VictorJuiz/thesis huggingface dataset:
A Computational Framework for Modeling and Simulation of Metal Binder Jetting

Metal 3D printing has rapidly grown as a viable manufacturing method in recent years, reducing the barrier to entry for fabricating highly complex geometries. Such structures are almost impossible to create using traditional metal manufacturing processes. One method which has shown promise is metal binder jetting. Unlike most laser-based metal 3D printing prototyping applications which generate parts on a point-by-point basis, binder jetting has the ability to fabricate parts on a layer-by-layer basis, allowing for simultaneous component array fabrication. This multi-stage manufacturing process entails many physical mechanisms such as particle kinematics and dynamics, liquid diffusion into a porous media, heating of a multiphase, porous media, and near-phase transformations. This study presents a computational framework to separate this complex manufacturing process into various discrete and continuum-based models which can be simulated using tailored numerical methods for each application. Example models are explored, including a powder deposition simulation using the discrete element method, a multi-phase layer heating simulation using the finite difference method, and deformation of binder jetted metal components during sintering using the material point method. A machine learning model is developed using an image segmentation neural network to predict how bulk sintered parts will deform without the need for a full-scale multiphysics simulation. Through this exploration, it was determined that a multidisciplinary approach that combines chemistry, materials science, physics, numerical methods, and machine learning can be utilized to create a digital twin of binder jetting for process characterization and to better inform design decisions.

Advisor: Zohdi, Tarek I
Micro-Electro-Mechanical Relay Technology for Beyond-Von-Neumann Computer Architectures

The invention and development of complementary metal-oxide-semiconductor (CMOS) transistor technology for digital computing have led to a global information technology revolution and economic boom, which have shaped modern society. However, transistor leakage current and limited subthreshold swing set a fundamental limit on the energy efficiency of digital computing today. Micro-electro-mechanical (MEM) switches (relays) previously have been shown to be promising for energy-efficient digital computing applications, due to their abrupt ON/OFF switching characteristics and negligible OFF-state leakage current./nThis dissertation focuses on novel applications of MEM relays for facilitating new computer architectures that can be much more efficient than the classic von Neumann architecture. First, MEM relays are demonstrated to operate reliably with millivolt signals at cryogenic temperatures, due to much lower hysteresis voltage and more stable ON-state resistance. A sub-10 mV relay-based inverter circuit is demonstrated at a temperature of 4 K. Our experimental study indicates that MEM relays should be able to operate at temperatures as low as 1.8 K, making them promising candidates for ultra-low power cryogenic digital interface circuits for quantum computing./nThen superconducting MEM relays using niobium (Nb) as the contact material are investigated, in order to further reduce the operation power consumption for quantum computing applications at cryogenic temperatures. The detailed fabrication process flow is shown, and temperature-dependent measurements are conducted to check the superconductivity of the Nb electrodes./nFinally, DC-voltage-driven oscillatory behavior of MEM relays is investigated via experimental study and finite-element-method-based computer simulations. Sub-harmonic injection locking and coupled oscillation behaviors of MEM relays are demonstrated, indicating that MEM relay oscillators are promising for implementing Ising machines, which can solve large-scale combinatorial optimization problems much more efficiently than conventional computer architectures.

Advisor: Liu, Tsu-Jae King
Athermal Phonon Sensors in Searches for Light Dark Matter

In recent years, theoretical and experimental interest in dark matter (DM) candidates have shifted focus from primarily Weakly-Interacting Massive Particles (WIMPs) to an entire suite of candidates with masses from the zeV-scale to the PeV-scale to 30 solar masses. One particular recent development has been searches for light dark matter (LDM), which is typically defined as candidates with masses in the range of keV to GeV. In searches for LDM, eV-scale and below detector thresholds are needed to detect the small amount of kinetic energy that are imparted to nuclei in a recoil. One such detector technology that can be applied to LDM searches is that of Transition-Edge Sensors (TESs). Operated at cryogenic temperatures, these sensors can achieve the required thresholds, depending on the optimization of the design./nIn this thesis, I will motivate the evidence for DM and the various DM candidates beyond the WIMP. I will then detail the basics of TES characterization, expand and apply the concepts to an athermal phonon sensor--based Cryogenic PhotoDetector (CPD), and use this detector to a carry out a search for LDM at the surface. The resulting exclusion analysis provides the most stringent limits in DM-nucleon scattering cross section (comparing to contemporary searches) for a cryogenic detector for masses from 93 to 140 MeV, showing the promise of athermal phonon sensors in future LDM searches. Furthermore, unknown excess background signals are observed in this LDM search, for which I rule out various possible sources and motivate stress-related microfractures as an intriguing explanation. Finally, I shortly discuss the outlook of future searches for LDM for various detection channels beyond nuclear recoils.

Advisor: Pyle, Matt
Hydrogel Actuated Carbon Fiber Microelectrode Array

Glial passivation and subsequent electrical insulation of implantable microelectrodes is amajor bottleneck for long-term viability of neural probes. Self-deploying microelectrodes have been developed to minimize glial scarring and adverse biological effects near neural recording sites, but typically suffer from low electrode densities and deployment distance. In this dissertation, we propose and evaluate a large displacement, self-deploying architecture using a water absorbing hydrogel to extrude a high density carbon fiber array out of a microfabricated shuttle./nTo enable mm-scale displacements, this device records electrical signals via mechanicallyunanchored carbon fiber microelectrodes and couples them through a physiological electrolyte to the backend metal electrodes. A hydrogel-compatible silicon microfabrication and assembly process for a high density microelectrode array is presented, and mechanical insertion into a tissue phantom is demonstrated. An equivalent circuit model and multichannel recording capability are validated using electrochemical impedance and crosstalk measurements./nThis hydrogel actuation mechanism can provide sufficient force to push at least 66 fibers concurrentlyin one direction while achieving tissue phantom penetration depths up to 2.5 mm. The physiological electrolyte bridge used for recording marginally increases total signal path impedance up to 10%, but does not significantly affect recording capabilities nor multichannel crosstalk. This novel mechanical insertion and electrical recording architecture enables deep tissue penetration while simultaneously recording from a dense array of unanchored microelectrodes. This work proposes a mechanically and electrically robust neural probe architecture that could significantly improve microelectrode density and actuation distance of current state-of-the-art self-deploying microelectrode arrays while minimizing potential glial passivation effects.

Advisor: Maharbiz, Michel M
Pister, Kristofer S. J.
Essays on Environmental and Financial Economics

Climate change may influence financial market participants in many ways. Particularly, in a market with financial frictions, real estate usually serves explicitly or implicitly as a collateral in debt financing. Risks of physical damage to real property resulting from climate hazards and sea level rise may bring about not only direct loss, but also credit constraint for property holders. How much agents are affected by climate risks is an important research question, and has been explored substantially in the burgeoning climate risk literature. Another important question is what actions can be taken to manage or reduce the risks, and how to evaluate those efforts, which is the main goal of this dissertation./nThe first chapter studies the effectiveness and efficiency of adaptation investments for averting property damage (e.g. defensible space, drainage system, shoreline stabilization, etc.). Using variation in grant availability for adaptation projects through a U.S. federal program as a quasi-experiment, I quantify the impacts of property-related adaptation investments on debt financing of local governments and the real estate sector. There are three main findings. First, following adaptation investments, the average borrowing cost of a county government decreases by 10-26 basis points for 20 years. Second, nationally, adaptation investments have an insignificant effect on outstanding debt, while in the South and Northeast debt falls by 4.2%. Third, an average investment has a project cost of $2 million and reduces property damage by $323,000 per year, which implies a 15-year internal rate of return of 19%. Overall, these results suggest that adaptation mitigates climate risks. Additional calculations reveal that current levels of adaptation are below the social optimum; and given current spending, capital could be allocated more efficiently by altering the distribution across regions./nThe second chapter leverages tools in natural language processing (NLP) to explore the potential of generating large-scale yet granular measurements of how individuals perceive climate change and actions for addressing climate challenges. Social media such as Twitter provide a platform for users with diverse backgrounds to freely share their opinions, and thus capture real-time, higher-dimensional information that is not reflected in standard opinion surveys or polls. In this essay, I evaluate the use of different machine learning models to classify opinions on climate change and related actions from tweets. For model training, I annotate a dataset of climate-related tweets using a multi-stage system that distinguishes between two types of climate actions, mitigation or adaptation. I show that a deep learning approach based on contextual embeddings (BERT) outperforms traditional models, and addressing unbalanced classes through up-sampling achieves additional gains in accuracy. Finally, I discuss the limitations and potential applications of text-based characterization of opinions on climate change actions.

Advisor: Sallee, James
Davis, Lucas
First-Principles Computational Approaches to Advancing Green Chemistry and Technology

The American Chemical Society defines Green Chemistry in terms of a visionary set of principles that encourage rethinking of the material and chemical economy to reduce waste, decrease hazard, and innovate towards a sustainable future. While Green Chemistry, like all chemistry, is ultimately an empirical science, computational efforts lend significant support to implementation of greener frameworks. The present work is divided into two parts, each describing work in first-principles quantum chemistry to address pressing problems in Green Chemistry. The first concerns the development of predictive models for catalysis applications, specifically the carbon dioxide reduction reaction (CO2RR). In this, we utilize high-level electronic structure theory to analyze the nature of the metal--carbon monoxide (M-CO) chemistry, the fundamental interaction in the electrocatalytic CO2RR. In this, we provide detailed description of the physical and chemical contributions toward M-CO bonding, significantly deepening understands of these bonds. We also characterize errors associated with the use of pseudopotentials for modeling relevant chemical systems, finding that these represent an often overlooked but significant inhibitor to accurate systems modeling. The second part of this work examines the mechanism of action for p-phenylenediamine (PPD) antidegradants, which are ubiquitous in tire manufacturing globally. While these compounds protect rubber against degradation due to surface ozone (O3), one of their key transformation products is acutely toxic to aquatic life. Through application of modern density functional theory, we determine a detailed mechanism for the ozonation of these compounds, which was hitherto absent from the literature. This mechanism also provides key insights into the particularly high O3 reactivity of PPDs, and could help guide future efforts to identify non-toxic alternatives for tire manufacturing.

Advisor: Head-Gordon, Martin
Harnessing Covalent Chemical Probes: From Inhibition to Induced Proximity Modalities

Covalent drugs have garnered intense interest due to the unique ways in which they can modulate protein function. Over the past decade, several covalent drugs have been approved that target EGFR, BTK, KRASG12C, and the SARS-CoV-2 Mpro for oncology and antiviral indications. The success of these drugs has driven scientists to design strategies to discover and characterize electrophilic molecules, including specialized screening methods and selectivity-profiling techniques. These technologies have enabled the rapid discovery of covalent chemical probes that selectively modulate protein activity to facilitate study of protein function in different biological contexts. These covalent tool compounds have frequently been further optimized to yield enzymatic inhibitors that provide clinical benefit to patients. Electrophilic compounds have also proven exceptionally useful for inducing proximity between proteins using bifunctional molecules, specifically for targeted protein degradation (TPD). Bifunctional degraders, or proteolysis targeting chimeras (PROTACs), consist of an E3 ligase recruiter linked to a ligand for a target protein. While many degraders recruit the E3s CRBN and VHL to degrade target proteins, more than 600 E3 ligases exist in human cells which could be used for this purpose. To expand the scope of TPD, I discovered a novel covalent ligand for the E3 ligase FEM1B. This ligand, EN106, can be incorporated into bifunctional PROTACs that degrade BRD4 and BCR-ABL, demonstrating that FEM1B can be used to induce degradation of target proteins. While protein degradation is often desirable, there are many cases where protein stabilization could have a therapeutic benefit instead. Here, I describe a strategy for targeted protein stabilization enabled by bifunctional deubiquitinase targeting chimeras (DUBTACs) that induce proximity between a deubiquitinase (DUB) and a target protein. Discovery of a covalent, allosteric ligand for the DUB OTUB1 led to the discovery of DUBTACs that stabilize mutant cystic fibrosis transmembrane conductance regulator (CFTR), whose degradation is central to cystic fibrosis pathology, as well as the tumor suppressor kinase Wee1. This DUBTAC platform allows the modular design of molecules that induce stabilization, unlocking proteins whose gain-of-function would be therapeutically desirable as potential drug targets. Finally, I share the discovery and optimization of covalent inhibitors of the SARS-CoV-2 main protease (Mpro), as part of an effort to discover pan-coronavirus antiviral compounds. This thesis describes the discovery of covalent chemical probes and how they can be harnessed to modulate protein function in ways that highlight potential opportunities for future drug discovery.

Advisor: Nomura, Daniel K
Search for Non-pointing and Delayed Photons in pp collisions at √s=13 TeV using the ATLAS detector

This thesis describes the search for displaced photons in the exotic decays of the Standard Model Higgs boson using 139 fb−1 of proton-proton collision data recorded by the ATLAS detector. The signal model involves the decay of the Higgs boson into a pair of long-lived supersymmetric particles, each of which subsequently decay into a photon and a stable supersymmetric particle. The current upper limits on the decays of the Higgs boson to the undetected particles is around 21% making this search extremely relevant. This analysis utilizes the ATLAS electromagnetic calorimeter’s unique capabilities to measure the photon’s direction of flight and its time of arrival. No significant excess was observed compared to theestimated Standard Model background and this null result was used to set 95% confidence level limits on the branching ratio of the Higgs to the pair of long-lived particles for the very first time.

Advisor: Shapiro, Marjorie
Modeling and Simulation of Electric Power Systems with Large Shares of Renewable Energy

To date, electric power systems are the most important technological achievement in the energy sector. Interconnected power systems require a careful operation to maintain supply stability and maintain efficient energy usage. Amid this complexity, two critical global transitions are needed: (1) moving away from greenhouse gases which negatively contribute to climate change; and (2) moving away from fossil fuels, which currently support transportation and heating systems./nComputer simulations are the only tools available to do the necessary research and development required to accomplish these goals. However, the practices and tools used by the electric power industry were developed for operational situations that are vastly different from current challenges for energy transition. Variable Renewable Energy uncertainty, fast varying resources, and fundamental changes to the energy transformation physics require an update to the operational and simulation practices./nThis work started focusing on the simulation of future systems, and by the time it was written, it had become a dissertation on the practices needed for today's system. We discuss the requirements needed to improve scientific computing practices and conduct simulations more systematically when developing new operation models. The work has designed and implemented three software tools to develop simulations: the first includes PowerSystems.jl –– a software package that handles the data ingestion and processing required for simulations across multiple time scales. The second tool includes PowerSimulations.jl which resolves issues of model-limited choice when implementing operations simulations of large interconnected systems. The third tool includes PowerSimulations.jl, which is based on a formalization of the simulation of power system operations and provides a scalable computational platform. Third, PowerSimulationDynamics.jl which concentrates on the modeling of power systems dynamics with a strong focus on integrating Inverter Base Resources. PowerSystems.jl and PowerSimulations.jl both enable the development of a novel Automatic Generation Control model that can assess reserve deployments. Both tools are used to develop a Markovian Graph approach when integrating probabilistic forecasts into operators' risk assessments./nFinally, this dissertation investigates the simulation techniques that are needed for system dynamics and challenges the applicability long-held simulation practices. It seeks to uncover the theoretical elements of simulation needs for the future grid. Results from PowerSimulationDynamics.jl confirm this dissertation's hypotheses and demonstrates the effectiveness of the simulation approach in replicating existing models. Furthermore, the findings in this thesis critically showcase the capabilities to simulate dynamics systems accurately using model transformations that reduces computational complexity without loss of accuracy.

Advisor: Callaway, Duncan S.
Single-Shot 3D Microscopy: Optics and Algorithms Co-Design

Computational imaging involves simultaneously designing optical hardware and reconstruction software. Such a co-design framework brings together the best of both worlds for an imaging system. The goal is to develop a high-speed, high-resolution, and large field-of-view microscope that can detect 3D fluorescence signals from single image acquisition. To achieve this goal, I propose a new method called Fourier DiffuserScope, a single-shot 3D fluorescent microscope that uses a phase mask (i.e., a diffuser with random microlenses) in the Fourier plane to encode 3D information, then computationally reconstructs the volume by solving a sparsity-constrained inverse problem./nIn this dissertation, I will discuss the design principles of the Fourier DiffuserScope from three perspectives: first-principles optics, compressed sensing theory, and physics-based machine learning. First, in the heuristic design, the phase mask consists of randomly placed microlenses with varying focal lengths; the random positions provide a larger field-of-view compared to a conventional microlens array, and the diverse focal lengths improve the axial depth range. I then build an experimental system that achieves <3 um lateral and 4 um axial resolution over a 1000x1000x280 um^3 volume. Lastly, we use a differentiable forward model of Fourier DiffuserScope in conjunction with a differentiable reconstruction algorithm to jointly optimize both the phase mask surface profile and the reconstruction parameters. We validate our method in 2D and 3D single-shot imaging, where the optimized diffuser demonstrates improved reconstruction quality compared to previous heuristic designs.

Advisor: Waller, Laura
Pieces of Change: Uncovering the Material Networks that Transformed Ancient Eurasian Interactions

Even in today’s world, money is not just about finance. Money has arranged social relations and powerfully connected people across continents for well over a millennium. This work explores museum-based materials currently housed in collections in the Middle East and Europe, including the movements of itinerant Islamic coins and worked pieces of ivory, each influenced by global exchanges centered in the Islamic World. When examined with digital archaeological tools, these assemblages of itinerant materials reveal previously undetected and under-explained global networks. Not least, Islamic coins reflect past Arab and Islamic encounters with Eurasian communities thousands of miles away from the Arabian mainland. These coins from the ‘Abbāsid Caliphate have been uncovered across Eurasia, including in modern-day Northern and Eastern Europe. The archaeology of these coins reveals a global economic system based on Arab silver that transformed ancient Eurasia to the global space that it remains today. Previously, these materials were largely studied as site- or region-based phenomena without a comprehensive study that investigates how they operated trans-regionally as networked assemblages, beyond their influence on economic systems and interactions. Nor have they been investigated as points of cultural connection to contemporary local communities. Consequentially, these important objects of archaeological and cultural heritage value remain largely inaccessible to source communities. To address these issues, this dissertation applies digital technologies in Archaeology, including network analysis, Assemblage theories, as well as consultation with local stakeholders to reveal this understudied multicultural past and its enduring legacy. This dissertation seeks to explain how itinerant objects inform global social relations and how materials often operate in unexpected, unintended ways. The cultural heritage represented in museum-based material assemblages cuts across national borders, languages, and far distances. These materials thus remind us that today, as in the past, social distance does not always correlate with physical distance.

Advisor: Joyce, Rosemary A