Masters Theses

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https://hdl.handle.net/10919/9291

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Taking a Functional Approach to Volunteering: Explaining Volunteer Congruence and Work Engagement
Gass, Jessica A. (Virginia Tech, 2024-11-14)
Using the functional approach to volunteering as a basis, I investigated the implications of volunteer motivation congruence (i.e., a match between motivations to volunteer and the satisfaction of those motivations) for work recovery and downstream work engagement. I focused on career and understanding-based volunteer motives and psychological detachment and mastery recovery experiences. This was evaluated using a cross-sectional survey with a sample (N = 119) of employees with past volunteering experience. I found that psychological detachment was higher when career motives were greater than career motive satisfaction. Agreement between motives and satisfaction for both career and understanding motivations was also found to be more important than disagreement for predicting mastery experiences. Neither recovery variable (detachment and mastery experiences) was found to predict work engagement. No hypothesized indirect effects of the work recovery variables on the relationship between volunteer congruence and work engagement were supported. Overall, the results show a novel pattern of findings that encourages future research on volunteer motivation congruence and recovery experiences.
Soft Sensing-Driven CO₂ Predictive Models in Educational Buildings
Meimand, Mostafa (Virginia Tech, 2024-10-14)
Indoor Air Quality (IAQ) plays a vital role in occupant well-being. Among various factors, CO2 concentration impacts the productivity and cognitive functions of occupants. Different strategies can be utilized to improve IAQ, including context-aware ventilation, air purification technologies, and integration of indoor plants. Existing methods in the literature for reducing CO2 concentrations rely on direct sensing, which requires advanced infrastructure that may prevent scalability. This study investigates a soft sensing approach, utilizing readily accessible features from Building Management System (BMS) to develop predictive models for CO2 concentration, offering a cost-effective alternative to direct sensor-based measurements. We leverage two different datasets to explore the feasibility and accuracy of the soft sensing approach. The first dataset aggregates CO2 data points compiled from existing literature, providing a broad perspective of IAQ variations across various educational settings. The second dataset is a publicly available, high-resolution set of IAQ measurements from several spaces over a month, allowing for detailed model training and testing. By applying machine learning techniques, we developed models that predict CO2 concentrations based on different sets of variables. We observed that the Random Forest model could predict CO2 concentration with a Mean Absolute Error (MAE) of 37.57 by utilizing room temperature, outdoor temperature, and the hour of the day. Moreover, this study assesses the transferability of the predictive models trained on a limited number of data points. We observed that using occupancy percentage results in more transferable models compared to other variable sets. The main contribution of this study to the body of knowledge is the evaluation of the soft sensing approach, which could pave the way for creating more scalable and infrastructure-independent systems to improve indoor air quality in educational facilities.
Effect of ionic strength on heterogeneous nucleation of calcite during biomineralization
Knight, Brenna M. (Virginia Tech, 2024-12-18)
Biominerals often form within a matrix of biomacromolecules in high-salinity environments, yet the relationships for how macromolecules and ionic strength influence the crystallization of sparingly soluble salts (e.g., CaCO₃) are not established. Developing a physical picture of these controls is hindered by the traditional assumption that background electrolytes are inert. In this study, we investigate calcite nucleation onto two model organic matrix polysaccharides, chitosan and alginate, in a series of ionic strength solutions (65 – 600 mM NaCl). Chitosan is near-neutral (at pH 8.5) and analogous to the structural polysaccharide chitin. In contrast, alginate has a strong negative charge akin to the many anionic biopolymers in the organic matrix. By measuring the rate of calcite nucleation onto these materials and fitting classical nucleation theory to the data, we find the interfacial free energy (γ_net) and the kinetic prefactor depend upon ionic strength for both polysaccharides. The thermodynamic barrier to nucleating calcite onto alginate strongly depends on ionic strength, while calcite nucleation onto chitosan shows a similar but weaker dependence. Parallel molecular dynamics (MD) simulations were conducted to examine ion (Ca²⁺, Na⁺, HCO₃⁻, Cl⁻) and water interactions with models of a carboxylated polysaccharide and a chitosan material. The MD predictions indicate that at higher ionic strength, the polysaccharide-solution interface is increasingly stabilized by progressively higher concentrations of Na⁺ and Cl⁻. Stronger Na⁺ interactions with the polysaccharide are observed in the carboxylated system. The numbers of H₂O and HCO₃⁻ in the Ca²⁺ hydration sphere decrease with increasing ionic strength, while the number of Cl⁻ increases for both polysaccharides. The evidence suggests the increase in interface stabilization by Na⁺ and Cl⁻ increases γ_net through reductions in the polysaccharide-solution interfacial energy. We predict the effect of higher salinity is enhanced for alginate because Na⁺ interactions with COO⁻ groups make it more difficult for Ca²⁺ to displace near-surface water and/ or Na⁺. Relatively weak Na⁺-chitosan molecular interactions lead to a lesser dependence on ionic strength. Calcite nucleation rates were also measured onto chitosan in a series of sodium halide solutions (NaCl, NaBr, NaI) and onto alginate in a series of chloride salts (LiCl, NaCl, CsCl) at constant ionic strength. CaCO₃ nucleation in the presence of electrolytes with the strongest hydration properties presents the lowest γ_net. Values of γ_net increase in the order Cl⁻
Impact of Real-Time Information and Road User Fees on Individuals Mode Choice Decision
Hridoy, Daud Nabi (Virginia Tech, 2024-12-02)
This research investigates the impact of multi-source real-time information and mileage-based user fee (MBUF) on individuals' mode choice behavior. It examines the interaction between MBUF and socio-demographic variables for different trip purposes. This research designs two separate web-based surveys. Each survey has revealed preference (RP) and stated preference (SP) components. The SP components consist of hypothetical scenarios to capture individuals' mode choice behavior based on real-time information and MBUF. The research develops a series of advanced econometric models using the collected survey data to understand the factors influencing individuals' mode choice behavior. The findings indicate that daily parking costs significantly impact individuals' mode choices and tend to discourage driving. Real-time information, such as parking space availability at workplaces and metro stations, encourages people to prefer drive and park & ride modes. Information on road closures and road accidents discourages people from driving, riding as auto-passengers, or taking TNC (Uber/Lyft) for trip purposes. Regarding MBUF, the results reveal that individuals are less likely to prefer motorized modes with the increased rate of MBUF. Full-time workers show more sensitivity towards MBUF for work trips, whereas college students are more sensitive to MBUF for recreational trips. Older adults are more sensitive to MBUF for work trips, and young individuals are more sensitive to MBUF for work and grocery/shopping trips. The results show that increased fuel costs, toll costs, bus fares, and delays reduce the likelihood of driving alone, carpooling, and transit. The findings of this research provide critical insights, supporting the implementation of evidence-based strategies to promote alternative sustainable transportation modes in the presence of real-time information and MBUF.
Dialogues with Nature: A Study on Nature as a Precedent for Form and Meaning in Architecture
Murray, Kirsten Ring (Virginia Tech, 2024-11-22)
The following research project examines the use of nature as a precedent for form and order in architecture. Includes a critical history of different beliefs and philosophies evident in Western thought. These principles are explored through the design of a complex of buildings and site interventions designed to promote a heightened sense of awareness of the natural surroundings, and to stimulate reflection on the relationship of the individual to nature and to the larger social collective.
Warm Start Algorithms for Bipartite Matching and Optimal Transport
Mittal, Akash (Virginia Tech, 2025-01-22)
Minimum Cost Bipartite Matching and Optimal Transport are essential optimization challenges with applications in logistics, artificial intelligence, and multimodal data alignment. These problems involve finding efficient pairings while minimizing costs. Due to the combinatorial nature of minimum-cost bipartite matching and optimal transport problems, the worst-case time complexities of standard algorithms are often prohibitively high. To mitigate this, prior information or warm starts are commonly employed to accelerate computation. In this thesis, we propose two novel warm-start algorithms for solving the minimum-cost bipartite matching problem, with the first algorithm extending naturally to the optimal transport problem. The first algorithm uses the LMR algorithm to produce dual weights with respect to a scaled version of an approximate matching or transport plan with high additive error. Using these dual weights as warm starts enables a faster computation of approximate matchings or transport plans with extremely small additive error (delta) faster than the original LMR algorithm, which currently holds the state-of-the-art execution time for approximating the optimal transport plan, a generalization of bipartite matching, in the sequential setting. By achieving lower additive error at a faster rate, our method provides a smoother trade-off between exact and approximate matchings or transport plans. Given that C is the largest cost of the given matching or transportation problem and delta is our chosen additive error, the running time for our first warm start algorithm for matching is O(n^2 * min(C / delta, sqrt(n))) and for optimal transport is O(n^2 * min(sqrt(nC / delta), C / delta)). Our second warm-start algorithm leverages machine-learned weights to accelerate the computation of minimum-cost bipartite matching. This learning-augmented approach achieves a theoretically superior running time compared to the previous best learning-augmented algorithms for the same problem. Overall, this thesis highlights the effectiveness of combining theoretical algorithmic advancements with modern learning-based techniques, resulting in robust and efficient solutions for fundamental optimization problems.
A tool for analyzing the evolution of non-uniformities in lithium-ion cylindrical battery cells at the module level under various operating conditions
Dange, Soham Suneel (Virginia Tech, 2025-01-22)
Lithium-ion batteries are critical components in electric vehicles, portable electronics, and grid energy storage systems, necessitating advanced modeling techniques to enhance their safety, performance, and lifespan. This thesis presents the development and validation of a coupled electrical and lumped thermal model for cylindrical lithium-ion batteries along with a finite difference thermal model for spatial temperature prediction of cylindrical cell These models address key challenges in simulating real-world battery behavior. The electrical model utilizes a 2 R-C pair equivalent circuit framework integrated with a busbar model to account for current imbalances in parallel-connected cells. This model is a common equivalent circuit model used to represent a Li-ion cell using a voltage source, series resistor, and two resistor-capacitor pair connected in parallel. A lumped thermal model coupled with the electrical framework dynamically adjusts parameters based on temperature variations, achieving a voltage prediction error of less than 200 mV. Additionally, the thermal model employs a finite difference method (FDM) to solve the 3D transient heat conduction equation, providing spatial temperature distribution within cells and capturing critical gradients between core and surface temperatures. The vectorization of the thermal solver reduced simulation time by half, and its validation against Ansys™ simulations and module-level data demonstrated temperature prediction accuracy within a 2–3°C margin. The developed tool is scalable for any number of cylindrical cells arranged in a rectangular grid, addressing key gaps identified in the literature, including the need to simulate spatial and temporal non-uniformities in state-of-charge (SOC), state-of-health (SOH), and temperature, which significantly affect battery performance and lifespan. It provides a scalable, efficient tool for predicting thermal and electrical behavior across cell and module levels. This work contributes to the development of a tool that will, enable informed design decisions for next-generation energy storage systems. Future research could focus on extending the model to incorporate aging effects, enhanced thermal management configurations, and real-time simulations for battery management systems.
A Dialogue between Permanence and Impermanence
Bi Ojes, Ronn (Virginia Tech, 2025-01-22)
This thesis, "A Dialogue Between Permanence and Impermanence," is an exploration of architecture's ability to embody time's duality—its capacity to endure and its inevitability to transform. The project envisions a hotel as a living metaphor for life itself, where the permanence of its structural essence contrasts with the fleeting presence of its occupants. Through materiality, light, and spatial transitions, the design fosters a conversation between what is lasting and what is ephemeral.
Cancer Proliferation at the Brain Metastatic Site: A Proteomic Exploration of Inter-Cellular Cross-talk Sustained by Cell-membrane/Secretome Interactions
Zhang, Yunqian (Virginia Tech, 2025-01-21)
Brain metastasis of breast cancer is one of the leading causes of mortality in patients suffering from cancer. The unique structure and components of the brain microenvironment, including the brain-blood barrier and the immune-suppressive environment, present significant clinical challenges to treating brain metastatic breast cancers. This study has hypothesized that the thriving of metastatic breast cancer cells within the brain is driven by the complex interactions between cancer cells and the brain tumor microenvironment, which is reshaped into a tumor-permissive environment. Therefore, by utilizing mass spectrometry-based proteomic analysis, this study focused on analyzing the secretome and cell surfaceome of metastatic breast cancer and brain-residential cells to reveal the interactions between these cells and contribution to various cancer-developing biological processes, including cell growth and proliferation, cell death and apoptosis, immune modulation, angiogenesis, extracellular matrix organization, and epithelial-mesenchymal transition. The project was conducted in three tiers: (1) profiling the secreted and cell membrane proteins, (2) mapping ligand-receptor interactions using an in-house ligand-receptor database, and (3) determining the functional roles of the interacting ligands and receptors. The analysis revealed a complex network of intercellular communications demonstrating how the cancer cells could potentially influence the brain residential cells and, conversely, how the brain cells could influence the cancer cells and contribute to reshaping the tumor microenvironments to support cancer progression. 3D co-culture spheroid models further underlined the influence of cell-cell interactions on tumor growth. Altogether, this work provides an integrated approach to understanding the molecular cross-talk within the brain tumor microenvironment and in-depth insights into potential therapeutic targets to disrupt tumor-promoting changes in the brain metastatic niche.
Beyond Curation: A Validation and Classification Infrastructure for an Educational Content Catalog
Aina, Adeyemi Babatunde (Virginia Tech, 2025-01-21)
To address the challenge of discovering computer science learning resources, the Smart Learning Content (SLC) catalog is designed to simplify access to the growing body of educational content. As part of the Standards, Protocols, and Learning Infrastructure for Computing Education (SPLICE) research community's efforts, the catalog functions as a centralized platform supporting SPLICE's objectives of improving interoperability, enabling comprehensive data collection, and facilitating data analysis in computer science education. The SLC catalog stands out from previous catalogs with an approach that applies an ontology-based content organization and validation services. Additionally, it serves as a platform where educators can contribute, access, and share a wide range of resources—including slideshows, interactive exercises, programming tasks, and Learning Tools Interoperability (LTI)-integrated content from various learning tools. While the primary goal of the catalog is to disseminate high-quality learning materials, its extensive and varied content requires robust organization and validation mechanisms to ensure educators can efficiently locate and utilize resources. The catalog is designed to further support diverse content types, including both standalone resources and content bundles. For one of our key contributors, OpenDSA—an e-textbook system—we have adopted latest LTI 1.3 standard. This implementation enables the catalog to disseminate content in both LTI 1.1 and LTI 1.3 standards, ensuring compatibility. One key improvement in LTI 1.3 is its security features, incorporating robust authentication methods to ensure stronger protection of sensitive student information. This updated standard enables learning tools to meet the evolving demands of digital education, providing educators and learners with more secure, flexible, and effective resources.
The Sacred Cycle -Architecture as a Vessel of Light and Spiritual Renewal
Siji, Serin (Virginia Tech, 2025-01-21)
This thesis explores the integration of sacred space architecture with the symbolism of light and geometry to create a transcendent environment for reflection, healing, and spiritual awakening. Through precise sunpath alignments and the interplay of light and shadow, the design draws on the natural rhythms of the solstices and equinoxes to guide visitors on a journey of introspection and renewal. Key architectural elements, such as the 32 memorial columns representing the victims of the Virginia Tech tragedy, symbolize resilience and transcendence. The use of natural materials, filtered light, and sacred geometry enhances the space's meditative qualities, inviting a deeper connection to both the self and the surrounding environment. The project seeks to honor the past, while fostering healing and a renewed sense of spiritual connection. By merging nature, architecture, and light, this design creates a sanctuary that embodies the cyclical nature of life, offering a timeless space for contemplation and enlightenment.
Influence of biological sex and thyroid hormone on skeletal and cardiac muscle metabolism in heat-stressed pigs
Dougherty, Dana Claire (Virginia Tech, 2025-01-21)
Heat stress (HS) is a substantial economic burden for the livestock industry, and the problem is expected to increase because of unprecedented warming temperatures caused by climate change. Skeletal muscle, which is the main source of income for the meat industry, is particularly sensitive to HS. Lean tissue accretion declines during HS, in part because of inefficient metabolism and altered metabolic flexibility. The first study examined whether thyroid hormone (TH) administration during HS could maintain metabolic flexibility in HS skeletal and cardiac muscle. Gilts (n=39) were placed in one of five treatment groups: Thermoneutral control (TNC), HS for 1 day (HS1C), HS1 with TH supplementation (HS1TH), HS for 7 days (HS7C), HS7 with TH supplementation (HS7TH). Semitendinosus red skeletal muscle (STR) and right ventricle cardiac muscle (RV) were dissected and snap-frozen for subsequent analysis. Metabolic flexibility (MF; P=0.08) tended to differ between HS1C, HS7C, with HS7TH being the lowest (P < 0.05) in STR. Pyruvate oxidation significantly differed with the lowest in HS1TH and highest in HS7TH in RV muscles (P < 0.05) but metabolic flexibility did not differ. However, citrate synthase (CS) and cytochrome C oxidase (COX) activity did not differ between treatments. The second experiment investigated the difference in skeletal muscle metabolism between gilts and barrows. Pigs (n=8/sex) were exposed to TNC, HS1, or HS7 environments. Skeletal muscle samples were used to analyze in vivo metabolism. Barrows tended (P=0.09) to be more MF than gilts even though no changes in mitochondrial enzyme activity were observed. However, gilts differed between environmental treatments regarding CS activity (P=0.03). Blood serum from the treated pigs was placed on skeletal muscle cell cultures. Metabolic flexibility between the in vivo muscle samples and the cell cultures was similar. In vitro culture at 37°C recapitulated the in vivo metabolic flexibility pattern. When the cell culture temperature was maintained at 41°C, MF was the lowest in cultures that received HS7 blood serum. In vitro males incubated at 41°C displayed an increase in CS and COX activity (P<0.01) but females increased 3-hydroxyacyl-CoA dehydrogenase (BHAD) activity at the same incubation temperature (P=0.02). This data indicates that in vivo systemic factor(s) can alter skeletal muscle metabolism in vitro.
Galactic Flood Fill Segmentation and Machine Learning Redshift Estimation
Ferguson, Matthew Chase (Virginia Tech, 2025-01-21)
This thesis explores the use of machine learning redshift estimation models trained on segmented galactic images. Segmentation of galaxies from the background is accomplished using a flood fill segmentation method which is novel to the field of galactic segmentation. Astronomy datasets are so large due to high volume modern surveys that automated analysis techniques are now required. Redshift is a prime example of an expensive measurement that is a candidate for automation. The Sloan Digital Sky Survey alone imaged more than 1 billion objects in 9 years, but only produced 4 million spectra over more than 20 years. Machine learning is an automation technology that promises to efficiently analyze imaging data alone such that redshift can be estimated with a high degree of accuracy. Ground truth redshift and multi-band galactic images were obtained for 200,000 galaxies from the Sloan Digital Sky Survey. Two model architectures were experimented with, a fully connected artificial neural network, and a convolutional neural network. Experiments were conducted on flood fill parameters, crop sizes, color spaces, and thresholding. We demonstrated that model performance on flood fill segments is higher than on unsegmented images across many crop sizes. The best achieved model performances for artificial neural networks, and convolutional neural networks are median absolute dispersions of 0.024 and 0.031, respectively.
Reimagining Urban Leftover Spaces Under Overpasses: Mitigating Urban Heat Islands and Heatwaves Through Green Space Transformation
Zhao, Jiahua (Virginia Tech, 2025-01-17)
The urban thermal environment is deteriorating due to the constructed urban space and climate change, which negatively impacts public health, especially in the Urban Canopy Layer (UCL). Recent research has investigated the contribution of microclimate parametric in specific urban typologies such as plazas and streets. However, the wasted under-bridge space, which is hard to utilize and usually deemed an obstacle to community connection, is lacking in the investigation. Therefore, this research aims to study the thermal mitigation and adaptation design strategy in terms of the thermal benefit of space, eventually transforming the space with a thermal comfort perspective to the community. The site was chosen under the Williamsburg Bridge in Lower Manhattan, a highly populated and dense low-income community. The method of study is research by design. Firstly, I used open-source GIS data and community reports to investigate the neighborhood's socioeconomic status and functional outdoor space. Secondly, we conducted a site visit and thermal walk in August to measure the microclimate parameters, including air temperature, Relative Humidity (RH), wind speed, and direction, to understand the impact of different heights of under-bridge space on thermal comfort. Thirdly, we use open-source Climate Studio and Ladybug to stimulate the thermal environment of under-bridge space and interpret it with the Universal Thermal Climate Index (UTCI). Based on the thermal environment analysis, measurement, and investigation to tailor the mitigative and adaptative design strategy for transforming the under-bridge. Eventually, the strategy will guide design implementation via proposed outdoor activity, planting strategies, and pavement patterns in the under-bridge space design, and it will evaluate the impact of thermal comfort with the implemented strategy on the space. The result revealed that the under-bridge space limited sunlight hours and the Sky view factor (SVF). It correlated with the space change, resulting in different stress levels on the UTCI scale. The evaluation result highlighted the tailored strategy in the design process can help alleviate the specific stress level of UTCI levels to zero or less thermal stress, especially in space with extremely low SVF conditions. This study can be a reference case for a similar under-bridge space transformation with a semi-open space, limited sunlight hours, low SVF, and close to the residential area. The study provides urban designer planners and landscape architects with a toolkit and approach from the pedestrian thermal comfort perspective rather than the aesthetic environment design in waste space reclamation strategy.
When Less Can Be More: Evaluating the Impact of Animated and Interactive Demonstrations in Voice-Assisted Counting Games for Young Children
Karunaratna, Sulakna Binoka Kumarihamy (Virginia Tech, 2025-01-17)
Early experiences with counting form a critical foundation for children's numeracy development. Despite the increasing use of voice assistants in young children's math learning, the effectiveness of different levels of demonstration—animated and interactive—accompanied by these assistants remains unclear. This study examines how different demonstrations in touchscreen devices, combined with voice assistants, supported children's developing counting skills. We developed a tablet counting game for children aged 2-4 years, incorporating voice assistant counting. In a user study with 32 children, we compared two conditions (animated and interactive demonstrations), with each condition also being evaluated against a baseline. We found that animated demonstrations improved math performance compared to the baseline, while interactive demonstrations did not. These findings suggest that counting with voice assistants has the potential to support early counting experiences and highlight the importance of designing educational technology with appropriate levels of demonstration to engage young learners without increasing cognitive overload.
A Framework for Identifying Roadway Characteristics Affecting Speeding-Related Crashes in Rural Areas
Belt, Kathryn Lanning (Virginia Tech, 2025-01-17)
Speeding is a major concern on all roadways and is a leading factor in traffic fatalities and serious injuries. Rural roadways are often disproportionately impacted by these traffic crashes and fatalities, despite the lower traffic volumes and populations. It is important to address this speeding issue, especially in rural areas, which can be done with an organized plan, such as a Speed Management Action Plan (SMAP), and collaboration with all parties involved. The goal of this research is to provide a framework to help rural areas identify locations that are at higher risk of speeding-related crashes by analyzing roadway characteristics that have a higher likelihood of a speeding-related crash to occur and which characteristics have a larger proportional influence associated with them. Identifying these roadway characteristics can help focus state crash analysis or countermeasure implementation to ensure that locations that are at highest risk of speeding-related crashes are receiving appropriate and effective speed management countermeasures. The framework identifies roadway characteristics that are more likely to contribute to speeding-related crashes, focusing on rural, non-interstate, and non-intersection roads. It underscores the importance of data-driven decision-making to prioritize high-risk locations and optimize resource allocation. By providing states with tools and information, the framework facilitates the identification of critical factors influencing speeding-related crashes, such as roadway alignment, surface conditions, and lighting. Additionally, it provides comprehensive guidance on data collection, data filtering, key characteristics to identify, data analysis, prioritizing findings, applying the results, and monitoring the implementations. This structured approach not only supports the effective use of crash data for informed decision-making but also aligns with the development and execution of SMAPs. The research utilized Virginia Department of Motor Vehicles Traffic Records Electronic Data System (TREDS) crash data for the year 2022 to create the framework. The roadway characteristics included in the analysis were determined using engineering judgement and past studies. Those characteristics were identified from the attributes: location of the first harmful event, light conditions, roadway alignment, roadway description, roadway defects, and roadway surface conditions. A proportional analysis method was used to calculate the speeding-related crash likelihood percentage and the systemic impact percentage for each characteristic included in the analysis. Key findings from this analysis revealed certain roadway characteristics, such as roadside, darkness, curved, two-way not divided, and wet surfaces that had high impacts on both the likelihood of a speeding-related crash occurring and high systemic impact. Virginia crash data was used in this study to test the framework developed to show its effectiveness and how it can be utilized by other states.
Multi-Kilovolt Gallium Nitride Power Transistors
Guo, Yijin (Virginia Tech, 2025-01-16)
Power semiconductor device, as a significant contributor to power electronics industry, plays an indispensable role in energy conversion applications including electric vehicles, data centers, consumer electronics, power grids, etc. The evolution of power semiconductor materials has progressed from traditional silicon (Si) to wide-bandgap materials including silicon carbide (SiC) and gallium nitride (GaN). Benefitting from the wide bandgap, high electron mobility and good thermal conductivity of GaN, GaN-based power devices can achieve fast switching speed, high breakdown voltage, and small on-resistance. They have been deployed in numerous power electronics applications, outperforming the Si and SiC counterparts. Nevertheless, despite their inherent advantages, the commercialization of GaN devices, particularly high-electron-mobility transistors (HEMTs), has predominantly been confined to the low-voltage domain of typically below 650 volts. This limitation blocks GaN HEMTs for medium- and high-voltage applications such as electric vehicles, renewable energy processing, and power grids, which have a total market size over USD$15 billion. The challenge for GaN HEMTs to reach high-voltage applications arises primarily from the highly non-uniform electric field (E-field) distribution within the device structure, predisposing the device to premature breakdown and limiting its operational voltage range. Consequently, the quest for higher voltage capabilities in GaN HEMTs requires the fundamental understanding and effective mitigation of this non-uniform E-field distribution. In this work, the p-type GaN-based Reduced Surface Field (RESURF) structure is proposed to balance the net charge in the two-dimensional-electron gas (2DEG) channel in GaN HEMT. This design enables a uniform distribution of E-field, enabling the voltage upscaling in GaN HEMT up to 10,000 V (i.e., 10 kV), which is the milestone voltage class in unipolar power devices for high-power applications. The first part of this thesis introduces the history, background and mechanism of power semiconductor devices and provides solid reasons for GaN as a competitive participant in power electronics industry. It covers a basic introduction about GaN HEMT devices and their commercialization status and states the challenges GaN HEMTs are facing when dealing with mass production. An innovative RESURF structure is introduced to overcome the existing trade-off between on-resistance and breakdown voltage, and to achieve superior overall performance that would be beneficial for GaN HEMT to upscale the voltage classes. Secondly, the development of a 10 kV unidirectional GaN HEMTs is discussed in detail. An optimized fabrication process flow, including etching, metal deposition, contact formation and dielectric passivation, is established. The RESURF structure is formed through a two-step chlorine-based etching process, with an innovative introduction of sulfur hexafluoride (SF6) that enables a self-termination etch stop onto the AlGaN surface without damage to the 2DEG channel beneath. A controlled slow etch recipe has been developed as well, aiming for large-scale manufacturing with improved yields. A detailed analysis of the on-state and off-state I-V characterization of devices with various RESURF thickness and length provides an insight into the device breakdown mechanism, which has been verified with physics-based technology computer-aided design (TCAD) simulation. The third part of this work demonstrates a 3.3-kV monolithic bidirectional switch (MBDS), which a novel device concept that can significantly simplify the circuit design in alternative current (AC) power conversion. A symmetrical p-GaN junction termination extension (JTE) design is proposed for electric field management, and the lateral conduction of this GaN-based MBDS enables a state-of-the-art high-voltage bidirectional switch with low on-resistance, achieving considerable performance advantage compared to the conventional bidirectional switch implemented by discrete devices. In summary, this research work covers the design, fabrication, characterization, simulation, and breakdown mechanism analysis of GaN-based unidirectional and bidirectional transistors for multi-kilovolt power conversion applications. The extended p-GaN configuration (RESURF for unidirectional devices and JTE for bidirectional devices) offers a spatially-distributed E-field management, enhancing the breakdown voltage scaling capability of GaN HEMTs to exploit the full material advantages of GaN.
Phosphorylation kinetics of cardiac gap junction regulation during stress
Stanley, Kari Elizabeth (Virginia Tech, 2025-01-15)
The coordinated contraction of the heart occurs because of the propagation of action potential between the cardiomyocytes. Gap junctions consisting primarily of connexin43 (Cx43) connect cardiomyocytes at regions of contact between cells known as the intercalated disc to facilitate cellular coupling. Cardiac pathologies frequently manifest with disrupted gap junctional intercellular communication which can generate potentially fatal arrhythmias, thus, it is essential to elucidate mechanisms underlying Cx43 regulation and altered intercellular communication. Phosphorylation of residues in the Cx43 carboxyl terminus can alter the subcellular localization, channel gating, and internalization of Cx43. The channel open probability of gap junctions is regulated, in part, by the phosphorylation of S368. Phosphorylation of S365 and S373 have been reported to exert gatekeeper effects on the phosphorylation of S368 and these phosphorylation events further affect protein interactions with 14-3-3 and zonula occludens-1 (ZO-1). While it is established that pS365 creates a conformational change preventing pS368, it is currently unclear precisely how pS373 regulates pS368. Further, it is unclear if alterations to these residues might impact protein binding and pathological cardiac remodeling during stress. Utilizing an ex vivo ischemia model, we find by immunofluorescent confocal microscopy that wildtype Cx43 hearts exhibit significantly decreased Cx43/N-cadherin colocalization during ischemia, while phospho-null mutant hearts retain Cx43/N-cadherin colocalization. Triton X-100 solubility assay indicates S365A/S373A mice have increased junctional Cx43 during ischemia. Additionally, we show that pS368 decay is more rapid in S373A mutants than wildtype suggesting, for the first time, that pS373 may prevent dephosphorylation at Cx43-S368 rather than promote Cx43-pS368. This knowledge could highlight potential therapies for prevention of cardiac remodeling and arrhythmogenesis.
Effectiveness of Alternative Reinforcing Strategies for Non-Contact Hooked Bar Lap Splices
Brown, Mason Kendall (Virginia Tech, 2025-01-15)
Closure joints are used in precast bridge construction to join two pieces of precast concrete. The pieces of concrete are joined by a lap splice which consists of longitudinal steel sticking out of each precast element and overlapped over the minimum required development length. State departments of transportation find it desirable to make the width of closure joints short. To achieve this, bridge engineers have been using hooked bars in the closure joints in lieu of straight bars, with the assumption that this would allow for shorter splice lengths. Though engineers in practice are doing this, design guidance does not exist. One research project by Coleman (2024) tested 58 beam-splice specimens to investigate the impacts of a variety of parameters on bond and anchorage and develop design guidance for hooked bar lap splices. This project did not investigate three parameters: the number of lap splices, the placement of transverse reinforcement, and the addition of steel fibers in the closure joint. For this thesis, 15 beam-splice specimens were tested in 4 point-bending to investigate the impact of these parameters on bond and validate the descriptive equation developed by Coleman (2024) to determine the bar stress of a hooked bar lap splice. The findings of this study suggest that the number of splices and the placement of transverse reinforcement has minimal impact on the bar stress developed, and the equation by Coleman (2024) adequately predicts the bar stress when these parameters were varied. The addition of steel fibers to the closure joint had a substantial impact on increasing the splice strength. In the beams where steel fibers were added in a 1% fiber volume fraction, the descriptive equation by Coleman (2024) underpredicted the bar stress for both unconfined and confined beams with the addition of fibers. Thus, this thesis proposes a factor to multiply the descriptive equation by determining the bar stress when steel fibers are added. With these findings, using steel fibers in closure joints for precast concrete can be used to reduce splice length in non-contact hooked bar lap splices.
Tailoring Microenvironment and Orientation of Immobilized Lactase for Improved Catalysis at Suboptimal pH
Fianu, Felicia (Virginia Tech, 2025-01-15)
The U.S. Greek yogurt market has experienced significant growth, rising from 1-2% in 2004 to 40% in 2015, resulting in a large amount of lactose-rich acid whey as a byproduct. Using lactase to transform this waste into valuable products has emerged as a promising solution. Covalent immobilization allows enzymes to be reused and prevents contamination of the product. While immobilizing lactases has been found to enhance their pH and temperature stability, undesired enzyme-substrate interactions can still lead to reduced enzyme activity. This study investigates novel approaches for enhancing the performance of immobilized lactase enzyme through controlled orientation and microenvironment modification. We utilized initiated chemical vapor deposition (iCVD) to fabricate tailored polymeric thin films as enzyme immobilization supports. A site-specific spycatcher/spytag system was employed for direct immobilization of lactase, while polycationic polymers were incorporated to modify the local chemical environment. Fourier Transform Infrared (FTIR) spectroscopy confirmed the retention of key functional groups in the polymeric supports. The epoxide-amine ring-opening reaction between the support and enzyme was verified, indicating covalent immobilization. Directed immobilization resulted in significantly improved enzyme activity compared to random immobilization, particularly at pH 7 and 8. Incorporation of hydrophobic crosslinkers further enhanced the activity of directedly immobilized Lactase, even exceeding that of the free LacZ-ST by 155% at pH 7, while no effect was observed for randomly immobilized LacZ. The inclusion of pH-responsive polycationic moieties in the support enabled LacZ to catalyze at pH 4, where the free enzyme is typically inactive. This study demonstrates the potential of combining controlled enzyme orientation with tailored microenvironments to optimize the performance of immobilized biocatalysts across a broader pH range.

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