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Workflows and automated platforms for nanocrystal synthesis, purification, and characterization
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Rui Hua Jeff Xu, accepted the attached license on 2025-04-21 at 12:35.The student, Rui Hua Jeff Xu, submitted this Dissertation for approval on 2025-04-23 at 13:59.This Dissertation was approved for publication on 2025-04-23 at 16:06.DSpace SAF Submission Ingestion Package generated from Vireo submission #21857 on 2025-10-19 at 19:15:11Nanocrystals (NCs) are a class of materials with promising potential across a wide range of applications. Most NCs are synthesized using solution-phase methods that enable synthesis of nanostructures across a wide range of compositions at a large scale. These solution-phase methods all rely on specific processes of precursor reaction, formation of nuclei, and growth of nuclei to form NCs with desired shapes and sizes. Unfortunately, such mechanisms of NC formation are at present poorly understood, that in turn limits our ability to synthesize desired NCs at scale. Understanding the synthesis mechanisms for NC synthesis remains a major barrier to the widespread application of NCs. Conventional manual batch-reactor solution-based NC synthesis suffers from both low throughput and significant synthesis reproducibility that in turn leads to poor understanding of their formation mechanisms. This dissertation thus focuses on developing automated synthesis, purification, and characterizations approach to enable the rapid generation of reproducible datasets for understanding the mechanisms of NC nucleation and growth. In Chapter 2, an automated batch reactor that enables the reproducible synthesis of NCs is reported. This reactor platform was capable of reproducible hot injection synthesis of CdSe (< 0.2% variation across runs) and was used to collect a large dataset for the hot-injection synthesis of CdSe NCs. Using the dataset collected from this synthesis platform, machine learning models are used to predict the synthesis outcomes of this synthesis chemistry in Chapter 3. ML explanability tools were then used to understand the impact of individual synthesis parameters on synthesis outcomes and thus uncover their influence on CdSe nucleation and growth. The model explanability tool SHAP has demonstrated to not only show the relative importance of different process parameters such as temperatures and concentrations but also allows us to select features that can lead to more accurate models. In Chapter 4, an automated NC purification platform using size-exclusion chromatography (SEC) was used to enable one-step purification of NCs in nonpolar inorganic phases to produce high-purity NCs suitable for imaging and other structural characterization applications. This platform allows for rapid purification of crude QD synthesis mixtures (< 100s per sample) to generate QDs in solvents free of excess nonvolatile solvents and ligands (as seen from NMR analysis) and can be seamlessly integrated into existing synthesis and characterization platforms. In Chapter 5, building upon the platforms and workflows described in previous chapters, plans for future research directions are provided. These proposals seek to further develop capabilities for automated synthesis and characterization platforms by integrating existing (Chapter 2-4) and planned platforms with powerful structural and chemical characterization tools. Data from these integrated platforms will then be used by physics-based modeling approaches to uncover mechanisms of NC nucleation and growth. Some preliminary results on designing and testing these planned platforms will also be discussed here. Finally, in Chapter 6, a summary of insights gained in this dissertation as well as some perspectives about the outlook on capabilities in self-driving laboratories and NC synthesis will be discussed, with emphasis on improving characterization capabilities and modeling efforts. Overall, this dissertation focuses on developing automated capabilities in NC synthesis, purification, and characterization with a focus on synthesis reproducibility, structural characterization, and interpretable models to understand NC nucleation and growth
Investigating common challenges in K-12 CS education: funding, teacher isolation, and teacher attrition
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Mariam Saffar Perez, accepted the attached license on 2025-04-21 at 14:39.The student, Mariam Saffar Perez, submitted this Dissertation for approval on 2025-04-21 at 14:48.This Dissertation was approved for publication on 2025-04-23 at 14:33.DSpace SAF Submission Ingestion Package generated from Vireo submission #21866 on 2025-10-19 at 19:15:12This dissertation explores the factors that influence the expansion of K-12 CS education in the United States. As the U.S. pushes for national expansion of K-12 CS education, there are challenges in expanding CS education equitably. Additionally, there are gaps in our understanding of CS teachers’ experiences. To that end, I have researched three important factors in K-12 CS education: funding, teacher isolation, and teacher attrition. My first chapter was focused on understanding how Career Technical Education (CTE) funding might impact CS education. I used linear regressions to study the relationship between a district’s CTE funding and their CS course offerings and enrollment. Using California data, the results from this chapter indicated that despite the suggestions to use CTE funding to fund CS education there was no relationship between CTE funding and an increase in CS course offerings or enrollment. My second chapter focused on CS teacher isolation, where a CS teacher is the only CS teacher in their school. An isolated teacher has fewer avenues for collaboration and this may increase job dissatisfaction and their likelihood for attrition. Using California data, the results from this chapter showed that CS teachers were among the most isolated subjects. Additionally, results indicated inequity in which students have access to CS. My third chapter focused on CS teacher attrition and how this compares with teachers of other subjects. There are arguments that CS teachers are at a higher risk of attrition, in part due to more lucrative positions in the CS industry. Using logistic regressions and a Cox-Proportional Hazards model, I investigated the relationship whether CS teachers are more likely to leave the profession when controlling for personal and school factors. Results from North Carolina show that CS teachers are actually at a decreased risk of attrition. These interconnected factors contribute to a broader understanding of how CS education is evolving in the U.S. and the ways in which educational systems can better support CS teachers and, by extension, ensure equitable access to quality CS education for all students. As the demand for CS education continues to rise, addressing these three narratives will be essential for maintaining the momentum of CS expansion and ensuring its long-term success
Three-dimensional image reconstruction in breast ultrasound computed tomography
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01The student, Fu Li, accepted the attached license on 2025-04-22 at 03:15.The student, Fu Li, submitted this Dissertation for approval on 2025-04-22 at 03:58.This Dissertation was approved for publication on 2025-04-22 at 15:03.DSpace SAF Submission Ingestion Package generated from Vireo submission #21880 on 2025-10-19 at 19:15:16Ultrasound computed tomography (USCT) is an emerging imaging technique that uses tomographic principles to obtain quantitative estimates of acoustic properties such as speed-of-sound (SOS), density, and acoustic attenuation (AA). Because it can produce high-resolution and high contrast images of tissue properties, the development of USCT as a breast imaging modality has received significant attention. It has several advantages over other breast imaging modalities, such as mammography, including low cost and being radiation- and breast-compression-free. While commercial systems for breast USCT are being actively developed, USCT remains an emerging technology and a topic of active research. USCT systems commonly utilize a circular ring-array of elevation-focused ultrasonic transducers. Volumetric imaging is then achieved by translating the ring-array orthogonally to the imaging plane. Recent advancements in breast USCT employing such ring-array systems have demonstrated promising progress. However, image quality remains limited due to the use of simplified reconstruction methods that rely on a two-dimensional (2D) wave physics model. In this 2D approach, the three-dimensional (3D) wave propagation physics and the focusing properties of the transducers are not considered, resulting in images with significant artifacts and degraded spatial resolution. Therefore, developing advanced reconstruction algorithms that account for 3D wave propagation effects is important to further enhance breast USCT imaging. To address these challenges and advance 3D breast USCT imaging, this dissertation investigates two main aims. The first aim is to develop a virtual imaging framework for realistic \textit{in silico} 3D breast USCT imaging studies, including stochastic, realistic virtual breast phantoms and high-fidelity virtual data acquisition models. The second aim focuses on proposing advanced high-resolution 3D image reconstruction techniques, including model-based and deep learning-accelerated approaches. To advance the development and evaluation of reconstruction algorithms for new medical imaging technologies, computer simulation studies, commonly known as virtual imaging trials (VITs), are widely employed. VITs provide researchers with an ethical and controlled mean to explore imaging system designs and reconstruction methods, particularly for emerging technologies like USCT, which are still in the early stages of development. In VITs, it is essential to account for variability in the ensemble of objects to be imaged. This variability facilitates the assessment of task-based image quality (IQ) and the optimization of imaging system parameters. To this end, a methodology for generating realistic stochastic 3D numerical breast phantoms is developed, enabling clinically relevant computer simulation studies of USCT breast imaging. These phantoms incorporate anatomical and property variability representative of clinical settings, including differences in breast size, shape, composition, anatomy, and tissue properties. Next, as part of the virtual imaging framework, a high-fidelity 3D ring-array USCT data acquisition model is developed. The ring-array USCT system employs an elevation-focused ultrasonic transducer. Previous work often used simplified imaging models based on 2D wave physics. While computationally efficient, such models have several limitations, including mismatches between 2D and 3D wave physics and simplified transducer models, which are often assumed to be point-like. These mismatches eventually result in image artifacts and reduced spatial resolution in the reconstructed images. To address these challenges, a 3D USCT forward model was developed, incorporating the spatial impulse response of the transducer for accurate wave simulation. This model accounts for out-of-plane acoustic scattering and the transducer's focusing properties in both transmit and receive modes, which are crucial for enabling accurate ring-array USCT simulation and reconstruction. To overcome the limitations of the commonly used 2D SBS method and enable high-resolution, accurate USCT imaging, a 3D full waveform inversion (FWI) method is developed. This method incorporates 3D wave physics and transducer focusing properties. Additionally, a multi-ring 3D FWI method is implemented to further enhance reconstruction accuracy by utilizing acoustic data from multiple vertical transducer locations. The proposed 3D image reconstruction methods were evaluated through computer-simulation studies using the developed NBPs and clinical data. The impact of the number of ring-array positions on image accuracy and vertical resolution was also systematically assessed. Although 3D FWI has great potential for accurate, high-resolution imaging, its practical application in USCT has been limited by the significant computational burden associated with repeatedly solving the 3D acoustic wave equation. Thus, there remains an important need for algorithmic innovations that can accelerate 3D FWI so that image reconstruction times can be significantly reduced. To address this, a learning-based approach is developed to map the 3D ring-array USCT data to idealized 2D USCT measurements, allowing a faster and more accurate 2D reconstruction method. Additionally, the use of multiple ring-array measurements from adjacent elevations is explored as a multi-channel input to the neural network, showing improved accuracy compared to only using single-ring data. In summary, the methods proposed in this dissertation have the potential to push the boundaries of what a ring-array USCT system in transmit mode can achieve, enabling the creation of images that depict the distribution of acoustic properties in tissue with high spatial resolution and accuracy. Eventually, these methods may benefit women by improving the early detection and diagnosis of breast conditions, especially for those with dense breast tissue
Subdimensional expansion method for multi-agent path finding with long narrow corridors
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Haoyuan You, accepted the attached license on 2025-07-24 at 20:31.The student, Haoyuan You, submitted this Thesis for approval on 2025-07-24 at 20:41.This Thesis was approved for publication on 2025-07-25 at 09:27.DSpace SAF Submission Ingestion Package generated from Vireo submission #22732 on 2025-10-21 at 10:06:21Multi-agent path finding, a problem largely related to the field of robotics, has been proven to be an NP-Hard problem and computationally heavy. Experience-based planning method is a sample-based method that uses a pre-computed database to reduce the planning time. We propose an algorithm that extends based on a prior experience-based framework to target specifically multi-agent path planning problems with long narrow corridors. By leveraging graph homomorphism, we expand a database for doorway problems to cover corridors with various shapes and sizes. The algorithm can find solutions for MAPF problems with a consistent 60-70% higher success rate compared to multiple baselines in environments with multiple narrow corridors without sacrificing performance. The proposed algorithm shows the ability to plan the paths for about a hundred robots in congested simulated environments with and without narrow corridors within a few seconds
Chasing the “tail at scale”: toward cloud-native architectures
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Jovan Stojkovic, accepted the attached license on 2025-07-07 at 20:51.The student, Jovan Stojkovic, submitted this Dissertation for approval on 2025-07-07 at 21:00.This Dissertation was approved for publication on 2025-07-08 at 13:29.DSpace SAF Submission Ingestion Package generated from Vireo submission #22435 on 2025-10-25 at 15:30:51Cloud computing is undergoing a radical transformation with the emergence of lightweight cloud-native computing paradigms, such as microservices and serverless computing. Users build their applications by combining services, benefiting from a simplified programming model and fine-grained billing. At the same time, providers consolidate many services into a smaller number of servers, improving the utilization of their infrastructure. However, the detailed characterization of cloud-native environments presented in this thesis shows that these workloads differ significantly from traditional monolithic applications. They execute services that run for short times, exhibit bursty invocation patterns, and have frequent I/O operations that cause context switches. In addition to their core logic, services also execute many auxiliary operations known as datacenter tax, such as data serialization and encryption. Finally, services have stringent tail latency bounds, requiring the slowest requests to complete within a strict deadline. These characteristics result in significant inefficiencies in performance, energy, and resource utilization when cloud-native workloads run on conventional servers with conventional software stacks, negating the paradigm’s potential benefits. The goal of this thesis is to design hardware platforms and software stacks that enable the execution of cloud-native workloads with orders of magnitude better efficiency. The first part of the thesis designs a new hardware stack for cloud-native services. It introduces µManycore, a CPU architecture that minimizes the tail latency of cloud-native services. The thesis then extends the architecture with HardHarvest to boost utilization via hardware-based core harvesting, and refines the microarchitecture with Mosaic for better performance under frequent context switches. Finally, this thesis integrates on-package accelerators into the architecture and proposes AccelFlow, a framework that enables fine-grained, low-overhead orchestration of accelerators to reduce the datacenter tax in cloud-native environments. To maximize the efficiency of the proposed hardware architecture, the second part of the thesis builds a full software stack that is tightly co-designed with the hardware. It begins with MXFaaS, a mechanism that improves resource utilization by efficiently multiplexing resources during bursts of same-function invocations. Then, it integrates the novel Concord distributed caching system for FaaS environments, and uses SpecFaaS to accelerate end-toend application workflows through speculative service execution. Finally, this thesis improves the energy efficiency of cloud-native environments with two frameworks: EcoFaaS, which uses fine-grained scheduling and dynamic frequency scaling, and SmartOClock, which underprovisions resources and selectively overclocks cores during load spikes
Until I overflow: queer Pentecostal intimacies in & beyond Brazil
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Joseph Coyle, accepted the attached license on 2025-07-15 at 13:07.The student, Joseph Coyle, submitted this Dissertation for approval on 2025-07-15 at 13:12.This Dissertation was approved for publication on 2025-07-16 at 07:50.DSpace SAF Submission Ingestion Package generated from Vireo submission #22582 on 2025-10-25 at 15:31:01This dissertation ethnographically examines Pentecostal igrejas inclusivas (inclusive churches) and the everyday intimacies of queer Pentecostal life. It argues that the intersection of queer and Pentecostal is not one of contradistinction but of possibility for imagining otherwise the intimacies and affects that shape minoritarian life. By tracking queer Pentecostal intimacies in spaces of friendship, kinship, imagination, conversation, dreams, and migrant and diasporic life, the dissertation illustrates the importance of intimacy and the minor in animating contemporary queer world-making projects, especially those that do not necessarily announce themselves as legible interventions into the political
Three essays in U.S. biofuels markets
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-08-01The student, Maria Gerveni, accepted the attached license on 2025-07-16 at 14:55.The student, Maria Gerveni, submitted this Dissertation for approval on 2025-07-16 at 14:56.This Dissertation was approved for publication on 2025-07-17 at 18:33.DSpace SAF Submission Ingestion Package generated from Vireo submission #22606 on 2025-10-25 at 15:31:02This dissertation consists of three chapters that collectively provide a comprehensive framework for understanding price dynamics, market interconnections, and regulatory mechanisms in U.S. biofuel markets. The dissertation investigates the complex relationships between ethanol, biodiesel, petroleum diesel markets, and the Renewable Identification Number (RIN) compliance system, all critical components of the Renewable Fuel Standard (RFS) policy framework. By quantifying price connectedness across different biofuel markets, developing an innovative methodology for estimating the RINs bank and estimating the relationship of RIN prices to the ratio of the RIN bank size relative to the annual RVOs, this dissertation contributes valuable insights for market participants, policymakers, and researchers seeking to navigate the evolving landscape of renewable energy markets. Each chapter builds upon the others, moving from ethanol terminal markets to biodiesel and petroleum diesel relationships, and finally to the regulatory mechanism (RINs) that binds these markets together under the RFS. Through this structure, the dissertation reveals how information flows across regional markets, how shocks propagate through supply chains, and how compliance mechanisms function across time, providing a multidimensional view of the U.S. biofuels sector. The first chapter (joint work with Teresa Serra, Scott H. Irwin and Todd Hubbs) focuses on Price Connectedness in U.S. Ethanol Terminal Markets. This essay shows, for the first time, the degree of price connectedness across the major regional ethanol markets in the U.S. Connectedness measures are based on forecast error variance decompositions that inform which prices drive system dynamics. We pay special attention to volatility spillovers to and from Chicago, as it is equipped with one of the largest terminals in the U.S. and is widely regarded as the center of ethanol price discovery in the country. Ethanol prices in the Chicago terminal electronic trading platform are also suspected of being manipulated over the 2017‐2019 period. We use Diebold and Yilmaz (2012 and 2014) and a rolling window approach to study the dynamics of price connectedness over time. Using daily data from 2013 to the beginning of 2021, we find that Chicago is the market that generates the most innovations to other market prices. In contrast, Chicago receives the least amount of innovations from all other markets, placing Chicago at the center of price dynamics. We find that price connectedness measures are correlated with market fundamentals, policy, and concentration in the Chicago terminal electronic trading platform, with the latter being associated to an increase in the relevance of Chicago as a central market. The second chapter (joint work with Scott H. Irwin and Teresa Serra) focuses on Price Connectedness in U.S. Biodiesel and Petroleum Diesel Markets. This essay quantifies price connectedness across U.S. biodiesel plant-level and wholesale markets, as well as between biodiesel and petroleum diesel wholesale markets. Using Diebold-Yilmaz (2012, 2014) connectedness measures and a rolling window approach, we examine how price connectedness evolves across regions and supply chain levels. We find strong intra-supply chain connectedness within biodiesel markets and cross-market connectedness between biodiesel and petroleum diesel wholesale markets. Connectedness declines sharply post-2022, coinciding with the renewable diesel boom. Regression results show that rising renewable diesel production reduces price connectedness by displacing both biodiesel and petroleum diesel, while production levels, feedstock costs, net imports, and policy support are positively correlated with connectedness. The third chapter (joint work with Scott H. Irwin and Todd Hubbs) focuses on RIN Bank: Estimation and Relationship with RIN Prices. This essay addresses a critical gap in renewable fuel policy analysis by developing the a systematic methodology to estimate annual RIN (Renewable Identification Number) bank levels under the U.S. Renewable Fuel Standard and examining the relationship of the RIN bank-to-RVO ratio with RIN prices. Establishing a balance sheet framework analogous to grain commodity accounting, we estimate RIN bank levels from 2010-2023 using publicly available EPA data. Our estimates reveal two distinct boom-and-bust cycles, with the total RIN bank peaking at 4.1 billion gallons in 2011 and 3.8 billion gallons in 2017, before declining to a historic low of 360 million gallons in 2022. Despite a limited 13-observation sample, econometric analysis demonstrates statistically significant inverse relationships between RIN bank-to-RVO ratios and RIN prices across multiple functional forms, consistent with established storage theory. These findings provide essential analytical tools for policymakers as the RFS enters discretionary target-setting beyond 2022 statutory mandates. The methodology enables assessment of proposed reforms through their expected effects on banking levels and associated price impacts, while the price relationships offer insights for managing compliance costs and market stability in evolving biofuel markets
Nanoparticles as delivery systems
No full textSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01The student, Catherine Jalomo, accepted the attached license on 2025-07-17 at 10:10.The student, Catherine Jalomo, submitted this Dissertation for approval on 2025-07-17 at 10:16.This Dissertation was approved for publication on 2025-07-18 at 10:31.DSpace SAF Submission Ingestion Package generated from Vireo submission #21660 on 2025-10-25 at 15:52:17Nanomaterials have been studied as drug delivery systems due to their tunable properties, including size and surface chemistry. Engineered nanomaterials can also be loaded with species that act as therapeutic agents. Loading and release of active agents from nanoparticles have been studied to optimize targeted delivery of pharmaceuticals, but less research has been done to apply nanocarriers to agrochemical delivery. In both human and plant pathology, controlled release systems have been used to mitigate the toxicity and side effects of treatment while maintaining a therapeutic dose. Hydrogels are used as a hydrophilic, biocompatible polymer matrix that can load both metal cations and small molecules, and release is closely tied to inter- and intramolecular interactions. Adapting hydrogels on the nanoscale can expand applications to delivery of both pharmaceuticals and agrochemicals. The work presented in this dissertation investigates the synthesis of hydrogel nanoparticles for controlled release of active agents, including the modification of polymeric matrices to tune delivery. In Chapter 1, the use of nanomaterials as delivery systems for therapeutic agents is discussed in detail. Background on the use of nanoparticles for controlled release, as well as an overview of the advantages of using hydrogel-based nanoparticles will be provided. Insights into the nanoparticle design and mechanisms of controlled release are also presented. The work presented in Chapter 2 examines the design and tunability of alginate-based hydrogel nanoparticles for release of copper (II), a micronutrient required for plant health that can improve crop yield of infected tomato plants. A small library of hydrogel nanoparticles with different polymer compositions was prepared to investigate the relationship between polymer matrix and both copper loading and release. The best performing hydrogel nanoparticles were composed of alginate, which coordinates Cu2+, and non-crosslinking biopolymer chitin. Chapter 3 discusses efforts to improve copper loading in and release from hydrogel nanoparticles. First, copper content in hydrogel nanoparticles was increased by incubating copper crosslinked hydrogel nanoparticles in an additional copper (II) sulfate treatment. Hydrogel nanoparticles that were twice loaded with Cu2+ contained and released more copper than nanoparticles without secondary treatment. In addition, unmodified hydrogel nanoparticles were exposed to a photoactive small molecule that competitively binds to copper coordination sites to displace Cu2+. Chapter 4 introduces greenhouse experiments that investigate the effects of copper-crosslinked hydrogel nanoparticles on diseased tomato plants. Tomato plants suffering from fungal infection can benefit from application of copper (II), but benefits are dose-dependent. In the greenhouse studies performed in this study, hydrogel nanoparticles were applied foliarly to infected tomato plants and the effects on biomass, enzymatic activity, and chlorophyll content were observed. Treating diseased tomato plants with hydrogel nanoparticles resulted in an increase in crop yield, indicating that hydrogel nanoparticles can be an effective delivery method for copper (II) and can reduced the effects of fungal infection. Preliminary efforts to expand encapsulation of active agents to include small molecules antifungals are outlined in Chapter 5. Incorporation of hydrophobic small molecules in biopolymeric hydrogels is a challenge for hydrophilic controlled release materials, and this trend is also observed on the nanoscale. While further optimization is needed improve encapsulation efficiency, hydrogel nanoparticles can incorporate the hydrophobic fungicides pydiflumetofen and fluopyram. Finally, Chapter 6 presents hydrogel-wrapped gold nanoparticles for photothermal therapy and delivery of anticancer agents. In this work, a hydrogel-based nanoparticle with a gold nanorod core is discussed as a plasmonic-enabled nanocarrier for targeted drug delivery. This system differs from previously discussed alginate hydrogel nanoparticles because it relies on thermally-responsive synthetic polymers derived from poly(N-isopropylacrylamide) and poly(ethylene glycol)
Soñamos vida digna: a multiple qualitative methods’ account of Puerto Rican feminists’ experiences with organizing and activism
No full textSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01The student, Amaury Rijo Sanchez, accepted the attached license on 2025-06-20 at 12:46.The student, Amaury Rijo Sanchez, submitted this Dissertation for approval on 2025-06-20 at 13:03.This Dissertation was approved for publication on 2025-07-01 at 13:41.DSpace SAF Submission Ingestion Package generated from Vireo submission #22349 on 2025-10-25 at 15:52:34Recurring, atmospheric devastations, an inconsistent and violent relationship between Puerto Rico and the United States, and the detrimental effects of capitalist globalization on the Caribbean archipelago overwhelmingly characterize and dictate precarious living conditions of Puerto Ricans. Nevertheless, Puerto Rican feminists living and organizing in the islands have reenergized and given breath to a new wave of social justice advocacy concerned with the overall welfare of the islands’ populations. Their distrust for and critiques of the different modes of economic extractivism, austerity, and disaster capitalism transpire in their multifaceted approaches to environmental and community protection and regeneration. Some of these approaches are seen through active resistance to state power, material and educational support for Puerto Rican populations, and place-making practices that promote solidarity and ensure the livelihood of marginalized individuals. Although these developments are actively discussed and critiqued within academic and mainstream platforms based in Puerto Rico, scholarship in the United States within the social sciences falls far behind when accounting for the lived experiences, activist strategies, and place-making practices of Puerto Rican feminists who operate within the recent wave of Puerto Rican environmental and social justice advocacy. Further, the type of feminism practiced in Puerto Rico provides fertile grounds for transnational feminist analysis as well as a critical approach to thinking through social and decolonial theories. This multiple qualitative methods research study is concerned with the exploration of Puerto Rican feminist approaches to the tensions, violence, and extraction that threaten the livelihood of Puerto Ricans, with keen focus on women and minorities. It explores feminist strategies foregrounded in safety, solidarity, and regeneration. This study stands out for its valuable contribution to sociological literature on violence, inequalities, social justice movements, and transnational feminisms
Flow electrooxidation of glycerol for industrial-scale applications
No full textSubmission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01The student, Rachel Gaines, accepted the attached license on 2025-07-02 at 09:35.The student, Rachel Gaines, submitted this Dissertation for approval on 2025-07-02 at 09:47.This Dissertation was approved for publication on 2025-07-10 at 16:38.DSpace SAF Submission Ingestion Package generated from Vireo submission #22399 on 2025-10-25 at 15:52:56Disposal of waste from manufacturing activities threatens human health, process profitability, and environmental sustainability. Purification and/or valorization of this waste addresses all three of these critical challenges. Electrocatalysis is a promising method for purification and valorization of waste, as it is compatible with renewable electricity and can produce two different products (one from the oxidation reaction and one from the reduction reaction) for the same energy input. In this dissertation, I focus on a specific type of carbonaceous waste – glycerol. Glycerol is a byproduct of biodiesel production, and its electrocatalytic valorization can enhance the financial and environmental sustainability of biodiesel manufacturing. I evaluate the valorization of waste glycerol through three objectives: (1) Can glycerol electrocatalysis be scaled from batch to flow systems, to improve translatability into industrial manufacturing facilities? (Chapters 2-3) (2) Can industrially-sourced waste glycerol be effectively valorized in flow systems? (Chapters 4-5) (3) Can electrocatalysis of waste glycerol be paired with another waste stream to generate additional valuable chemicals? (Chapter 6) I address these questions by using flow electrolyzers, multivariate optimization protocols, and a comprehensive analysis of catalyst materials, feed compositions, and operating regimes. My results demonstrate optimal conditions for reaction selectivity and activity; key catalysts and operating conditions for reaction stability and economic viability; and requisite operating conditions for multi-waste valorizability. These results lay the foundation for economically-viable scale-up of waste glycerol electrocatalysis. More broadly, they demonstrate a cohesive evaluation of the opportunities present in valorization of complex waste streams