UC Merced

Catalysis lies at the heart of modern chemical synthesis, underpinning the efficient production of pharmaceuticals, agrochemicals, and advanced materials. As the global scientific community increasingly prioritizes sustainability, there is a growing necessity to move away from catalytic systems that rely on precious metals such as iridium, rhodium, and palladium, elements that are not only scarce and expensive but also associated with significant environmental and supply-chain concerns. Homogeneous and organocatalytic approaches offer a compelling alternative for reducing the ecological footprint of chemical processes. In this context, Earth-Abundant transition metals and main-group elements have emerged as powerful platforms for sustainable catalysis. Despite their enormous potential, their widespread adoption remains hampered by several unresolved challenges, including susceptibility to side reactions, air- and moisture-sensitivity requirements, high catalyst loadings, and harsh reaction conditions. Moreover, the mechanistic landscapes governing these catalytic cycles remain considerably underexplored compared to their precious metal counterparts, limiting the rational design of improved systems. Against this backdrop, this dissertation explores sustainable homogeneous and organocatalytic strategies employing Earth-Abundant elements (manganese, iron and silicon) for three synthetically valuable transformations: borylation, hydroboration, and hydrodefluorination. Borylation and hydroboration target boronate esters, high-value synthetic intermediates widely utilized in pharmaceutical synthesis, agrochemistry, and cross-coupling reactions. Hydrodefluorination, on the other hand, enables the selective synthesis of fluorinated arenes, privileged structural motifs in drug discovery and medicinal chemistry. Beyond their synthetic value, hydrodefluorination reactions carry broader environmental significance, as selective C–F bond cleavage offers a promising strategy toward the remediation of per- and polyfluoroalkyl substances (PFAS). The first chapter of this dissertation presents the first manganese-catalyzed chemoselective C(sp)–H borylation of terminal alkynes, employing [Mn(SiNSi)Cl2] (SiNSi = 2,6-[EtNSi(NtBu)2CPh]2C5H3N), as the precatalyst. Mechanistic studies reveal that the choice of activator critically governs the reaction pathway, dictating chemoselectivity between C–H activation and alkyne insertion. Uniquely, HBPin serves a dual role as both the borylating reagent and an in situ activator, directing the catalyst toward the C–H activation pathway and the selective formation of alkynylboronate esters. This chapter thus underscores the pivotal importance of mechanistic understanding in rationalizing and controlling catalytic outcomes. The second chapter describes the first use of silicon as an organocatalyst for C–B bond formation, employing commercially available triethoxysilane for the chemoselective single and sequential hydroboration of terminal alkynes. This transformation affords alkyl gem-diboronate esters, attractive precursors in drugs containing 3D non-flat motifs. Notably, while silicon-catalyzed hydrofunctionalization reactions reported in literature require the silicon center to be activated as a strong Lewis acid, this work demonstrates that a non-Lewis acidic silicon source is fully competent for catalysis, representing a significant conceptual advance in main-group organocatalysis. Mechanistic insights implicate non-covalent interactions between the multiple bonds of the substrate and the silicon catalyst as key contributors to reactivity. This mechanistic insight directly guided catalyst improvement, wherein a fluorinated analogue of triethoxysilane was identified as a superior catalyst, delivering nearly twice the efficiency of the parent system. The third chapter extends the application of triethoxysilane as a universal catalyst for the hydroboration of a broad range of unsaturated substrates, including alkenes, nitriles, ketones, and esters. Unlike the alkyne hydroboration described in the preceding chapter, mechanistic studies reveal that the electronic and steric properties of heteroatom-containing aromatic substrates exert distinct effects on catalyst efficiency, highlighting substrate-dependent mechanistic divergence. Notably, a fundamentally different mechanistic pathway is operative for the hydroboration of nitriles, involving a catalytically active species distinct from triethoxysilane itself. The final chapter presents the first bench-stable iron catalyst, [Fe(tBubipy)(OTf)₂] (tBubipy = 4,4'-di-tert-butyl-2,2'-bipyridine; OTf = CF₃SO₃⁻), for the hydrodefluorination of fluoroarenes. The catalyst demonstrates remarkable efficiency, achieving a turnover number (TON) of 31.6 for 2,3,5,6-tetrafluoroanisole at room temperature. The extended catalytic lifetime is attributed to the robust five-membered chelate formed by the tBubipy ligand, which stabilizes the catalyst resting state and prevents deactivation through ligand decoordination. At elevated temperatures, the system exhibits enhanced performance, facilitating up to five sequential hydrodefluorination cycles for hexafluorobenzene, underscoring its potential for the exhaustive defluorination of highly fluorinated substrates, a capability of direct relevance to sustainable strategies for PFAS remediation.

Understanding the relationship between enzyme structure and function is a central goal in structural biology, with important implications for enzyme design, drug discovery, and biotechnology. In this dissertation, we investigate the role of structural flexibility and dynamics in enzyme catalysis using engineered enzymes as model systems and advanced X-ray crystallography techniques.Our results demonstrate that optimal catalytic function arises from a balance between rigidity and flexibility. In relatively simple, single-step reactions, increased rigidity can enhance catalysis by preorganizing the active site to closely match the transition state geometry, thereby reducing the need for conformational rearrangement. In contrast, more complex, multi-step reactions require enzymes to sample multiple conformational states to accommodate substrates, intermediates, and products throughout the catalytic cycle. In these cases, conformational flexibility becomes essential for efficient function. Through structural and comparative analyses of evolved enzyme variants, we show how directed evolution modulates the conformational landscape of enzymes, enabling expansion or reshaping of the active site and selectively enriching catalytically productive states. Additionally, time-resolved and room-temperature crystallographic approaches provide insight into non-equilibrium conformations and transient states that are not accessible through conventional cryogenic methods. Together, these findings provide a unified framework for understanding how enzymes balance rigidity and flexibility to achieve efficient catalysis, and highlight the importance of dynamics in shaping enzyme function.

Accurate characterization of fine particulate matter (PM2.5) exposure remains a critical challenge in air quality science, particularly in regions with complex emission sources and limited regulatory monitoring coverage. Traditional regulatory monitoring networks may fail to capture local variability due to their limited number and distribution of monitors because of their higher cost. This dissertation addresses these limitations by integrating information from community-based low-cost air quality monitoring, advanced spatiotemporal statistical modeling, considering socioeconomic status demographics, and global fusion model evaluation to improve understanding of PM2.5 exposure patterns in California’s San Joaquin Valley. Chapter 2 evaluates a dense network of low-cost air quality sensors deployed across Fresno County by comparing observations from community monitors with regulatory monitors. Using spatial interpolation techniques, this analysis identifies periods of elevated PM2.5 concentrations that were not detected by nearby regulatory monitors, demonstrating that low-cost sensors can capture fine-scale variability in air pollution. Chapter 3 applies Bayesian hierarchical spatiotemporal models to examine the relationship between PM2.5 concentrations and socioeconomic and demographic characteristics across the San Joaquin Valley from 2021 to 2023. Results indicate that higher PM2.5 levels are disproportionately concentrated in census tracts with greater socioeconomic disadvantage and higher proportions of residents of color. Comparisons with communities designated under California’s Assembly Bill 617 reveal substantial overlap with identified pollution hotspots, while also identifying additional high-burden areas outside current boundaries used to define high-risk communities. These findings underscore the importance of high-resolution monitoring and modeling approaches for informing equitable air quality management and environmental justice policy. Chapter 4 investigates how low-cost sensor data compares to exposure modeling frameworks, influences estimates of PM2.5 concentrations, and the resulting Air Quality Index (AQI) across California. By comparing model outputs with community monitoring data, this study demonstrates that low-cost sensors improve the spatial representation of pollution. These improvements have important implications for public health communication, as more accurate AQI estimates can better inform individual and community-level decisions regarding air quality exposure. Together, these three studies demonstrate that community-based monitoring networks can serve as both a scientific tool and resource for impacted residents, enhancing exposure assessment, improving model performance, and describing environmental inequities.

Rational design of molecules for chemical transformations and optoelectronic devices requires an understanding of excited state electronic transitions and the pathways connecting stable conformers on potential energy surfaces (PES). In this work, we demonstrate how computational methods like PES scans can be used to elucidate and design new reagents for chemical transformations. We also show how to use photophysical characterization of compounds, like charge transfer (CT), oscillator strengths, and absorption profiles can be used to design and chemically tune optical properties. We then show how Δ-SCF methods can be used to model CT states qualitatively using difference densities and quantitatively with CT distance metrics against methods like TD-DFT. Lastly, a new method for transition state (TS) optimization is developed and evaluated on complex topologies like the Müller-Brown and Schlegel surfaces.

Growth mixture models (GMMs) constitute a versatile statistical framework for capturing population heterogeneity in longitudinal data by identifying latent classes of growth trajectories. Since their introduction in 1999, the models have been extended and disseminated through sustained methodological advancements, yet uncharted gaps remain in their implementation. This dissertation develops and evaluates advanced Bayesian methods to address two such gaps, predictor selection and missing data handling, across two studies. In Study 1, I focused on predictor selection in conditional GMMs. Beyond the usefulness of GMMs in describing heterogeneous growth patterns, predicting these patterns offers an enhanced understanding of substantive phenomena. However, the question of how best to select important predictors has remained unaddressed in the GMM literature. As a principled approach to this issue, I proposed Bayesian variable selection via shrinkage priors and evaluated seven priors (ridge, lasso, hyperlasso, elastic net, horseshoe, regularized horseshoe, and spike-and-slab) through a Monte Carlo simulation. Results showed that the horseshoe, regularized horseshoe, and spike-and-slab offered the best balance between detecting true nonzero predictors, shrinking small effects, and controlling false selections. In addition, the regularized horseshoe, spike-and-slab, and horseshoe achieved superior predictive accuracy, though the regularized horseshoe and spike-and-slab came at the cost of lower convergence rates. The ridge prior showed limited effectiveness in selecting important predictors and high false selection rates. Practical recommendations are provided for choosing shrinkage priors, with caveats and other implementation considerations. In Study 2, I addressed missing data in GMMs under attrition scenarios common in longitudinal research. Existing missing data techniques either discard incomplete cases, require the number of latent classes to be pre-specified as part of the missing data treatment, or ignore the population heterogeneity. To overcome these limitations, I developed a Bayesian nonparametric multiple imputation approach using the Chinese restaurant process (MI-CRP). This new approach retains existing values, imputes missing values, and accounts for population heterogeneity without requiring the number of classes to be predetermined. A Monte Carlo simulation compared MI-CRP with four existing methods, including full information maximum likelihood, Bayesian estimation, multiple imputation via joint modeling, and multiple imputation via fully conditional specification. Results showed that MI-CRP adequately recovered the growth factor means and avoided extreme covariance bias. MI-CRP also achieved higher convergence and valid replication rates at low class separation with smaller sample sizes, and higher classification accuracy with less variability under challenging conditions. Overall, the advanced Bayesian methods developed and evaluated in this dissertation fill unresolved methodological gaps in GMMs and equip applied and methodological researchers with principled tools for implementing these models.

Online harassment, hate speech, and misinformation pose persistent challenges for digital platforms. As centralized moderation faces well-documented limits in scale and accuracy, platforms increasingly turn to community-based systems that mobilize ordinary users to identify and correct harmful content. Yet what makes such systems effective remains poorly understood. This dissertation examines community-based content moderation across three levels of analysis: platform, individual, and collective. It draws on archival data from X’s Community Notes (1.4 million notes posted between 2021 and 2025) and a controlled experiment on EatSnap.Love, a simulated social media platform (N = 137).Chapter 3 investigates how platform design parameters shape bystander intervention. Drawing on bystander intervention theory, it theorizes how disapproval channel availability and reporting effort jointly structure participation, and tests this framework using a 2 × 3 × 2 × 2 between-subjects experimental design. Contrary to predictions from offline bystander theory, higher procedural effort increased rather than reduced reporting, suggesting that effort functions as a commitment filter rather than a uniform deterrent. Disapproval channel availability broadened engagement, and rules reminders moderated the selective effect of effort. Chapter 4 examines personality differences using the Big Five framework. Integrating LIWC-22 linguistic analyses of 2,197 Community Notes contributors with self-reported personality data from the experimental sample, the chapter identifies a systematic engagement–quality gap: Openness predicted who actively intervened, but Conscientiousness predicted whose contributions achieved cross-partisan helpful status. Extraversion and Neuroticism were negatively associated with quality, indicating that the traits motivating intervention differ from those producing effective corrections. Chapter 5 analyzes the linguistic properties of corrections through sentiment analysis, LDA topic modelling, and fractional logit regression on 8,941 tweet-note pairs. Notes adopted a verification-oriented vocabulary and were consistently more negative than the tweets they addressed. Crucially, the degree of shared agreement among note authors that the original tweet was misleading dominated all linguistic predictors of helpfulness, suggesting that effective collective correction depends on shared evaluative consensus rather than stylistic features of the correction itself. Together, these findings advance a design-contingent account of community-based moderation in which platform design, individual differences, and shared evaluative judgment operate as selective conditions determining who recognizes harm, who acts effectively, and when collective correction succeeds.

Metabolic dysfunction-associated steatotic liver disease (MASLD) affects more than 30% of the global population and primarily afflicts individuals with metabolic syndrome (MetS) due to their overlapping risk factors. MetS is defined as the presence of three or more of the following risk factors: (1) abdominal obesity, (2) atherogenic dyslipidemia, (3) hyperglycemia with or without insulin resistance, (4) hypertension, (5) proinflammatory state, and (6) prothrombotic state. Currently, there is no FDA-approved treatment for MASLD; therefore, the first line of non-pharmaceutical defense is caloric restriction (CR). However, failure to maintain a caloric restriction diet among obese individuals is extremely high, resulting in body mass regain (BMr). BMr following CR has been shown to exacerbate impaired lipid metabolism by inducing excess hepatic fat accumulation and causing an imbalance between lipid availability and its utilization. This impairment in hepatic lipid metabolism may induce MASLD. Subsequently, the accumulation of excess lipids in the liver promotes the production of reactive oxygen species (ROS) through a disequilibrium in redox signaling. This shift catalyzes the transition between MASLD to a progressive onset of metabolic dysfunction-associated steatohepatitis (MASH). The benefits of CR on MetS and MASLD are well established; however, the literature lacks focused studies on the pathways associated with lipid metabolism and redox signaling after BMr. In this dissertation, the Otsuka Long Evans Tokushima Fatty (OLETF) rat was used as a model of MetS due to its similarities to the human conditions. This model was used to investigate the effects of partial and complete BM regain following caloric restriction on hepatic lipid metabolism, redox balance, and thyroid hormone signaling during MetS. We demonstrated that: (1) partial BMr induced hepatic fat accumulation via elevated triglycerides with saturated fatty acids, downregulated fatty acid oxidation proteins, and reversed the benefits induced by CR suggesting that even partial regain of BM (PR) can promote MASLD, (2) PR impairs TH signaling, uncoupling TH signaling and its regulation of hepatic lipid metabolism via a reduction in TH receptor beta (THRβ), associated with changes in the hepatic lipidome (ACars, CLs, LPCs, PCs, and PIs), all of which induce the progression of MASLD to MASH by increasing hepatic injury, and (3) complete BM regain during early Mets increased 4-hydroxenenol (4HNE) despite elevated catalase (CAT) activity demonstrating that complete regain induced oxidative injury associated with impaired hepatic lipid metabolism. Collectively, this dissertation provides insight into the detriments of BMr (complete or partial) following CR on hepatic lipid metabolism, oxidative injury, and TH signaling during MetS.

Individuals often face decisions in which the consequences of their actions for others are uncertain, and in many cases this uncertainty is actively maintained. A growing literature documents that individuals avoid information about the social consequences of their choices, a behavior commonly referred to as strategic ignorance. This dissertation argues that such ignorance is not merely an individual choice, but a socially embedded phenomenon shaped by interactions with others, internalized norms, and beliefs about the behavior of others. Using a series of experiments based on moral wiggle room environments, I examine how different features of the social context influence both the willingness to acquire information and the behavioral consequences of remaining ignorant. The results reveal a striking asymmetry: while many individuals avoid acquiring information themselves, others are readily willing to provide it. When information is imposed, a substantial share of decision-makers revise their choices in a more prosocial direction, demonstrating that ignorance—and its behavioral consequences—can be constrained by others. At the same time, prompting individuals to consider what constitutes appropriate behavior prior to making a decision increases prosocial actions, consistent with the activation of latent norms, whereas eliciting such beliefs after decisions instead reflects post-hoc justification. In addition, individuals respond to information about the behavior of others by aligning both their information acquisition and allocation choices with perceived norms. Taken together, these findings provide a unified account of moral decision-making in which behavior is shaped by a layered system of enforcement, ranging from external interventions to fully internalized regulation. The results highlight that the key determinant of behavior is not only what individuals know, but how the social environment shapes what they choose not to know.

This dissertation investigates the impact of organic matter amendments (OMAs), with a focus on compost, on urban soil health and carbon storage. This research addresses key questions surrounding the impacts of OMAs not only in urban areas, but on soils generally, over month to decade timescales on soil biogeochemistry and management-relevant soil characteristics. Chapter two is a meta-analysis, focusing on how soil organic matter (SOM) and soil nutrients, such as nitrogen (N) and phosphorus (P), respond to different kinds of OMAs. We found that of the OMAs tracked – biochar, biosolids, and compost – biochar increased SOM% the most, by 6.5 percentage-points, while compost increased SOM% by 3.1. While biosolids saw the lowest SOM gains at 2.1, they had significantly greater gains in soil N and P. This chapter highlighted the urban OMA work done to this point, but stressed the importance of long-term and deep soil sampling, as most studies were 30cm or shallower and were monitored for less than three years, both of which informed our future work, particularly chapters three and four.Chapter three examines the short-term impacts of compost application on turfgrass soil biogeochemistry. A three-month greenhouse study was conducted to assess how recycled water and compost additions impact soil greenhouse gas emissions and plant growth. We found compost increased root biomass by over 50%, while recycled water increased aboveground biomass by up to 300%. These changes in biomass were partially driven by changes in available P and N, which significantly increased with compost and recycled water respectively.These results compliment chapter four, which delves into the multi-annual effects of compost application. We conducted a two-year field trial assessing soil greenhouse gas emissions and bulk soil properties over two annual compost applications. We found a no changes to soil C or N, potentially due to high decomposition rates of the applied compost, evident by an increase of 10% in cumulative CO2 emissions from our compost plots. Compost plots also saw reduced N2O emissions, important for whole system nutrient budgets. However, this study highlighted the need to monitor compost application on longer timescales, leading to our chapter four project.Chapter five dives into the long-term impacts of compost application in urban areas, specifically looking at changes in soil C to one meter and in different C fractions. We found bulk soil C to be increased by 50% with long-term compost additions, especially in the top 10cm and 75-100cm depth ranges. This increase was consistent in the mineral-associated fraction as well, which is thought to be slower to change and contain highly persistent soil C. In addition, the molecular makeup of this mineral fraction became more similar to compost, indicating the direct incorporation of compost C into even deep mineral C pools.In conclusion, we found significant gains in whole profile bulk soil C and soil C persistence with long-term compost application, and that short-term compost application can reduce N2O emissions while improving turfgrass growth.

Soft corals in the Xeniidae family pulse in an energetically expensive manner to generate fluid flow, which benefits them biologically. This flow has been modeled using computationally intensive numerical methods, such as the Immersed Boundary Method, which limits the ability to explore a wide parameter space. To address this challenge, we introduce Gaussian Process Regression as a surrogate model to capture the kinematic and flow outcomes of these simulations. By training on a limited set of simulations, the surrogates enable accurate predictions across a broader range of parameters than those originally sampled. These surrogate models can then be optimized to identify the parameter combinations which maximize fluid flow. The resulting optimal parameters can further guide additional numerical simulations, creating an iterative framework for targeted exploration of the parameter space. This approach offers new insights into how the coral polyp musculature and local flow conditions influence the resulting kinematics and fluid transport while substantially reducing computational cost.