Doctoral Dissertations

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  • Machine-Learning Assisted Atomic Simulations of Defect Dynamics in Multicomponent Concentrated Alloys
    Huang, Wenjiang (Virginia Tech, 2024-12-06)
    This dissertation investigates the complex defect diffusion behaviors in concentrated solid solution alloys (CSAs) including high-entropy alloys (HEAs), which are critical for understanding their exceptional mechanical and radiation-resistant properties. Through a combination of multiple atomistic-level simulation techniques and novel machine learning methods, this work reveals how the intricacies of local atomic arrangements and chemical heterogeneities influence diffusion processes, thereby offering new insights into alloy design and optimization. The research initially focuses on the vacancy-mediated diffusion employing binary Ni-Fe concentrated alloys as model systems. To evaluate the impact of local chemical short-range orders (SROs) on vacancy diffusion, both random solid solution configurations and alloys with SROs are prepared using hybrid molecular dynamics (MD) and metropolis Monte Carlo (MMC) methods. The results demonstrates that the development of SROs can significantly impede vacancy-mediated diffusion and enhance the chemically biased diffusion between Fe and Ni sites. Such findings suggest that the diffusion behavior in CSAs can be intricately controlled by adjusting the chemical ordering, a principle that could revolutionize alloy design strategies. Moreover, the study establishes a linear correlation between changes in the enthalpy of mixing and the formation of SROs, indicating that the reduction of enthalpy of mixing towards the more negative direction within an alloy system acts as a driving force for the observed diffusional slowdown. Advancing the methodological frontier, this dissertation introduces a state-of-the-art approach that integrates machine learning (ML) with kinetic Monte Carlo (KMC) simulations to efficiently investigate the controversial phenomenon of "sluggish diffusion" in concentrated alloys. As the first step, the Ni-Fe concentrated alloys are used as model systems. The complexity of defect diffusion in varying local atomic environment in CSAs makes it impractical to apply the standard nudged elastic band (NEB) method for on-the-fly determination of defect migration barriers at each step. By developing an artificial neural network (ANN) model trained on a dataset of NEB-computed migration barriers, it enables precise, efficient, and on-the-fly predictions of vacancy migration barriers for arbitrary local atomic environments during KMC simulations, including both random solution configuration and alloys with SROs. The diffusivities derived from this ANN-KMC modeling closely align with those from independent MD and temperature-accelerated dynamics (TAD) simulations at their accessible temperatures. The research delves into the sluggish diffusion mechanisms over the entire composition range of the Ni-Fe alloy system, elucidating them through the lens of ANN-KMC-derived insights at both high and low temperatures. The exploration then extends to quinary FeNiCrCoCu HEAs, utilizing a similar but improved ANN model to predict vacancy migration barriers across a wide compositional range. Due to the challenges of exploring the vast HEA compositional space, to date most experimental and computational studies have been limited to equiatomic compositions. This model, remarkably effective despite being trained solely on equiatomic HEA data, accurately predicts vacancy migration barriers in non-equiatomic compositions and their binary to quinary subsystems. Implementing this ANN model as an on-the-fly barrier calculator for KMC simulations, such ANN-KMC framework derives diffusivities nearly identical to the those from independent MD simulations but with far higher efficiency. This capability facilitates an extensive study of over 1,500 HEA compositions, uncovering the presence of sluggish diffusion in many non-equiatomic compositions. The analysis provides critical insights into the interplay between compositions, complex potential energy landscape, and percolation effect of the faster diffuser (i.e., Cu) on sluggish diffusion behaviors, offering invaluable perspectives for experimental alloy design and development. Lastly, the dissertation delves into interstitial-mediated diffusion in FeNiCrCoCu HEAs, confirming the presence of sluggish interstitial diffusion by comparing the equiatomic HEA with a range of reference systems. To study the non-monotonic concentration dependences in interstitial diffusion, a machine learning KMC (ML-KMC) method has been developed to simulate 〈100〉 dumbbell interstitial diffusion across various HEA compositions. Diverging from conventional KMC (C-KMC) and random sample KMC (RS-KMC) approaches, which approximate transition energies through a mean-field and random sampling methods, respectively, the ML-KMC predicts dumbbell formation energy on-the-fly based on local atomic configurations. This enables it to effectively replicate diffusion patterns from independent MD simulations. This novel ML-KMC approach offers a promising high-throughput method for studying HEAs, avoiding the expensive computational overhead associated with calculating dumbbell migration barriers. The impact of the percolation effect of faster diffusing elements (Cr, Cu) is also analyzed. Insights from this study can advance the understanding of compositional-dependent diffusion and provide valuable insights for the HEA design. Beyond the achievement of these completed works, two promising future projects have been evaluated that could significantly advance the field of diffusion research. The first initiative seeks to broaden the scope of the ANN-KMC framework, aiming to significantly enhance simulation efficiency across a broad range of HEA compositions. An accurate ANN model for predicting interstitial migration barriers has already been developed, and its full integration into the KMC framework could enable more accurate diffusion simulations. The second project aims to develop a comprehensive ML interatomic potential tailored specifically for HEAs, intended to improve the predictive accuracy of MD simulations. Although progress has been made in modeling an equiatomic CoCrFeMnNi HEA, constructing a robust ML potential for HEAs faces substantial challenges, primarily due to the extensive data requirements and computational demands.
  • Engineering Organoids for Stem Cell Maturation
    Gandhi, Neeti Nimish (Virginia Tech, 2024-12-06)
  • Design and Electrochemical Performance of Sodium-Based Batteries
    Zhang, Qipeng (Virginia Tech, 2024-12-06)
    Low-cost, high-performance energy storage solutions are in great demand for applications such as vehicle electrification and electricity generation from renewable sources. Lithium-based batteries have emerged as strong contenders due to their high energy density and stability. However, their reliance on scarce lithium reserves and high production costs makes them impractical for many applications. Sodium-based batteries (SBBs) are gaining traction as a more affordable option, with costs of $50 to $100 per kWh and an abundant resource base. Despite these advantages, SBBs still face many obstacles, primarily due to limited research on sodium-based chemistries. Additionally, sodium-based batteries have inherent limitations, including lower energy capacity and reduced cycle life, which restrict their viability for long-term use. This thesis addresses several critical challenges faced by SBBs and explores new strategies for enhancing their performance and viability for large-scale applications. First, a low-concentration, non-flammable electrolyte consisting of 0.3 M NaPF6 in a mixed solvent was formulated and tested in SBBs. This electrolyte significantly improves the cyclability and performance of SBBs across a wide temperature range, with high-capacity retention at both elevated and sub-zero temperatures. Molecular simulations reveal that the improved ion-pairing underpins the exceptional performance. This development addresses major challenges in SBBs by offering a safer, more cost-effective solution for large-scale applications. Second, sodium-sulfur (Na-S) batteries were explored to achieve high energy densities. An external acoustic field was implemented to enhance Na-S battery performance by inhibiting the shuttle effect and reducing dendrite growth, two key challenges in Na-S systems. This method offers a scalable, non-chemical solution to improve cycle life and efficiency, making Na-S batteries a more viable candidate for large-scale energy storage. This progress, along with the high theoretical capacity of Na-S batteries, helps address the limitations not resolved by the electrolyte engineering work of SBBs. Third, the mechanisms of Na2Sx (x≤2) precipitation in sodium-sulfur (Na-S) and sodium-oxygen-sulfur (Na/O2-S) systems were investigated. The results reveal that higher-order sodium polysulfides display the lowest current density, indicating a stronger driving force is needed to initiate their reaction. In Na/(O2)-S systems, the transition from high-order to low-order oxy-sulfur intermediates demands less energy compared to Na-S systems. The insights gained here help further optimize Na-S/Na/(O2)-S batteries to enhance their performance and cycle life. Together, the work in this dissertation addressed several critical needs in the development of SBBs and helped advance their commercialization.
  • A Statistical Methods-Based Novel Approach for Fully Automated Analysis of Chromatographic Data
    Kim, Sungwoo (Virginia Tech, 2024-12-04)
    Atmospheric samples are complex mixtures that contain thousands of volatile organic compounds (VOCs) with diverse physicochemical properties and multiple isomers. These compounds can interact with nitrogen oxides, leading to the formation of ozone and particulate matter, which have detrimental effects on human health. Therefore, it is essential to apply effective analytical methods to obtain valuable information about the sources and transformation processes of these samples. Gas chromatography coupled with mass spectrometry (GC-MS) is a widely used method for the analysis of these complex mixtures due to its sensitivity and resolution. However, it presents significant challenges in data reduction and analyte identification due to the complexity and variability of atmospheric data. Traditional processing methods of large GC-MS datasets are highly time-consuming and may lead to the loss of potentially valuable information from relatively weak signals and incomplete characterization of compounds. This study addresses these challenges. An automated approach is developed that catalogs and identifies nearly all analytes in large chromatographic datasets by combining factor analysis and a decision tree approach to de-convolute peaks. This approach was applied to data from the GoAmazon 2014/5 campaign and cataloged more than 1000 unique analytes. A novel method is then introduced to automatically identify quantification ions for single-ion chromatogram (SIC) based peak fitting and integration to generate time series of analytes. Through these combined approaches, a complex GC-MS dataset of atmospheric composition is reduced and processed fully automatically. Additionally, a machine learning-based dimensionality reduction algorithm was applied to the generated time series data for systematic characterization and categorization of both identified and unidentified compounds, clustering them into 8 distinct groups based on their temporal variation. These data are then used to generate fundamental insight into the atmospheric processes impact composition. This analysis aimed to elucidate the effects of meteorological conditions on these compounds, particularly the impact of wet deposition through precipitation scavenging on gas- and particle-phase oxygenated compounds. Hourly removal rates for all analytes were estimated by examining the impacts of precipitation on their concentration.
  • Comparative and Evolutionary genomics of Nucleocytoviricota
    Karki, Sangita (Virginia Tech, 2024-12-03)
    Viruses have been historically identified by their smaller sizes and simple genomic features compared to cellular life forms. Advances in virus cultivation and metagenomic analysis in recent years have shown that giant viruses, classified within the phylum Nucleocytoviricota, possess remarkably large genomes and complex structures, rivaling those of bacteria. Apart from their unusual genome and virion size, these nucleocytoviruses also encode Eukaryotic signature proteins (ESPs), including membrane trafficking proteins, cytoskeletal components, histones, ubiquitin signaling, and components of RNA and DNA processing proteins that are hallmarks of their eukaryotic hosts. Despite these intriguing findings, many groups of nucleocytoviruses remain underexplored. Similarly, their genomic complexity for example large genome size and encoded ESPs raise important questions about the role of nucleocytoviruses in the origins and evolution of eukaryotic hosts cells. In my work, I address this gap by performing comparative genomics and phylogenetic analysis to explore the genomics and evolutionary dynamics of giant viruses. In Chapter 1, I provide a literature review on giant viruses, their history, and their evolutionary links with eukaryotes. This chapter establishes the necessary background for the subsequent chapters. In Chapter 2, I perform comparative genomics, phylogenetics, and environmental distribution analysis to provide insights into the genomes and biogeography of the members of Asfarviridae family in the Nucleocytoviricota. In this chapter, I show that these viruses are widespread in the ocean, they have genes involved in different metabolic processes, and the members within this family have broad genomic diversity. In Chapter 3 and 4, I perform comprehensive phylogenetic analysis to uncover the co-evolutionary dynamics of nucleocytoviruses and eukaryotes. In Chapter 3, I focus on the vesicular trafficking and transport, ubiquitin system, and cytoskeleton system proteins to uncover complex patterns of gene exchange. My findings reveal that these proteins were acquired relatively recently by viruses, and in some cases multiple times independently suggesting that these genes might be important for countering the host changing environments and immune defenses. Similarly, in chapter 4, I focus on the replication and transcriptional machinery to study the ancient co-evolutionary dynamics of the virus and its host. My findings show that the DNA polymerase, especially the eukaryotic delta polymerase, a key processive polymerase required for genome replication in all eukaryotes, clusters adjacent to an ancient viral clade. The viral enzymes forming deep-branching clades adjacent to eukaryotic lineages, suggests their origin predates the Last Eukaryotic Common Ancestor (LECA). The replication and transcription machinery needed for viroplasm hints at an ancient virosphere with relics from extinct proto-eukaryotic lineages. Overall, these studies highlight the ancient as well as recent gene acquisition patterns between nucleocytoviruses and the hosts and provide valuable insight into the coevolutionary dynamics of these groups.
  • Exploring Phosphorus Dynamics in Mid-Atlantic Soils: A Multi-Scale Analysis Integrating Soil Fertility and Land Management for Environmental Sustainability
    Badon, Thomas Beauregard (Virginia Tech, 2024-12-03)
    The legacy phosphorus (P) in the Eastern Shore of Virginia poses significant challenges for crop nutrition and water quality. Nutrient losses from row crop agriculture and poultry litter applications have potential to cause water quality impairments affecting the environment, aquaculture, and tourism industries. To address these concerns, this study investigated P management strategies across various scales. The first component of the study focused on optimizing edamame production in the context of high legacy soil P levels and harvest efficiency. Over three years, field experiments on Bojac sandy loam soil assessed the effects of different P fertilizer rates and legacy P levels on edamame yield, biomass, and P uptake. Results showed that short-season edamame in high legacy P soils had significantly more yield than long-season varieties. However, additional P fertilization was deemed unnecessary for soils with P concentrations above 21 kg P ha-1, as current edamame P recommendations exceed the crop's P removal needs. Moreover, mechanical harvesting efficiency was notably higher for short-statured edamame varieties (89.3%) compared to tall varieties, indicating their preference for improved harvesting. The second component examined the influence of agricultural lime on legacy P phases in the soil. Lime was applied at rates ranging from 0 to 2690 kg ha-1 to an acid sandy loam Ultisol (pH < 5.1). Using partial Hedley P fractionation, changes in water-soluble P, soil test P (Mehlich-1 extraction), and total soil P (nitric acid digest) were monitored. Although lime application significantly affected soil pH, calcium (Ca), and magnesium (Mg), it did not significantly alter the relative proportions of water-soluble and soil test P. This indicates that while lime can improve soil pH and nutrient availability, it does not substantially impact P phase distribution. The final study utilized historical water quality data from the Virginia Institute of Marine Sciences and GIS technology to analyze the impact of land use and land cover (LULC) on nitrogen (N) and P concentrations in 52 watersheds. Row crop LULC was significantly correlated with higher total nitrogen (TN) concentrations (p = 0.03), while forested LULC was linked to lower TN (p = 0.02) and nitrate-nitrite (NOx) concentrations (p = 0.05). Thirty-two out of 52 watersheds had mean total P concentrations exceeding 0.10 mg L-1, with stormflow conditions showing significantly higher total P concentrations and loadings compared to baseflow. Landscape-scale turbidity strongly correlated with elevated total P levels, emphasizing the role of particulate P transport. Baseflow samples also had higher ammonia (NH3) and NOx concentrations, but stormflow resulted in higher loadings. In conclusion, effective P management on the Eastern Shore requires a coordinated approach that addresses soil, crop, watershed, and landscape-scale factors in cooperation with multiple stakeholder groups. This study highlights the importance of optimizing agronomic practices and implementing targeted conservation strategies to mitigate nutrient and sediment losses, thereby improving both crop production and environmental quality.
  • Enhanced Feature Representation in Multi-Modal Learning for Driving Safety Assessment
    Shi, Liang (Virginia Tech, 2024-12-03)
    This dissertation explores innovative approaches in driving safety through the development of multi-modal learning frameworks that leverage high-frequency, high-resolution driving data and videos to detect safety-critical events (SCEs). The research unfolds across four methodologies, each contributing to advance the field. The introductory chapter sets the stage by outlining the motivations and challenges in driving safety research, highlighting the need for advanced data-driven approaches to improve SCE prediction and detection. The second chapter presents a framework that combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) with XGBoost. This approach reduces dependency on domain expertise and effectively manages imbalanced crash data, enhancing the accuracy and reliability of SCE detection. In the third chapter, a two-stream network architecture is introduced, integrating optical flow with TimeSFormer with a multi-head attention mechanism. This innovative combination achieves exceptional detection accuracy, demonstrating its potential for applications in driving safety. The fourth chapter focuses on the Dual Swin Transformer framework, which enables concurrent analysis of video and time-series data, this methodology shows effective in processing driving front videos for improved SCE detection. The fifth chapter explores the integration of corporate labels' semantic meaning into a classification model and introduces ScVLM, a hybrid approach that merges supervised learning with contrastive learning techniques to enhance understanding of driving videos and improve event description rationality for Vision-Language Models (VLMs). This chapter addresses existing model limitations by providing a more comprehensive analysis of driving scenarios. This dissertation addresses the challenges of analyzing multimodal data and paves the way for future advancements in autonomous driving and traffic safety management. It underscores the potential of integrating diverse data sources to enhance driving safety.
  • Knowledge-guided Machine Learning for Sensor-based High-Performance Autonomous Material Characterization
    Zhang, Junru (Virginia Tech, 2024-12-02)
    Knowledge-guided machine learning enables sensor-based high-performance material characterization that drives accelerated materials discovery and manufacturing. Traditional materials discovery workflows are driven by low-throughput characterization processes that involve several manual sample preparation steps and require relatively large amounts of material. While automated material dispensing processes now provide the ability to automate the synthesis of materials, the characterization of material composition, structure, and properties remains challenging due to the lack of reliable high-throughput characterization methods. Commercial benchtop characterization instruments are gold standards for characterizing the composition, structure, and properties of materials but lack synergy with state-of-the-art accelerated materials discovery workflows, which are based on miniaturized transducers for material testing (e.g., sensors), automation, and low-volume test formats. Due to the time- and resource-intensive nature of experimentation and the limited budget imposed on autonomous experimentation workflows in practical applications, the data generated from accelerated material discovery workflows are usually sparse and imbalanced, challenging the construction and training of machine learning models. In this dissertation, we create knowledge-guided machine learning models to support sensor-based high-performance autonomous material characterization. Several different types of knowledge-guided machine learning models were established for high-performance sensor-based characterization of material composition and phase. Specifically, three new methodologies are proposed and developed: 1. A new rapid and autonomous high-performance characterization method for accelerated engineering of soft functional materials is proposed to overcome the challenge of low-throughput characterization and manual data analysis. The proposed method is compatible with state-of-the-art material synthesis platforms combining automated sensing and sensor physics-guided machine learning that reduces the characterization cycle time and improves the material phase classification accuracy. Utilizing domain knowledge of measurement processes that generate data (e.g., sensor physics) and thermodynamics that govern material phase for feature engineering improved model and process performance. 2. To help mitigate the challenge of low measurement confidence associated with material composition measurement using biosensors, a novel knowledge-guided machine learning approach that integrates domain knowledge in sensor chemistry and physics is proposed. The proposed method implements data augmentation techniques to address sparsity and imbalance of biosensor data and identified new features in biosensor time-series data that are predictive of target analyte concentration and probability of false positive and negative responses. 3. A novel deep learning model with knowledge-guided cost function supervision is proposed to improve biosensor performance, specifically to improve the classification of false responses and reduce biosensor time delay. This new methodology combines regression- and classification-based data analyses, significantly improving biosensor accuracy and speed. The method fuses theory that governs dynamic sensor response (i.e., data generation) with machine learning models to guide regression and classification tasks, providing improved model interpretability and explainability. With the advancement of knowledge-guided machine learning and sensing technologies, the performance of experimental tools and processes for accelerated materials discovery and manufacturing applications can continue to be improved, particularly with respect to speed and reliability, which are critical performance attributes for future industrial adoption.
  • Laminar and turbulent convective heat transfer over bodies at an angle of attack
    Tai, Tsze Cheng (Virginia Polytechnic Institute, 1968)
  • The dynamics of an ultrasonic cavitation bubble
    Okereke, Chinenyenwa, A. (Virginia Polytechnic Institute and State University, 1979)
  • Electrostatic oscillations in inhomogeneous plasmas
    Staton, Leo Douglas (Virginia Polytechnic Institute, 1968)
  • Contributions to Lanchester combat theory
    Springall, Anthony (Virginia Polytechnic Institute, 1968)
  • Physical and chemical modifications of certain soils by calcium hydroxide treatment
    Pettry, D. E. (Virginia Polytechnic Institute, 1968)
  • The formation and chemistry of certain heterocyclic dianions
    Rogers, Tommie Gene (Virginia Polytechnic Institute, 1968)
  • Metabolic derangement in response to ingestion of imbalanced amino acid mixtures
    Soliman, Abdel-Gawad Mohamed (Virginia Polytechnic Institute, 1967)
  • On the damping of flexural plate vibrations by the application of viscoelastic layers
    Shelley, Philip Eugene (Virginia Polytechnic Institute, 1967)
  • Isothermal flow of Newtonian fluids through conical packed beds of uniform spheres
    Schildcrout, Sigmund Albert (Virginia Polytechnic Institute, 1967)