to correlate our measured electronic doping density to electrochemically measured defect densities on the same samples. This work will provide an implementable tool to quantitatively assess electronic doping and defect density values for various perovskite compositions which will be useful for optimizing future solar cell devices.

Multiple Particle Tracking (MPT) is a powerful technique for studying the behavior of microscopic particles, such as viruses and nanoparticles, by tracking individual displacement and movement. One application of MPT is to measure microstructural changes in the brain extracellular environment (ECM) in development and aging, and in response to disease onset and progression. MPT of nanoparticle probes results in the generation of thousands of individual nanoparticle trajectories, from which geometric features, diffusion coefficients, and viscosities can be extracted. The vast array of trajectories contained within our dataset presents a good opportunity for integration into deep learning models that contains self-supervised learning, equivariant graph neural network, and Equivariant transformer. However, to enable MPT data to be trainable and predictable by deep learning models, we need to curate the data to be readable and useable by these models. To enable this, I have created a database and developed a data architecture that would allow MPT data to be passed into Deep learning models that use various techniques such as transformers. I am currently working on utilizing the data architecture on a Deep Learning model that uses transformers and self-supervised learning to predict trajectories of MPT particles. From this model, my expected accuracy of prediction of the trajectories for the MPT data is around 85%. This can allow us to learn complex features directly from raw MPT trajectory data, improve our predictions, and extract biological insights. The python package with our data architecture, the various SQL scripts, and the model will be provided as an open-source resource, allowing other researchers to expand upon my code and apply their unique modifications based on their own data and trajectories.

Since their introduction to clean energy applications, organic-inorganic lead halide perovskites have received great attention for their potential to create highly efficient, manufacturable and cheap solar cell devices. To make effective perovskite solar cells, charge transport layers are used to remove electrons and holes from the bulk perovskite semiconductor, increasing current, voltage and power conversion efficiency. Phosphonic acid self-assembled monolayers (SAMs) are a common hole transport layer. The phosphonic acid binds to the transparent conductive oxide electrode while an organic head group forms the SAM/perovskite interface. This head group is key for charge transfer and voltage characteristics, but the structure-function relationship is still poorly understood. My project investigates the role that deposition techniques and electronic structure play in the optimization of this SAM/perovskite interface. Expanding from the standard two step spincoating SAM/perovskite deposition method, I explored whether the codeposition of the two layers or the addition of a SAM solvent wash step produced an improved interface. I also fabricated films using several different SAM compositions to test for performance trends and improvements compared to the current field standard SAM, Me-4PACz. I collected photoluminescence lifetimes, quantum yields and solar simulation measurements to evaluate film performance. Preliminary data shows that neither the washing step nor codeposition add any performance benefit, but the single step codeposition achieves a more streamlined manufacturing method. Two of the new experimental SAMs performed comparably to Me-4PACz. These results encourage codeposition of the SAM/perovskite interface as the most efficient method to create high quality devices and show promising alternatives to the industry standard Me-4PACz SAM.

Current concentrations of carbon dioxide are 420 ppm, a 50% increase since the industrial revolution. Nitrous oxide (N₂O) concentration has increased by 18% since the industrial revolution and is currently 319 ppb. This slight increase may not appear alarming, but since nitrous oxide traps 300 times more heat than carbon dioxide, lowering emissions of this greenhouse gas will help stabilize the climate. One major source and sink of N₂O is production and reduction by microbes, respectively, which have been perturbed by anthropogenic increases of nitrogen. Novel microbes found in low pH (3-6) subsurface sites in Tennessee have been shown to respire N₂O, reducing it to N₂ using both Clade I or II nitrous oxide reductases. Microbes that can reduce N₂O at such low pHs (below 5) are rare but could be beneficial as a sink for nitrous oxide. One such strain is a novel Bacteroidetes that encodes a Clade II nitrous oxide reductase and was provisionally named strain S13. In my experiment, I aim to better understand the metabolic processes of this strain and identify the optimal temperature for growth, using xylose, a 5-carbon sugar, as the carbon source. S13 was then grown in an electron acceptor (N₂O) limiting environment, electron donor (xylose) limiting environment, and fermentative conditions. After the microbes completed their growth, the gas and metabolite concentrations were measured using Gas Chromatography and High-Performance Liquid Chromatography. When grown on xylose, the products produced were hydrogen gas, succinate, and acetate. Optical density of these tubes was measured over the course of their growth to determine growth rates and maximum yield (optical density). This was done at 6 temperatures: 15°C, 20°C, 25°C, 30°C, 35°C, and 40°C. These data indicated that S13’s optimal temperature for growth was 25°C. This information could be utilized in future bioremediation and nitrous oxide control efforts.

PIONEER is a rare-pion decay experiment, which aims to test Lepton Flavor Universality (LFU), a consequence of the Standard Model (SM) of particle physics. The SM is very successful but is known to be incomplete as it cannot describe gravity, dark matter, and other observed phenomena. PIONEER will test LFU by measuring the relative frequency of the two primary decays of a subatomic particle known as a pion. The ratio of the rates of pion decay to muon and pion decay to electron is predicted extremely precisely by the SM and is sensitive to physics beyond the SM. Therefore, this ratio is extremely important to measure. Muons quickly decay to electrons, so the final product of both decays is an electron, but their energies can distinguish the decay path. Thus, this measurement requires an extremely sensitive calorimeter to measure the energies of the resulting electrons. One candidate for this calorimeter is a large array of LYSO crystals. LYSO is a fast, dense, high-light-yield scintillator whose intrinsic properties suggest it would be a natural candidate for the experiment. Despite its advantages, a large, LYSO-based calorimeter has never been developed. We wish to measure certain properties of large LYSO crystals, such as energy resolution and uniformity, to determine if they meet the requirements necessary for use in the PIONEER calorimeter. Bench tests conducted thus far have displayed impressive single-crystal resolution and uniformity at low energies when crystals are wrapped in a well-fitted specular reflector. Energy resolution tests were conducted on an array of 10 LYSO crystals, using 17.6 MeV gamma rays produced by the Van der Graff accelerator at the Center for Experimental Nuclear Physics and Astrophysics (CENPA) here at UW. LYSO crystal performance and energy resolution have been shown in preliminary tests to be within the specifications for the PIONEER calorimeter.

Climate change caused by CO2 emissions creates a need for greater energy efficiency, as energy production produces CO2. One area of improvement is in chemical separations, which consume roughly half of all industrial energy use. Most of these processes could be made ten times more efficient by switching from energy-intensive distillation and absorption processes to membrane separations. Mixed Matrix Membranes (MMMs) are a particularly promising option for use in gas separations. These MMMs incorporate Metal Organic Framework (MOF) crystallites within polymer membranes to balance the benefits of both. However, the parameters that determine the growth of some MOFs, like ZIF-8, are still unknown. Here, we explore the growth conditions of ZIF-8 to enable the production of these MMMs on an industrial scale. By exposing zinc oxide (ZnO) coated silicon wafers to 2-methylimidazole (HmIm) in a tube furnace, I measure ZIF-8 crystal formation at varying temperatures, temperature gradients, and anneal times. Crystal thickness is measured using ellipsometry to observe successful crystal formation. Using COMSOL software, we explore models of the HmIm concentration to determine what experimental variables could have the largest effect on ZnO to ZIF-8 conversion. Preliminary experimental results suggest a specific temperature profile is needed, as well as a required minimum anneal time for successful ZnO to ZIF-8 conversion. Furthermore, temperature gradients appear to impact HmIm concentration, which affects crystal growth. These findings may enable industrial-scale production methods for MMMs with ZIF-8, and may also prove useful when producing MMMs with other MOFs.

Alpine areas are host to diverse plant communities that support ecosystems with their structure and floral resources and existing through specialized adaptations to harsh high-elevation conditions. An ongoing question in these plant communities is whether composition is shaped by stochastic processes (e.g., dispersal limitations) or by deterministic processes (e.g., climate) and if those processes select for common phylogenetic clades across space. This study evaluates the drivers of dissimilarity in vascular plant communities of alpine areas of 32 peaks in the Cascade Mountain Range of Washington State and the effects of incorporating phylogenetic relatedness to these conclusions. Observing an average of 54 species per peak, an inventory of 315 vascular total plant taxa were compiled to construct a phylogenetic tree relating each taxa to one another. We used multivariate techniques to quantify the phylogenetic and taxonomic differences between alpine plant communities and to relate those differences to each peak’s climate, geology, and topography. Our models indicate that each peak’s elevation, geologic parent material, and precipitation seasonality had the largest role in shaping alpine plant communities relative to the baseline effects of distance between peaks. Despite phylogeny contributing to lower overall dissimilarity, it conforms to the same trends between peaks and does not change the relationships to space, geology, and climate seen in taxonomic distance at the mountain-range scale. These results support the existence of deterministic spatial patterns of geology and climate driving community composition but fail to explain any evolutionary processes influencing colonization and survival in alpine environments.

Industrial chemical separation processes, such as distillation, drying, and evaporation, consume 10-15% of the world’s annual energy production. Membranes, which act as a selective barrier to separate compounds, are substantially more energy efficient than traditional chemical separation methods that require heat. Inorganic membranes are inherently suitable for many separation processes because they are chemically and thermally stable; however, ceramic membranes are mechanically fragile and costly to produce, which limits their usefulness. Commercial polymeric membranes are economical compared to inorganic membranes but degrade in harsh organic solvents and high-temperature environments. To reduce the cost of chemical separations, methods to improve the chemical and thermal stability of polymeric membranes are needed. Vapor phase infiltration (VPI), a gas-phase synthesizing technique, is one possible strategy for improving membranes, consisting of sorption, diffusion, and entrapment of vapor-phase inorganic reactants within organic polymers. The infiltration of inorganics through VPI has been shown to enhance the properties of polymeric membranes by producing cost-effective, chemically stable, and temperature-tolerant hybrid membranes. However, the change in mechanical properties when converting polymeric membranes to hybrid membranes is generally not well understood, and this understanding is crucial for maximizing the lifetime and durability of organic-inorganic membranes. In this study, polyethersulfone (PES) membranes are subjected to trimethylaluminum and water under various VPI process conditions in a custom-built reactor. Thermogravimetric analysis is utilized to quantify the extent of inorganic infiltration by measuring the aluminum oxide loading within PES membranes. The mechanical properties of these hybrid membranes after VPI are characterized for their tensile stress, modulus, and maximum pressure through dynamic mechanical analysis and burst pressure testing. Ongoing research to establish the physical properties of organic membranes with inorganic nanostructures is discussed. These results help optimize the trade-off between membrane stability and mechanical properties, which may allow many industrial chemical operations to become more sustainable.

Bacteria face a variety of threats, including antagonistic killing by other bacteria in competition for space and resources. In response to this antagonism, many bacteria have evolved specific defense systems. One pertinent example is the Pseudomonas aeruginosa Response to Antagonism (PARA) in P. aeruginosa, which provides defense against various antagonists by activating a suite of genes, mediated by two-component pathway Gac/Rsm, in response to kin cell lysis. The Gac/Rsm machinery is conserved across the Pseudomonas genus, but its function in defense has not been studied outside of P. aeruginosa. Here, we investigate whether two divergent Pseudomonas species, P. putida (KT2440) and P. protegens (Pf-5), similarly use Gac/Rsm in defense. To do this, we performed competitive growth assays against an antagonistic competitor, Enterobacter cloacae, comparing Gac/Rsm deletion mutants against wild-type, and quantified relative survival as an indicator of competitive fitness. Preliminary data indicate that the deletion of the core Gac/Rsm gene gacS results in dramatically decreased competitive fitness for Pf-5, but not for KT2440. This indicates that Pf-5 uses the Gac/Rsm system in a similar manner to P. aeruginosa and that, while Gac/Rsm is conserved, it may differ in function between species. To identify additional specific genes involved in defense systems, we set up a genome-wide screen. The screen indicated that genes related to the flagellum and lipopolysaccharide biosynthesis may be involved in defense against antagonism, which was surprising because these well-characterized structures have never before been implicated in defense. Work is currently underway to validate these genes as true defense factors and determine the mechanism by which they confer survival. Our findings advance the understanding of defense systems among Pseudomonas species by shedding light on their conservation and complexity, thus providing a foundation for future work on defense systems across bacterial phyla.