A sampler has been fabricated to facilitate the in-situ speciation of Cr. Teflon® was selected as the material for the samplers because of its inert chemical nature. The design of the sampler is based on the Supported Liquid Membrane (SLM) extraction technique, which utilizes charged organic carrier molecules loaded onto a polymeric (Teflon®) support membrane and the principles of electrostatics to selectively transport Cr ions through an ion-pairing mechanism. Cr ions in the feed solution that have an opposite charge from the carrier molecule form an ion-pair with the carrier and are transported through the membrane and deposited into a second aqueous phase referred to as the acceptor phase. A counter-ion from the acceptor phase is exchanged for the Cr ion to complete the extraction process. Since the acceptor phase is contained in a Teflon® bottle, the SLM sampler is capable of speciation and storage of Cr ions, which is a major advantage over current speciation techniques. A food coloring test was used to check the samplers for leaks. A plastic barrier was used in place of the polymeric membrane and the acceptor phase bottle was filled with DI water. The sampler was submerged in a beaker containing food coloring and DI water. The bottle contents were checked for the presence of food coloring using UV-vis spectroscopy. The sampler was determined to be leak-free if the bottle did not contain food coloring. All systems prepared were validated upon the initial test and required no further manipulation to ensure structural soundness. The SLM extraction technique involves two liquid-liquid extractions (LLEs). Before the samplers could be evaluated for their performance and stability in Cr speciation applications, liquid-liquid extraction studies were conducted on both systems (Cr (III) and Cr (VI)) to determine the optimal operating parameters (carrier concentration, decanol concentration, and acceptor phase concentration) of the SLM system. The selectivity of each system was also evaluated to validate proper SLM function. The performance of the samplers was evaluated in a series of tank studies that focused on the uptake of Cr into the acceptor phase as well as the depletion of Cr ion from this phase. The goal of the performance studies was to determine the mechanical and chemical stability of the SLM samplers. As part of the validation process, selectivity studies and studies without the carrier molecule were conducted to ensure that the systems were functioning according to SLM theory. Tank studies that simulated natural sampling condition were also conducted. The results of the tests conducted in the laboratory indicate that the SLM samplers are a stable, reliable, and viable method for Cr speciation. Future directions of this project will include the incorporation of the SLM sampler into the existing Multi-layer Sampler (MLS) technology as well as the analysis of the stability and performance of the incorporated systems in the ""in-situ speciation application. / Ph. D.

<p dir="ltr">State-level policy decisions significantly influence various aspects of our daily lives, such as access to healthcare and education. Despite their importance, there is a limited understanding of how these policies and decisions are formulated within the legislative process. This dissertation aims to bridge that gap by utilizing data-driven methods and the latest advancements in machine learning (ML) and natural language processing (NLP). By leveraging data-driven approaches, we can achieve a more objective and comprehensive understanding of policy formation. The incorporation of ML and NLP techniques aids in processing and interpreting large volumes of complex legislative texts, uncovering patterns and insights that might be overlooked through manual analysis. In this dissertation, we pose new analytical questions about the state legislative process and address them in three stages:</p><p><br></p><p dir="ltr">First, we aim to understand the language of political agreement and disagreement in legislative texts. We introduce a novel NLP/ML task: predicting significant conflicts among legislators and sharp divisions in their votes on state bills, influenced by factors such as gender, rural-urban divides, and ideological differences. To achieve this, we construct a comprehensive dataset from multiple sources, linking state bills with legislators’ information, geographical data about their districts, and details about donations and donors. We then develop a shared relational and textual deep learning model that captures the interactions between the bill’s text and the legislative context in which it is presented. Our experiments demonstrate that incorporating this context enhances prediction accuracy compared to strong text-based models.</p><p><br></p><p dir="ltr">Second, we analyze the impact of legislation on relevant stakeholders, such as teachers in education bills. We introduce this as a new prediction task within our framework to better understand the state legislative process. To address this task, we enhance our modeling and expand our dataset using various techniques, including crowd-sourcing, to generate labeled data. This approach also helps us decode legislators’ decision-making processes and voting patterns. Consequently, we refine our model to predict the winners and losers of bills, using this information to more accurately forecast the legislative body’s vote breakdown based on demographic and ideological criteria.</p><p><br></p><p dir="ltr">Third, we enhance our analysis and modeling of state-level bills and policies using two techniques: We normalize the inconsistent, verbose, and complex language of state policies by leveraging Generative Large Language Models (LLMs). Additionally, we evaluate the policies within a broader network context by expanding the number of US states analyzed from 3 to 50 and incorporating new data sources, such as interest groups’ ratings of legislators and public information on legislators’ positions on various issues.</p><p><br></p><p dir="ltr">By following these steps in this dissertation, we aim to better understand the legislative processes that shape state-level policies and their far-reaching effects on society.</p>

In the rapidly advancing field of artificial intelligence, Large Language Models (LLMs) like GPT-3, GPT-4, and Gemini have revolutionized sectors by automating complex tasks. Despite their advancements, LLMs and more noticeably smaller language models (SLMs) still face challenges, such as generating unfounded content "hallucinations." This project aims to enhance SLMs for broader accessibility without extensive computational infrastructure. By supervised fine-tuning of smaller models with new datasets, SQUAD-ei and SQUAD-GPT, the resulting model, KERMIT-7B, achieved superior performance in TYDIQA-GoldP, demonstrating improved information extraction while retaining generative quality. / Inom det snabbt växande området artificiell intelligens har stora språkmodeller (LLM) som GPT-3, GPT-4 och Gemini revolutionerat sektorer genom att automatisera komplexa uppgifter. Trots sina framsteg stårdessa modeller, framför allt mindre språkmodeller (SLMs) fortfarande inför utmaningar, till exempel attgenerera ogrundat innehåll "hallucinationer". Denna studie syftar till att förbättra SLMs för bredare till-gänglighet utan krävande infrastruktur. Genom supervised fine-tuning av mindre modeller med nya data-set, SQUAD-ei och SQUAD-GPT, uppnådde den resulterande modellen, KERMIT-7B, överlägsen pre-standa i TYDIQA-GoldP, vilket visar förbättrad informationsutvinning samtidigt som den generativa kva-liteten bibehålls.