71446 research outputs found
Sort by
Quantifying the Health Impacts of Air Pollution: Methods for Causal Exposure–Response Estimation and Policy-Relevant Evidence
No full textAmbient fine particulate matter (PM2.5) remains one of the most consequential environmental risks to human health worldwide, contributing to millions of premature deaths each year. Yet determining how mortality risk changes across the full PM2.5 exposure range—particularly at low concentrations where regulatory decisions are most sensitive—remains a fundamental scientific and methodological challenge. Observational air pollution data are high-dimensional and subject to complex confounding structures, spatial heterogeneity, and model misspecification. Recovering credible causal exposure--response functions (ERFs) in this setting requires methods capable of flexibly addressing heterogeneous effects, nonlinear relationships, and uncertainty in both exposure and outcome models. This dissertation develops, evaluates, and applies such methods to strengthen the evidence base for air-quality regulation and policy-relevant health-impact assessment in the United States.
The overarching theme of this dissertation is connecting rigorous statistical methods with meaningful public health and policy insight. First, I evaluate the performance of widely used ERF estimators and synthesize guidance for when different approaches are most appropriate. Second, I introduce a new causal inference method designed to address a pervasive but under-recognized source of bias—local confounding—that arises when the strength and type of confounding vary across the exposure distribution. Third, I demonstrate how these methodological advances can be applied to a real policy context through a transparent, reproducible health-impact assessment of proposed energy infrastructure. Across all three aims, I emphasize design-based workflows, principled diagnostics, and reproducibility to support robust inference in settings where regulatory stakes are high.
Chapter 1 addresses a longstanding gap in understanding which statistical methods reliably estimate ERFs under realistic confounding and exposure–outcome structures. I compare seven commonly used ERF estimators across a comprehensive set of simulation scenarios that vary the true ERF shape, the confounding mechanism, the degree of effect heterogeneity, and sample size. These include traditional regression models (linear, spline-based, and threshold) and design-based causal estimators that use entropy balancing or generalized propensity score matching. Two key insights emerge. First, regression-based ERFs can exhibit substantial bias when confounding is nonlinear or heterogeneous, even when the resulting curves appear smooth and precise. Second, design-based causal estimators that explicitly balance covariates across the exposure distribution tend to be more robust, particularly with large sample sizes. Applying all methods to a national cohort of more than 68 million Medicare beneficiaries reveals a distinctly nonlinear ERF: mortality risks rise steeply at lower PM2.5 concentrations and attenuate at higher levels. This chapter concludes with concrete methodological recommendations and fully reproducible code to support adoption.
Chapter 2 fills a critical methodological gap by providing a causal inference framework specifically tailored to settings where confounding varies across the exposure distribution. I introduce REBEL (Rolling Entropy Balancing for Exposure--response functions under Local confounding), a new design-and-analysis pipeline for continuous exposures. REBEL (i) constructs overlapping exposure windows, (ii) achieves covariate balance within each window through entropy balancing, (iii) calibrates each window to the full target population to recover population-level effects, and (iv) aggregates local estimates via an overlap-aware meta-estimator. I also develop diagnostics for detecting local confounding and propose a counterfactual cross-validation approach for tuning algorithmic parameters. In simulations, REBEL consistently outperforms existing ERF estimators when local confounding is present and remains competitive when it is not. Applied to 68.5 million Medicare beneficiaries, REBEL uncovers a steep, supralinear increase in all-cause mortality at low exposures to coal-derived PM2.5 —a pattern masked by global models—suggesting that traditional approaches may materially understate coal’s health burden. This chapter also derives a coal-specific exposure–response function that explicitly adjusts for potential local confounding, providing one of the first flexible, population-based ERFs for coal-derived PM2.5 in the literature and demonstrating how source-specific toxicity can be estimated with improved causal validity.
Chapter 3 demonstrates how these methodological tools can be translated into actionable evidence through a rigorous, policy-relevant assessment of PM2.5 impacts from a proposed 2,200-MW natural-gas combined-cycle plant in Colleton County, South Carolina. The analysis integrates source-specific emissions estimation, reduced-complexity atmospheric dispersion modeling (InMAP), population-weighted exposure assessment, environmental justice profiling, and health and economic valuation using U.S. EPA tools. Under conservative assumptions, the plant would expose more than 2.09 million people across South Carolina and Georgia to measurable increases in annual PM2.5, with the highest burdens concentrated in nearby census tracts characterized by lower incomes, lower property values, and higher proportions of Black residents. Estimated health damages reach up to $27.9 million annually, rising further under higher-capacity operating scenarios. Sensitivity analyses reveal that operational decisions (e.g., capacity factor) have substantially greater influence on community exposure than modest variations in design parameters such as stack height. This chapter provides a transparent, reproducible template for early-stage health-impact assessment of energy facilities.
Taken together, these chapters (i) provide principled guidance for choosing among ERF estimators and diagnosing when causal designs are needed, (ii) introduce a new method that addresses locally varying confounding in continuous-exposure settings, and (iii) demonstrate how causal evidence can be scaled to evaluate community-level risks from proposed energy infrastructure. By bridging methodological rigor with policy relevance, this dissertation advances the tools needed to credibly quantify the health impacts of air pollution and to inform air-quality and energy decisions.Biostatistic
Navigating Digital Worlds: Empirical Studies of Choice and Behavior in Sociotechnical Systems
No full textThis dissertation empirically examines the nature and consequences of three types of choices that individuals, firms, and communities make (or might make) against the background of complex sociotechnical environments, new technologies, and competing goals.
First, in Chapter 1, I present research about consumer choice and price comparison behavior in the context of the market for ridesharing services Uber and Lyft. Combining several novel data sources along with benchmark evidence from existing literature, the work calibrates a simple sequential search model to benchmark observed search behavior against theoretical predictions. The work finds that consumers compare prices substanially less than canonical models would predict given observed levels of price dispersion and benchmark estimates of consumer search costs. While the individual benefits of searching are modest, the aggregate implications are large; in a back-of-the-envelope calculation, we find that New York City-based rideshare customers collectively leave over \$300 million per year on the table by not comparing prices (about 6\% of platforms' gross booking volume), illustrating how small frictions can have substantial aggregate implications for the distribution of surplus in digital markets.
Second, in Chapter 2, I present research that studies conflict and disagreement in online conversations. The study develops and experimentally tests a novel AI-based mediation tool designed to facilitate constructive online dialogue across political divides. Powered by a large language model (LLM), the tool automates mediation interventions grounded in communication and conflict resolution principles such as paraphrasing, identifying agreement, and encouraging perspective-taking. In a randomized controlled trial, the system successfully generated context-sensitive interventions in contentious conversations; however, effects on participants' attitudes towards people they disagree with were limited. The findings highlight both the promise and the challenges of using LLMs to promote healthier online discourse at scale. Note that results presented in the present chapter are based on a partial sample of data from the study; future drafts of this work will include results from the full sample of data.
Finally, in Chapter 3, I present emerging research that studies workers' beliefs about the future labor market impact of emerging digital technologies, with a focus on AI and large language models (LLMs). As AI tools like LLMs become increasingly capable, workers must make decisions about how to respond. Importantly, these decisions are shaped not only by the actual rate and direction of technological change, but also by workers' beliefs about these future trajectories. Importantly, these beliefs may differ across workers and may not align with expert forecasts about the likely impact of AI, while nonetheless shaping economic behavior. In this research, we design and conduct a survey and randomized experiment to study the role of worker beliefs in shaping labor market responses to generative AI, with a focus on two professional fields: law and management consulting. There are three main findings. First, we find that beliefs vary substantially within and across professions. Second, we find that workers expecting larger AI impacts are somewhat more likely to report AI training, degree enrollment and workplace LLM use (though results are noisy and not significant based on current sample). Third, we find that exposure to contrasting expert narratives shifts stated beliefs about the likely impact of AI on the labor market; we do not find evidence of a significant impact on behavioral outcomes in current sample (small effects possible with more data). The findings in this chapter are based on preliminary pilot data from an ongoing project; findings may change as additional data collection continues.
The title of this dissertation, "Navigating Digital Worlds", emphasizes that, at least collectively, we have agency in the digital worlds that we create and inhabit. Sociotechnical spaces are not fixed or inevitable, but are instead imagined, constructed, and shaped by people, technologists, experts, and policymakers. The choices we make about the structure of these spaces then shapes the opportunities, constraints, and consequences we face in subsequently navigating them.Business Administratio
Trioxacarcin-based Antibody-Drug Conjugates: Optimization of payload structure and a triazene linker system design
No full textThe trioxacarcins are a set of rigid, pentacyclic natural products first isolated from Streptomyces bottropensis DO-45 in 1981. Their exceptional cytotoxicity inspired a phase 1 clinical trial only a few years later, which was halted abruptly due to unforeseen cardiotoxicity, resulting in a participant’s passing. Forgotten for decades, interest in trioxacarcins resurged more recently with the advent of contemporary, targeted cancer therapies, including Antibody-Drug Conjugates (ADCs), which promise to deliver the toxic payload directly to the tumor, thereby improving the therapeutic index. Following the initial disclosure of the total synthesis of Trioxacarcin A and related natural products back in 2013, the Myers group has been hard at work developing simplified, scalable pharmacophores that maintained the impressive potency of the initial bacterial isolates, and incorporated those into ADCs. Unfortunately, the very functionality responsible for its activity – the unique spiro-epoxide capable of alkylating guanine nucleobases in DNA – proved to be incompatible with the ADC platform, reacting with the nucleophilic amino acid side chains of the monoclonal antibody instead. Additional efforts resulted in the development of a bromohydrin-based prodrug strategy, however these drug-linkers lacked the requisite activity.
This thesis sets out to understand the underpinning reasons for the lack of ADC activity in several complementary ways. In Chapter 2, I present the final attempt at synthesizing trioxacarcin ADCs with an intact spiro-epoxide, using chemical methods to install a functional handle near the reactive functional group, in the hope of shielding it from the monoclonal antibody.
Chapter 3 identifies the bromohydrin reversion kinetics as the likely culprit of the lack of activity. Using a hypothesis-driven approach, we synthesize a panel of new trioxacarcin analogs with the goal of improving the efficiency of the spiro-epoxide reformation. We then identify a payload with the reversion kinetics improved 7-fold over the initial lead compound, and prove that the ADC activity is directly related to the rate of this process. Unfortunately, the improved reversion kinetics come at a cost of lower warhead potency, leading to equipotent ADCs.
In Chapter 4 I describe the design and synthesis of a novel drug-linker system inspired by the glioblastoma drug temolozomide. It incorporates a highly reactive triazene motif, which fragments in aqueous buffers to reveal a diazonium cation that immediately reforms the parent spiro-epoxide. By synthesizing different drug-linkers I show how the fate of the warhead is directly related to the substitution pattern of the triazene. The new constructs exhibit vastly superior epoxide reformation kinetics, with full conversion within 20 minutes versus multiple hours observed for the bromohydrin systems.Chemistry and Chemical Biolog
Preserving Information in Quantum Systems via Coherent Driving
No full textAmong the fundamental phenomena that characterize a quantum system is coherent superposition between states, which we can write for a two-level system as:
|ψ⟩ = α|0⟩ + e^(iφ) β|1⟩
A superposition is characterized by a phase φ which can be difficult to conceptualize with our physical intuition, trained only by experience in a classical world. Perhaps we can best approach it by considering the phase of classical waves, which we can concretely visualize, and which lead to some analogous phenomena like interference. In effect there is additional information in the system beyond the probability that it is measured in one state vs. the other.
This phase is both a resource and a vulnerability of quantum systems. On one hand, it gives the system another degree of freedom which we can control. On the other hand, it can be easily lost, since the phase of a superposition tends to be very sensitive to fluctuations in the system's surrounding environment. The length of time that the phase remains known in a system (or equivalently, that the system maintains its information) is called the coherence time.
In this thesis I will discuss two examples in which a coherent superposition of states is used to preserve information in a quantum system. In both cases the key laboratory tool used to access the quantum system is an AC field of classical amplitude which coherently drives the states, and in both cases we will see the beneficial properties of the stationary solutions of the combined system plus field, which are superposition states (in the basis that does not include the field). Chapter 1 will deal with an example of a coherent field extending the coherence time of a two-level system. I will show experimental data demonstrating this effect in the electron spin of the silicon vacancy in diamond, where the coherent driving field is a strain field in the carbon lattice. Chapter 2 will explain how the decay channels of a single atomic system can be controlled using superpositions of states, a phenomena which could be used to increase the fidelity of a state measurement. These results are computational rather than experimental, but I will suggest a real quantum system in which this phenomena could be demonstrated and calculate expected results.Engineering and Applied Sciences - Applied Physic
Environmental factors and immune related outcomes in the United States
No full textEnvironmental factors have been found to be important risk factors of human health. Previous studies
suggested that PM2.5 could perturb the immune system. PM2.5 is complex mixture of multiple
constituents, and each of these constituents could have varying impact on health. However, most existing
studies treated PM2.5 as a single exposure. How each of the constituents affects the immune system is
less clear. Non-optimal temperature is another environmental factor that could potentially impair the
immune function of human body. While the effects of mean temperature have been extensively studied,
the influence of temperature variability—particularly over longer time periods—remains insufficiently
explored.
In this dissertation, we attempted to address these gaps. We began by studying the association between
long-term exposure to PM2.5, PM2.5 constituents, source-specific PM2.5 and hospital admissions from
non-respiratory infections among the Medicare beneficiaries in the US between 2000-2016. We found that
PM2.5 was associated with increased risk of hospital admissions from total non-respiratory infections and
its subtypes including intestinal infections, urinary tract infections and septicemia. Sulfate, nickel and
copper contributed the largest weights in the association between PM2.5 and non-respiratory infections.
Anthropogenic sources of PM2.5 were found to have the largest impact on the exposure-outcome
associations.
In chapter II, we studied the association between seasonal temperature variability, seasonal temperature
and hospital admissions from infections using an adapted difference-in-difference analysis among the
Medicare beneficiaries. We found that increased temperature variability in both warm and cold season,
higher mean temperature in warm season and lower mean temperature in cold season were associated
with increased admissions from infections.
In the last chapter, we shifted our focus from the older population to an immunocompromised group—
kidney transplant recipients. Here we investigated the association between exposure to PM2.5
constituents prior to kidney transplant (KT) and post-KT outcomes including acute rejection, delayed graft
function, graft failure and mortality. We found that sulfate contributed the larges weight in the
association between the PM2.5 mixture and long-term post-KT outcomes (graft failure and mortality)
while lead, organic carbon and nickel contributed the largest weights in the short-term outcomes (delayed
graft function and acute rejection).
This dissertation contributes to the growing body of evidence on the impact of environmental exposures
on the immune system. It further demonstrates that the health effects of PM2.5 can vary depending on its
chemical constituents and specific sources. Overall, this work provides important insights into the broader
health implications of air pollution and climate change on human populations.Population Health Science
Anatomical and Functional Characterization of Gastrointestinal to Spinal Cord Circuits
No full textViscerosensory functions such as those governing the gastrointestinal (GI) tract
are essential for maintaining homeostasis, regulating appetite, and generating the
sensory experiences that shape daily life. Sensory circuits linking the colon to dorsal
root ganglia (DRG) neurons and spinal cord are required to detect and transmit
mechanical and chemical signals from the gut to the central nervous system, enabling
appropriate physiological and behavioral responses. Despite their importance, these
colon-DRG-spinal cord pathways remain remarkably understudied, leaving fundamental
questions about how internal sensory signals are encoded unanswered. Here, we
characterized the anatomical and functional organization of colon-DRG-spinal cord
circuits and investigated how spinal cord neurons process signals from the colon
compared to the skin in normal and disease states.
Using genetic labeling strategies targeting colon-innervating DRG subtypes, we
identified four distinct central morphologies that include basket-like, simple, peri-central,
and midline-crossing arbor subtypes, which are distinct from the more tufted arbor
morphology of skin-innervating DRG neurons. In addition, we discovered a unique
subset of Calca+ colon-innervating neurons that project through the dorsal columns and
terminate in and around the area postrema and nucleus of the tractus solitarius,
iii
revealing a previously unknown direct pathway for colon-derived sensory information to
the brainstem.
To assess how sensory information from the colon is processed in the spinal
cord, we developed an in vivo electrophysiology recording preparation to assess spinal
cord neuron responses to colon or skin stimulation. Surprisingly, the majority of colon-
distension-responsive spinal neurons also responded to innocuous skin stroking
(‘convergent neurons’). Convergent neurons displayed graded responses to innocuous
and noxious colon distension, while colon-stimulus-responsive-only neurons were
activated primarily by noxious distension stimuli. Optogenetic activation of colon- and
skin-innervating DRG neuron subtypes revealed that convergent neurons exhibited
higher peak firing rates and higher response correlations to skin-directed than colon-
directed stimuli, suggesting differential integration of visceral versus somatic inputs.
Finally, in a mouse model of colitis, we find that convergent neurons exhibited
heightened responses to both colon and skin stimulation, including prolonged excitation
following innocuous skin touch, while colon-stimulus-responsive-only and skin-stimulus-
responsive-only neurons were unaffected by DSS treatment. These data indicate that
convergent spinal cord neurons may contribute to cross-sensitization and persistent
pain following GI inflammation.
Taken together, our findings reveal distinct morphological and functional features
of colon-innervating DRG neurons and highlight the extensive convergence of visceral
and somatic inputs in the spinal cord, with implications for understanding chronic pain in
GI disorders.Biological and Biomedical Science
Arsenic Exposure and the Epigenome: Evaluating Effects of Periconceptional and Gestational Arsenic Exposure on DNA Methylation
No full textExposures before conception can impact sperm and oocyte epigenomes, morphology, and function with potential consequences for the developing fetus and child. The motivation of this work is to evaluate how early life exposure to arsenic can impact offspring epigenomes. To address this, we begin by studying the impacts of maternal arsenic levels on DNA methylation and DNA methylation-based age estimations that serve as potential approximations of gestational maturity in cord blood and placental tissues. Our findings indicate that there are sites sensitive to arsenic exposure across populations and tissue types and that biological clocks that estimate gestational duration are sensitive to arsenic exposure. We then explore DNA methylation as a function of parental arsenic exposure in a study of children born with spina bifida to determine whether observations about DNA methylation in this group can shed light on the biological pathways linking arsenic exposure and spina bifida – a neural tube defect with complex etiology. While specific arsenic-associated etiology remains unclear, our results indicate that DNA methylation of tissues taken from children born to mothers with higher arsenic exposure differs from those taken from mothers with lower arsenic. Finally, we evaluate the role of folate and arsenic on rDNA methylation of sperm in mice exposed through their mothers. We conclude from these investigations that arsenic and folate impact rDNA methylation with specific loci in the rDNA being sensitive to variations in both. Taken altogether, this work demonstrates the role of gestational arsenic on the epigenome in multiple tissues and sheds light on this relationship in understudied tissues and segments of the DNA.Population Health Science
Interrogating sequence-structure-function relationships in DNMT3A
No full textDNMT3A is a complex chromatin-modifying enzyme with a critical role in mammalian development and cancer biology. As one of two human de novo DNA methyltransferases, DNMT3A is responsible for establishing and maintaining proper gene expression profiles that allow the cell to differentiate properly during development. Dysregulation of this process in hematopoietic stem cells can lead to improper gene expression programs and diseases such as clonal hematopoiesis and Acute Myeloid Leukemia, as well as developmental disorders such as Tatton Brown Rahman syndrome. The role of DNMT3A in the genome has been extensively studied, and progress has been made towards understanding the role of DNMT3A at this genomic scale. However, at a molecular scale, many mysteries regarding the precise biochemical mechanisms by which DNMT3A performs its functions and is altered in disease remain. Specifically, the precise molecular mechanism by which the AML hotspot R882H mutation in DNMT3A causes its dominant negative effect remains unclear. In this dissertation, I will describe my work applying biochemical, genetic, and biophysical techniques to unravel the molecular basis of DNMT3A activity and its perturbation in disease.
In chapter 1, I will begin by giving an overview of what is currently known about DNMT3A biology. Then, I will briefly review literature on the evolution and biochemistry of protein-protein interfaces, with a focus on homo-oligomers. This body of work provides important context for the oligomeric behavior of DNMT3A and particularly the R882H mutation. Then, I will provide an overview of deep mutational scanning, broadly defined. Deep mutational scanning is a powerful genetic technique that, driven by increases in the affordability of DNA sequencing and synthesis technology as well as implementation of CRISPR-based methods, has recently diversified in both questions addressed and methods.
In chapter 2, I use base editor scanning to look for activating mutations to DNMT3A. In doing so, I uncover several activating mutations. These include a mutation at the ADD-MTase autoinhibitory interface, a cryptic splice site in the DNMT3A gene that causes a four amino acid deletion in the ADD-MTase linker, and a C-to-Y mutation in the ADD domain that based on existing structures is unlikely to perturb the known ADD-based autoinhibitory mechanism. In particular, this C-Y mutation may inform drug discovery efforts to design and understand small molecule DNMT3A activators.
In chapter 3, I use deep mutational scanning, biochemistry, and Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS) in collaboration with Shaunak Raval and Malvina Papanastasiou to uncover the molecular mechanism of the hotspot R882H mutation. First, we show that the R882H mutation causes the mutant protein to form aberrant oligomers, explaining its dominant negative effect. Then, we use a rescue mutant of DNMT3A R882H, L859F, to propose a model in which the R882H mutation pre-orders the residues that form the RD interface of DNMT3A into their binding-competent state, promoting oligomerization. L859F instead opposes this pre-ordering, rescuing the oligomeric state of the protein. This work both solidifies the oligomerization-promoting effect of the R882H mutation and provides evidence for its biophysical mechanism, which will be useful in drug discovery efforts to treat diseases associated with this mutation. Furthermore, this work describes the first report for this type of aberrant biochemical mechanism in cancer, providing a mechanistic framework that may be operative in other disease contexts as well.
In chapter 4, I discuss unpublished work exploring the role of the N-terminal regulatory domains in DNMT3A oligomerization and activity. This preliminary data suggests that the PWWP domain may make contacts with the MTase domain that are important for enzyme function, and that these contacts may also be involved in the ability of DNMT3A to form the RD interface. I also show that these properties seem to be exclusive to the DNMT3A PWWP domain, through experiments subbing the DNMT3B PWWP domain for that of DNMT3A. Finally, in chapter 5 I discuss conclusions and future directions to build upon this dissertation.Chemical Biolog
Fracture of Complex Hydrogels: Dynamic and Microstructural Effects
No full textHydrogels are a unique class of soft materials composed of three-dimensional polymer networks swollen with large amounts of water. This hybrid solid–liquid composition enables hydrogels with tissue-like mechanical properties, permeability to small molecules, and exceptional designability. By adjusting the combination of solvent, polymer, and network topology, hydrogels can be tailored into functional materials that meet diverse application requirements. These properties make hydrogels highly attractive for a wide range of applications, particularly in biomedical engineering, soft robotics, and bioelectronics.
Understanding the mechanical behavior of hydrogels is essential for guiding material design, evaluating failure criteria, and ensuring reliable performance in practical applications. It also provides valuable inspiration for the development of other soft polymeric materials such as elastomers and thermoplastics. In this thesis, we investigate the mechanical behavior of hydrogels, with a primary focus on their fracture properties. The complex conditions near the crack tip make the fracture process highly sensitive and show interesting phenomena when the chain topology, external stimuli, or loading conditions are altered at the crack tip.
A fundamental feature of hydrogels, and many other polymers, is viscoelasticity, which exhibits both elastic and viscous behavior depending on the timescale of deformation. Viscoelasticity originates from the molecular architecture of polymer networks. The flexibility of covalent C–C bonds allows for segmental motion, while interchain interactions dissipate energy and enable stress relaxation, with recovery to some degree upon unloading. In swollen systems like hydrogels, polymer–solvent interactions further influence chain mobility and relaxation dynamics by facilitating or hindering molecular motion. Additionally, chain entanglements act as transient constraints on motion, contributing to energy dissipation and delayed elastic recovery. Together, these factors result in the complex, time-dependent mechanical response that defines viscoelastic materials. While viscoelasticity is important for global deformation, its role becomes even more critical when considering how cracks initiate and propagate. In particular, the fracture behaviors of hydrogels are fundamentally shaped by their time-dependent mechanical response, which governs how energy is dissipated near the crack tip and how the material resists crack propagation. In these systems, energy dissipation around the crack tip is not only governed by intrinsic material toughness but also by the rate of deformation and solvent dynamics. A faster loading rate can decrease the apparent fracture toughness by decreasing energy dissipation through viscoelastic relaxation mechanisms. Similarly, the solvent viscosity in hydrogels modulates chain mobility and effective chain length, with higher solvent viscosity leading to much decreased resistance to crack propagation. In certain conditions, though hydrogels can show nearly perfect elasticity, their fracture can still show nonelastic process due to the complex and non-uniform crack tip zone. One striking phenomenon observed under these conditions is crack branching, where rapid deformation results in the formation of multiple crack paths. This behavior underscores the complex interplay between material structure, loading conditions, and environmental factors in defining the fracture response of viscoelastic materials.
In addition to passive viscoelastic effects, polymer networks can be engineered to actively respond to mechanical stress through mechanochemically triggered reactions. One such example is the incorporation of disulfide bonds (-S–S-), which can undergo dynamic exchange reactions under triggers like UV light and free radicals. When materials containing these bonds undergo crack initiation, localized stress near the crack tip can be relaxed when the dynamic reaction is activated, effectively redistributing stress and limiting crack growth, as the network is able to reorganize and reform in response to mechanical damage. This mechanochemical coupling introduces a powerful design strategy: leveraging molecular reactivity not only to enhance material toughness but also to enable the material to adaptive, self-protective behavior under extreme conditions.
Building on the concept of responsive materials, we introduce a triggerable crosslinking strategy using ferric citrate to strengthen natural polymer matrices. In this work, a complex crosslinker, ferric citrate, is applied to coordinate with chitosan, forming a robust film. The resulting ferric citrate-crosslinked chitosan film exhibits significantly improved mechanical strength, enhanced acid resistance, and recyclability. This system demonstrates a promising approach for the development of sustainable and functional biopolymer materials, where the crosslinking is not only reversible but also tunable based on environmental pH. By integrating a metal–ligand coordination chemistry with sodium citrate, we achieve a recyclable and chemically resistant material platform suitable for applications in packaging, filtration, or biomedical devices.
To further enhance the mechanical performance of hydrogels, we developed a method for fabricating hierarchically structured fibrous hydrogels using Wet Rotary Jet Spinning (WRJS). This scalable and rapid technique enables the production of continuous microfibers with controlled alignment and diameter. Taking polyvinyl alcohol (PVA) as a model system, we fabricated a fibrous PVA hydrogel with superior mechanical strength and flaw tolerance. The fibers, with diameters below 10 µm, were rapidly produced in large quantities by spinning PVA solution into a coagulation bath, followed by a salting-out process that stabilizes the fibrous structures. Inspired by the mechanical architecture of spider silk and other natural hierarchical structures, the resulting hydrogel combines high extensibility, strength, and crack resistance, attributed to the aligned, hierarchical fibrous network that dissipates energy effectively under stress. This hierarchical design offers a compelling route to engineering tough, scalable hydrogels with structural features mimicking natural load-bearing tissues, like tendons.
This dissertation presents a comprehensive investigation into the fracture mechanics of hydrogels, with particular emphasis on the complex mechanical behaviors near crack tips. Through a combination of experimental studies, material design, and theoretical insights, we explore how viscoelasticity, dynamic crosslinking, and hierarchical structure influence crack propagation and energy dissipation. The findings contribute to a deeper understanding of time-dependent fracture in soft materials and provide new strategies for designing tough, responsive, and multifunctional hydrogel systems suitable for a broad range of applications.Engineering and Applied Sciences - Engineering Science
Characterization of Electrodes and Electrolytes for Aqueous Organic Redox Flow Batteries Using Static Cells
Get PDFEnergy storage systems have become an essential component to the renewable energy transition. Aqueous organic redox flow batteries (AORFBs), specifically, have garnered interest as stationary battery technologies to solve the issue of intermittency of renewable energy for grid-scale electricity generation. In this work, we introduce a simplified, static cell design to enable straightforward evaluation of extremely low capacity fade rates for flow battery electrolytes using battery cycling methods. These static cells demonstrate the capability to reduce standard deviations in measured overall capacity fade rates per day across multiple experiments compared to measurements in flow cells. Furthermore, they allow the capability to, for the first time, decouple contributions to capacity fade due to time- and cycling rate-denominated fade because of their small volumes. Using porous electrode theory, we simulate the physics of reactions and transport inside the cell to investigate the roles of imperfect impregnation of the electrodes in the cell by the electrolyte. Then, using variations of the original static cell design, we investigate the confinement of organic molecules in micropores with surface characterization and voltammetry. Macroscopic and microscopic simulations confirm micropore confinement can influence the thermodynamics of charge transfer. We investigate the performance of several organic molecules in microporous electrodes and elucidate trends between electrophilicity, charge, and molecular structure on activity in the micropore. Finally, we demonstrate the ability to increase the energy density of aqueous organic secondary batteries in the form of static cells and flow cells by using micropore confinement of the active species.Engineering and Applied Sciences - Engineering Science