VictorJuiz/thesis huggingface dataset:
Corrosion Performance of Steel and Nickel Alloys in Elevated Temperature Solar Salt

Nitrate molten salts are widely used in applications requiring high temperature heat transfer and thermal energy storage such as in the chemical and metal industries and in concentrated solar power plants. Recently, several advanced nuclear reactor designs have proposed the use of nitrate salt as an intermediate loop or thermal energy storage system. In doing so, nitrate salt is aimed to be operated at temperatures above the current max operating limits around 570°C. At these elevated temperatures, corrosion rates of materials in molten nitrate salt environments have been observed to markedly increase, owing in large part to the decomposition of the nitrate salt. While significant research has been conducted regarding elevated temperature corrosion of materials in nitrate salt, particularly rare are long-term, elevated temperature corrosion studies which enable more detailed understanding of the interplay between nitrate salt decomposition and material corrosion behavior. In this dissertation, the corrosion performance of steel and nickel alloys in elevated temperature Solar Salt (60wt% sodium nitrate, 40wt% potassium nitrate) was investigated. Chapter 3 describes the results from a multi-alloy corrosion experiment conducted at 630°C for 600hr. Twelve different materials were investigated, revealing significantly better corrosion performance for alumina-forming austenitic steel and nickel alloys when compared to typical stainless steels such as 316SS. This is followed by a relatively brief Chapter 4 in which 316SS was exposed in Solar Salt for 645hr at elevated temperatures ranging from 570°C-650°C. Through this temperature scoping study, a temperature inflection point was discerned in the rate of increased corrosion of 316SS, hinting at a change in the rate controlling process for corrosion within the salt. The heart of the dissertation revolves around a long-term corrosion study (Chapter 5) conducted on 316SS and Alloy 601 at a range of elevated temperatures up to 5000hr. Microscopy revealed a complex evolution in corrosion scale morphology exhibited by the materials as a function of both temperature and time. Solar Salt was found to become more corrosive over time to fresh samples, and over-nitriding of the metal surface was readily observed. These and other results were synthesized in a proposed corrosion mechanism for 316SS and 601 in elevated temperature Solar Salt with significant implications for the use of these and other related materials in molten nitrate salt applications.

Advisor: Hosemann, Peter
Citizen-initiated Contacting in the Russian Federation: Approaches from the Citizen Perspective

This dissertation examines why citizens in autocratic contexts engage in robust forms of citizen contacting. Citizen contacting is a foundational democratic practice, yet several authoritarian regimes, including Russia, have been heavily adopting institutions that facilitate direct citizen contact with the state. Analysis from the state’s perspective reveals the many reputational, informational, and administrative benefits these types of institutions can provide. However, such analysis does not explain why citizens engage with these institutions, which for them may be perceived as political window-dressing or a highly risky enterprise in identifying oneself and one’s criticism directly to state officials. Despite these concerns, citizens in Russia are increasingly engaging in this practice, through both the state’s extensive citizen appeal and e-government systems. Through data collection from state-issued sources and an original survey, I explore the citizen’s perspective on what motivates them to interact with the state. Ultimately, I argue that the prevailing models through which Russian citizens view these systems are a Resource-based Approach and an Approach of Everyday Resistance. What my findings demonstrate is that citizens in authoritarian regimes view contacting channels as vehicles through which one can apply one’s personal resources to improve one’s own welfare and more safely exercise and express their own political voice to the state and regime.

Advisor: Wittenberg, Jason
Fish, M. Steven
Development of Amorphous-silicon-based Optical Coatings for Gravitational-wave Detectors

The search for new coating materials, particularly high-refractive-index layers, is crucial in the field of interferometric gravitational-wave (GW) detectors. These detectors require coatings with low mechanical loss to minimize thermal noise and low optical absorption to maintain measurement stability and precision. The development of novel high-index coating materials with improved mechanical and optical properties has the potential to advance GW detection and enable more precise measurements of astrophysical phenomena./nThis study investigated the structural and mechanical properties of amorphous silicon (a-Si), hydrogenated amorphous silicon (a-Si:H), and amorphous silicon carbide (a-SiC) films deposited using magnetron sputtering (MS). The effects of deposition parameters, such as working gas, gas pressure, substrate temperature, and post-growth treatments including vacuum annealing and post-hydrogenation, were explored. The choice of working gas and its pressure, which influences the kinetic energy of the sputtered atoms upon arrival at the substrate, was found to significantly affect the properties of the deposited films. The impact of energetic incoming atoms is reflected in compressive film stress and film densification, a phenomenon known as the atomic peening effect. Additionally, a transition in dominant defects from nanovoids in e-beam a-Si to divacancies and self-interstitials in MS a-Si was observed, which can be attributed to the higher kinetic energy of incident atoms. By selecting appropriate working gas and deposition conditions, such as low gas pressure and high growth temperature, the structural order was improved, as indicated by lower bond angle deviation, and the mechanical loss of MS a-Si was reduced at both low temperatures and room temperature./nIntroduction of hydrogen to a-Si had a significant impact on its optical absorption at infrared wavelengths and the room-temperature mechanical loss. We propose that hydrogen plays a dual role in a-Si:H, acting as a passivating agent for dangling bonds, particularly at high temperatures, and facilitating structural relaxation, and the latter is responsible for the observed changes in the physical properties of a-Si:H. The incorporation of carbon in a-SiC films resulted in higher mechanical loss and optical absorption compared to pure a-Si films. These findings provide insights into the underlying mechanisms and offer potential solutions for optimizing the performance of a-Si-based coatings for GW detector mirrors.

Advisor: Hellman, Frances
Scalable Binding

Any useful agent will face many tasks and must rely on transfer of prior knowledge acquired in a scalable manner. This thesis explores inductive biases that enable scalable pre-training of representations -- and algorithms that bind them -- from the design of architectures capable of adaptive computation for scalable generative modeling, to self-supervised objectives that prepare embodied agents with mechanisms for state representation and reward maximization./nFirst, I consider the challenge of gracefully scaling generative models to high-dimensional data, motivating the importance of adaptive computation, a property missing from predominant architectures. This leads to a simple attention-based architecture for diffusion models capable of dedicating computation adaptively across its input and output, attaining superior performance in image and video generation despite being more domain-agnostic and efficient. Visualizations of read attention demonstrate how the model learns to dedicate computation to more complex parts of samples; e.g. in cases of high redundancy such as video prediction, it learns to simply copy information when appropriate and focus computation on more complex dynamics./nNext, I show how self-supervised objectives that exploit more domain knowledge can be used to efficiently solve related downstream tasks. In the domain of perception, I show how a simple self-supervised objective for space-time attention can be used to solve a range of tasks involving temporal correspondence and object permanence, central challenges in state representation for embodied agents. In the domain of reinforcement learning, I motivate the importance of scalable construction of task distributions and demonstrate how meta-reinforcement learners -- and underlying exploration and stimulus-reward binding mechanisms -- can be pre-trained with self-supervised reward models./nFinally, I conclude with a perspective on open problems in scalable pre-training, with a focus on the interplay between transfer across modalities, universal generative modeling objectives for discrete and continuous data, and adaptive computation.

Advisor: Efros, Alexei A
Bottom-up and Top-down Approaches to a Quantum Theory of Gravity

In this dissertation, we explore these two complementary approaches to quantum gravity – holography as a bottom-up perspective and string theory as a top-down framework./nOn the holography side, we study two novel extensions of the AdS/CFT duality – TT deformed CFTs and their gravitational duals, and a two-dimensional BCFT with two conformal boundaries dual to AdS spacetime with two End-of-the-World branes that can intersect at a defect. We also develop a bulk flow procedure that can be interpreted as generating a sequence of coarse-grained holographic states. We demonstrate the existence of a novel inside-out entanglement island for the eternally inflating multiverse. We compute the entanglement entropy of free fermions in the presence of a partially transmitting boundary and use it to study entanglement islands for a blackhole with non-trivial graybody factors./nOn the string theory side, we study non-perturbative effects in minimal string theory. We compute various instanton contributions like the ZZ cylinder, the ZZ annulus one-point function, and the disk two-point function using string field theory. We also match these to results from the dual matrix model or matrix quantum mechanics.

Advisor: Nomura, Yasunori
Evaluation of a statewide integrated medical and social service case management policy innovation: A multi-level assessment of equitable implementation for frontline staff and high-risk, high-need Medicaid patients

Implementation science provides frameworks, theories, models, strategies, and mechanisms to support researchers and practitioners in translating evidence-based interventions into practice and improving the effectiveness of these interventions. However, until recently most implementation science studies did not attend to factors related to health equity. Arguably, the fields of implementation science and health equity research and practice often seek to meet similar outcomes including widespread access to services and resources, improved quality of services delivered, and generalizable benefits from evidence-based intervention implementation./nThis dissertation evaluates equitable implementation and sustainment of California’s Whole Person Care initiative, which tested whether provision of care coordination, housing assistance, and other social services could improve cost and outcomes of care for high-risk, high-need Medicaid enrollees. Health systems increasingly use case management programs to integrate social and medical services to support health equity for high-risk, high-need patients. Limited evidence exists about key components of integrated case management program implementation and sustainability, especially from a health equity perspective./nChapter 1 is a thematic analysis exploring how a single WPC pilot integrating medical and social services for high-risk, high-need patients addressed health equity through pilot design and implementation. Chapter 2 applies a mixed methods approach to identify key individual, interpersonal, and organizational factors associated with case manager job satisfaction and intention to leave their role among a sample of Whole Person Care frontline case management staff. Chapter 3 applies a thematic analysis approach to identify multi-level factors hindering or supporting equitable sustainability of Whole Person Care pilot components beyond the pilot phase. Combined, these analyses provide a blueprint for assessing equity research and practice leveraging the strengths of implementation science, while also offering pragmatic findings to inform future efforts to design, implement, and evaluate integrated case management models broadly.

Advisor: Chuang, Emmeline
Credit Supply and Household Debt Decisions

This dissertation contains two chapters. In Chapter 1, I combine randomized controlled trials with transaction-level data and survey data, I show that credit-limit extensions significantly increase consumer expectations about their future income without increasing realized income. A one-dollar higher qualified credit limit raises consumer income expectations over the nextsix months by 40 cents and total consumption by 34 cents. The expectation changes explain around 35% of the total spending responses to credit limit extensions. The results show that consumers infer information about their future income from credit supply, and this learning behavior impacts their economic decision-making greatly./nIn the second chapter, I structurally estimate a life-cycle model incorporating the income-inference channel to study how shocks to banks’ beliefs affect their credit extensions and consumers’ subsequent spending behaviors. I find that, When the precision of bank signal about consumer future income increases by 25%, equilibrium credit supply and spending for the median consumer respectively increase by 14.11% and 4.11%. As suggested by the counter-factual analysis, when the advancement of information technology enables banks to extract more precise signals about household future income, credit supply, and spending tend to increase. The elasticity of spending with respect to bank-signal precision is around 0.16.

Advisor: Malmendier, Ulrike
Membrane Curvature Sensing and Stabilization in Macroautophagy

A fundamental question in the central cellular recycling pathway of macroautophagy (hereafter autophagy) is how the autophagosome membrane is shaped around its cargo. At its heart, autophagosome biogenesis is a membrane remodeling event that sequesters cytoplasmic inclusions targeted for lysosomal degradation. And, while the biochemical basis for the recruitment of core autophagy proteins and individual subreactions has been largely worked out, critically, the physical mechanisms for membrane engulfment of cargo have yet to be elucidated. Among the most important outstanding questions are how membrane shape participates in the recruitment of early autophagy proteins and how the highly curved phagophore membrane is stabilized during autophagy initiation.Here, we use in vitro reconstitution on membrane nanotubes investigate how core autophagy proteins in the LC3 lipidation cascade and PI3K kinase complexes interact with curved membranes, providing insight into the possible roles in regulating membrane shape during autophagosome biogenesis. I found that ATG12–ATG5-ATG16L1 was up to 100-fold enriched on highly curved nanotubes relative to flat membranes. At high surface density, ATG12–ATG5-ATG16L1 binding increased the curvature of the nanotubes. While WIPI2 binding directs membrane recruitment, the amphipathic helix α2 of ATG16L1 is responsible for curvature sensitivity. Molecular dynamics simulations revealed that helix α2 of ATG16L1 inserts shallowly into the membrane, explaining its curvature-sensitive binding to the membrane. These observations show how the binding of the ATG12–ATG5-ATG16L1 complex to the early phagophore rim could stabilize membrane curvature and facilitate autophagosome growth. Further, reconstitution of the Class III PI3K complexes on membrane nanotubes shows that both Complex I and Complex II are similarly curvature sensitive, opening questions about the role of membrane curvature in the differential regulation of PI3K complex localization and regulation in the cell. Finally, these insights were facilitated by improvements and simplifications to existing membrane tube and in vitro reconstitution methods, which accelerated the work of precise biophysical quantification. Together, this work provides mechanistic insight into the physical basis of membrane remodeling in autophagosome biogenesis.

Advisor: Hurley, James H
Identification of Transcriptional Enhancers in Development and Disease

Enhancers are non-coding DNA elements found throughout the genome that, in concert with transcription factors, coactivators, and general transcriptional machinery, activate cell-type specific gene expression. Initial studies on enhancers demonstrated these regulatory elements contained clusters of transcription factor binding sites to recruit endogenous transcription factors and drive elevated expression of a target gene . These early works highlighted that theirregulatory activity was maintained despite alterations in their orientation and/or positioning relative to the targeted gene. Nearly half a century later, enhancers are center stage in efforts to characterize the regulatory components and mechanisms behind development and disease. This dissertation is a study on mammalian enhancers, the genome-wide approaches for their identification, and their contributions in early developmental processes. Chapter 1 provides an overview of the enhancer properties uncovered from various experimental systems and how these properties are harnessed to predict and further dissect enhancer activity. Chapters 2 and 3 comprise two separate projects that involve 1) an extensive in vivo assessment of active enhancers that are hidden from canonical biochemical-based methods for enhancer identification and 2) the characterization of tissue-specific enhancers across the Shox2 locus that regulate early heart, face, and limb development. Altogether these works demonstrate the critical roles of enhancers for normal organismal development and the ongoing challenge of mapping increasingly large datasets to insights on enhancer prediction and function.

Advisor: Pennacchio, Len A
Aligning Robot Representations with Humans

Robots are becoming increasingly weaved into the fabric of our society, from self-driving cars on our streets to assistive manipulators in our homes. To act in the world, robots rely on a representation of salient features of the task: for example, to hand me a cup of coffee, the robot considers movement efficiency and cup orientation in its behavior. However, if we want robots to act for and with people, their representations must not be just functional but also reflective of what humans care about, i.e. their representations must be aligned with humans'. What's holding us back from successful human-robot interaction is that these representations are often misaligned, resulting in anything from miscoordination and misunderstandings, to learning and executing dangerous behaviors./nTo learn the human's representation of what matters in a task, typical methods rely on data sets of human behavior but this data cannot reflect every individual, environment, and task the robot will be exposed to. This dissertation advocates that we should instead treat humans as active participants in the interaction not as static data sources: robots must engage with humans in an interactive process for finding a shared representation. We formalize the representation alignment problem as a joint search for a common representation. Then, rather than hoping that representations will naturally be aligned, we propose having humans directly teach them to robots with representation-specific input. Next, we enable robots to automatically detect representation misalignment with the human by estimating a confidence over how much the robot's representation can explain the human's behavior. We demonstrate how human-aligned representations can lead to novel human behavior models with broad implications beyond robotics, to econometrics and cognitive science. Finally, this thesis concludes by asking ``How can robots help the human-robot team converge to a shared representation?'' and discusses opportunities for future work in expanding representation alignment for seamless human-robot interaction.

Advisor: Dragan, Anca D
Field Hand Labor in the Cybershire Imaginary and Urban Agrarian Questions Beyond the Metabolic Rift

Early hopes for the dramatic and significant expansion of Urban Agriculture (UA) in the East Bay Area to help mitigate the metropolitan metabolic rift were not borne out. Using Critical Race Theory, Industrial Ecology, and Urban Political Ecology, I explore this phenomenon by examining the operations of four nonprofit UA organizations: Spiral Gardens, City Slicker Farms, Urban Adamah, and Urban Tilth. I conclude that a simultaneous valorization and stigmatization of farm work (Field Hand Labor) and the absence of engineered municipal material support infrastructure for agroecological urbanism (the Cybershire Imaginary) curtail food sovereignty, and yet these organizations nonetheless perpetuate politics and moral economies that suggest Chayanovian differentiation and repeasantization based partly in a legacy of Black Agrarianism (the Urban Agrarian Question).

Advisor: Iles, Alastair
Accelerating Electronic Structure Calculations with Machine Learning

New chemicals and new materials have transformed modern life: pharmaceuticals, pesticides, surfactants, alloys, catalysts, polymers, battery electrodes, and countless other materials play critical roles in healthcare, construction, energy, and other wide-ranging industries. New materials are not generally stumbled upon by happenstance, but rather are discovered through a long process that involves extensive physics-based computer simulations at the atomic level. Electronic structure calculations play an important role in the discovery process, but they can be extremely computationally expensive. As such, there is a long history of approximation methods that trade off speed and accuracy./nMachine learning has the potential to open a new frontier on this speed-accuracy trade-off, and in doing so, significantly accelerate discovery of new materials. In this dissertation, we first cover the quantum mechanical background necessary to understand the problem setting, written with the machine learning community in mind as the audience. Next, we surveythe learning-based methods that are pushing the speed-accuracy frontier, along with some foundational non-learning-based methods. Lastly, we investigate self-supervised learning as a mechanism for understanding the shape of the potential energy surface without expensive-to-obtain supervision on energies and forces.

Advisor: Gonzalez, Joseph