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School of Chemistry and Biochemistry

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College of Sciences

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https://finding-aids.library.gatech.edu/agents/corporate_entities/153

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Integrating Machine Learning Solutions into Untargeted Metabolomics and Xenobiotics Workflows

(Georgia Institute of Technology, 2024-05-01) Rainey, Markace Alan

Untargeted metabolomics explores the entirety of small molecules within biological samples, providing insights into metabolic alterations associated with various conditions. Standard methodologies like NMR and LC-MS are pivotal in identifying molecular markers but often fall short in fully deciphering the metabolic landscape due to limitations in accurately annotating a vast number of metabolites. This gap in annotation hampers the diagnostic application and biological interpretation of metabolomic data. Ion mobility spectrometry (IMS) offers a solution by providing semi-orthogonal data that enhances metabolite annotation. IMS separates ions based on their collision cross-section (CCS), a property influenced by an ion's mass, shape, charge, and external factors like temperature and pressure. When integrated with mass spectrometry (MS), IMS aids in resolving ions’ of similar or identical mass-to-charge ratio (m/z), offering a refined approach to metabolite characterization. This thesis focuses on employing computational strategies within LC-IM-MS workflows to facilitate rapid metabolite characterization. Chapter 1 outlines the challenges in metabolomics, specifically the limitations of current LC-MS workflows and the concept of the "dark metabolome." This introductory chapter provides the theoretical framework to better understand ion mobility and the use of quantitative-structural activity relationships to predict molecular properties. The chapter also discusses xenobiotics—external compounds impacting health—and their characterization challenges. Chapter 2 introduces Collision Cross Section Predictor 2.0 (CCSP 2.0), a machine learning-based tool for predicting ion mobility-derived CCS values. CCSP 2.0, developed to improve the accuracy and ease of CCS prediction, is evaluated for its efficacy in enhancing annotation accuracy in LC-MS workflows. It utilizes a support-vector regression model and incorporates a comprehensive library of molecular descriptors, demonstrating superior prediction accuracy and utility in reducing false positive annotations. Chapter 3 presents a workflow for automated detection of polyhalogenated xenobiotics in biological samples using LC-IM-MS. This approach combines CCS to m/z ratios, Kendrick mass defect analysis, and CCS prediction to filter isomeric candidates. A case study on the detection of per- and polyfluorinated alkyl substances in human serum exemplifies the workflow's effectiveness. Chapter 4 describes an analytical chemistry experiment for undergraduate students, focusing on laser-induced breakdown spectroscopy (LIBS) and its application in data science education. This chapter emphasizes enhancing students' programming literacy and analytical skills through hands-on experiments and analysis using Jupyter Notebooks. The experiment, adaptable to various curricula, showcases real-world applications of LIBS, including its use in space exploration. Chapter 5 summarizes key findings from the research, discussing the implications of integrating computational methods in metabolomics and the potential advancements in ion-mobility mass spectrometry. Future research directions are proposed to further explore and refine these methodologies. Appendix A explores an on-going project aimed at predicting analyte concentrations without standard calibration curves using machine learning. This approach predicts relative ionization efficiencies of lipids from their structural properties, demonstrating the potential of machine learning in streamlining quantitative analyses in metabolomics. In conclusion, this thesis underscores the importance of computational approaches in enhancing metabolite annotation and characterizing xenobiotics, contributing valuable tools and methodologies to the field of metabolomics.
Exciton and Charge Carrier Nonlinear Dynamics in Hybrid Organic-Inorganic Metal Halides

(Georgia Institute of Technology, 2024-04-27) Rojas Gatjens, Jorge Esteban

Hybrid-organic inorganic metal-halide semiconductors pose an interesting photophysical scenario. Due to the ionic and dynamic nature of the structure, their excited-state properties are highly correlated with the structure's vibrations, distortions, and solvation dynamics. This thesis studies the interactions between excited states (nonlinear dynamics), their manifestation in physical observables, and their relation to the material's structure and fabrication. We explore the exciton and charge carrier nonlinear dynamics, via incoherent and coherent spectroscopy, in hybrid organic-inorganic mixed-halide lead perovskites, Ruddlesden-Popper metal halides, and perovskite nanocrystals. The many-body interactions manifest in incoherent spectroscopy as nonlinearities in the photoluminescence and/or photocurrent and, in two-dimensional coherent spectroscopy lineshapes, in the spectral linewidth, phase, and many-particle state feature (e.g. biexcitons, trions). For bulk hybrid organic-inorganic mixed-halide lead perovskites, we resolve incoherent nonlinear dynamics with sub-picosecond resolution in the photoluminescence and photocurrent. We were able to characterize the competition between defect-assisted recombination, Auger recombination, and amplified spontaneous emission. For the Ruddlesden-Popper metal halide perovskites and perovskite nanocrystals, we used two-dimensional coherent spectroscopy to explore ultrafast exciton scattering events and exciton-carrier coupling dictating the exciton-quantum dynamics. The work of this thesis sheds light on the many-body interactions in hybrid-organic inorganic metal-halide semiconductors and provides the tools to transition from the description to the control of nonlinear dynamics.
Evolution Before Origin: A Conceptual and Experimental Framework

(Georgia Institute of Technology, 2024-04-27) Matange, Kavita

The search for chemical processes preceding the emergence of life presents a significant challenge in scientific inquiry. The development of biochemistry on early Earth presents some of the most creative chemistry in the known universe. In this work, we present a novel approach to advance our understanding of chemistry on early Earth based on wet-dry cycling. We outline a conceptual and experimental model of chemical evolution. As context, we outline currently accepted models of the origins of life and emphasize the utility of chemical evolution. With water at the center of the model, we demonstrate or infer that chemical systems are capable of selection, coupling, and creativity. We outline the framework of our wet-dry cycling-based model of chemical evolution and hold that during early chemical evolution, molecules were selected based on solubility in water, condensation-dehydration chemistry and resistance to hydrolysis upon assembly assemblies. Our model maps evolutionary concepts onto chemical processes. (a) a generation is a single wet-dry cycle; (b) heredity is information passed from one generation to the next; (c) information is associated with non-random chemical composition; (d) selection results in inheritance of certain molecular species over generations; (e) fitness confers persistence (i.e., survival) of molecules and specific molecular assemblies; (f) variation is spatiotemporal differences in information; and (g) water is an “energy currency” that links reactions and, upon changing activity, alters reaction free energies. In this work, we have employed wet-dry cycling and observed unexpected experimental outcomes like combinatorial compression, synchronicity, and continuous chemical change. We have suggested that biologically relevant concepts like adaptation and exaptation are universal, synergistic, and recursive, and along with biopolymers, apply to small molecules such as metabolites, cofactors, and building blocks of extant polymers. Altogether, this research establishes a platform for the analysis of complex mixtures at the Origin of Life and offers new horizons for the mapping of evolutionary phenomena onto prebiotic systems.
Probing nonlinear phenomena with multidimensional and entangled photon spectroscopies

(Georgia Institute of Technology, 2024-04-25) Malatesta, Ravyn Alysha

This dissertation is concerned with understanding what is learned from novel spectroscopic techniques using entangled photon pairs and how they compare to coherent multidimensional spectroscopy. With both theory and experiment, it considers what is learned by treating the light in light-matter interactions semi-classically (as a periodic perturbation of a quantum system) versus treating light fully quantum-mechanically, taking advantage of quantum characteristics of light such as polarization or time-frequency entanglement.
Quantitative Analysis of Inorganic Chemical Species on an Impact-Penetrator Module

(Georgia Institute of Technology, 2023-04-27) Govindaraj, Chinmayee

In-situ planetary missions to astrobiologically relevant icy worlds with unique access to subsurface samples for habitability analyses are typically carried out using complex and costly soft lander platforms that are large, heavy and power-intensive. Smaller, more power-efficient, lower cost payloads like impactors and penetrators are innovative ways to address these issues. The Ice Shell Impact-Penetrator (IceShIP) is a penetrator science payload under development since 2015. Icy Moon Penetrator Organic Analyzer (IMPOA), is a sub payload of IceShIP, and is a first-of-its-kind, compact science platform capable of sustaining ultra high-g loads of up to 50,000 g, enabling subsurface sampling on icy ocean worlds like Europa, Enceladus, or even Martian polar regions. The IMPOA platform can enable detection of low concentration organic species using the principle of laser-induced fluorescence. To upgrade the impact resistance of the IMPOA payload, several design changes were made, the most prominent of them was to move away from glass microfluidic chip design to polymer-based architecture. To upgrade the scientific capabilities of IceShIP, inorganic detection capability was added by employing the principle of capacitively coupled contactless conductivity detection (C4D). The benchtop instrument was tested using lab-generated Europa-relevant samples, and miniaturized to fit within the IceShIP module. This was named the Micro Inorganic Conductivity Detector for Europa (MicroICE). A complete polymer body version of MicroICE was designed and tested, called the Polymer-based Contactless conductivity Detector for Europan Salts (PolyCODES). PolyCODES is the first C4D device to use the PEDOT:PSS conductive polymer and was a design choice made to increase the potential for impact resistance. MicroICE was equipped with an automated, two-channel microfluidic routing mechanism, called the Solenoid-based actuator assembly for Impact- Penetrators (SIP). The SIP, integrated with MicroICE or PolyCODES demonstrated a low mass, small size, low power instrument at TRL 3. The upgraded IceShIP canister is geared towards high acceleration space flight missions. Future design upgrades could include the integration of microchip capillary electrophoresis. Success during impact tests of components configured to functionally conduct analytical measurements will elevate the readiness to a true TRL of 4 value.
Topology and chemistry of competing interactions in tetravalent lanthanides

(Georgia Institute of Technology, 2023-04-27) Ramanathan, Arun

Lanthanides in the trivalent oxidation state are typically described using an ionic picture that leads to localized magnetic moments. The hierarchical energy scales associated with trivalent lanthanides produce desirable properties for e.g., molecular magnetism, quantum materials, and quantum transduction. Here, we show that this traditional ionic paradigm breaks down for praseodymium in the 4+ oxidation state. Synthetic, spectroscopic, and theoretical tools deployed on several solid-state Pr4+-oxides uncover the unusual participation of 4f orbitals in bonding and the anomalous hybridization of the 4f1 configuration with ligand valence electrons, analogous to transition metals. The resulting competition between crystal-field and spin-orbit-coupling interactions fundamentally transforms the spin-orbital magnetism of Pr4+, which departs from the Jeff = 1/2 limit and resembles that of highvalent actinides. Our results show that Pr4+ ions are in a class on their own, where the hierarchy of single-ion energy scales can be tailored to explore new correlated phenomena in quantum materials.
Versatile Design Strategies of Protein Nanoparticles for Vaccines and Drug Delivery

(Georgia Institute of Technology, 2023-04-25) Bhattacharya, Sonia Bholanath

The broad theme of this thesis is designing protein nanoparticles with applications in vaccination and targeted delivery. We bring focus to three protein nanoparticle scaffolds in this thesis- bacterial sourced encapsulins and viral sourced PP7 and Qb - all with variety of applications in the literature. We establish the natural immunological properties of the encapsulin scaffold for engineering peptide nanoparticle vaccines. For further vaccine development efforts using these particles, we delve into ideas about optimum peptide antigen conformations that can be displayed on these particles along with heterologous combination vaccine therapies. We take cues from peptide’s native design and evaluate heterologous vaccination strategies with epitope focusing in mind in two ways. We immunofocus on peptide epitope’s sequence and conformation to develop monoclonal antibodies by sequentially priming and boosting with PP7-displaying linear or loop conformation peptide sequence then with whole protein containing the same peptide sequence in native conformation. Alternately, we design a similar vaccination strategy that displays same peptide antigen in a biologically relevant loop conformation but put them on two scaffold- PP7 and Enc- to improve peptide recognition. We inquire into the effects of directionality of introducing the scaffold to the immune system on antigenic peptide responses. Additionally, I discuss our preliminary efforts at targeting multiple epitopes for leishmaniasis vaccine with no commercial precedence. Finally, we explore the glycan ligand multivalency on surface functionalized Qb particles and show improved targeted delivery in hepatocytes. The collective work presented here is meant to guide the vaccine and drug delivery community on optimum design strategies of the plug-and-play particles.
Metabolomics and Machine Learning for Early-Stage Cancer Diagnosis

(Georgia Institute of Technology, 2023-04-25) Sah, Samyukta

Amongst all omics sciences, metabolomics is the most recent and is rapidly advancing as one of the predominant methodologies for early disease diagnosis and precision medicine. Metabolomics involves the high-throughput analysis of low molecular weight metabolites and their interactions with biological networks. Metabolomics studies involve measuring the abundance of hundreds to thousands of metabolites in biological fluids, tissues, and cells, and provide instantaneous snapshot of the status of the biological system. For cancer research, metabolomics is a powerful platform as it enables the identification of metabolic alterations useful in diagnosis, prognosis, and therapeutics. However, because of the complexity of the metabolome, no single analytical platform can capture the complete metabolic profile of a biological system. Thus, the use of complementary techniques allows for a more comprehensive analysis. Mass spectrometry (MS) is one of the commonly used techniques in metabolomics. Due to its high sensitivity and high-resolution, MS based metabolomics studies can provide a wide breath of knowledge of cancer metabolism. MS is typically coupled with separation techniques such as liquid chromatography (LC) or capillary electrophoresis to reduce the spectral complexity. MS based metabolomics studies generate a large amount of data and thus the use of machine learning (ML) methods are becoming increasingly popular to interpret and visualize metabolomics data and uncover new biological insights into disease biology. This thesis work focuses on MS based metabolomics analysis for improved understanding of cancer metabolism. As diagnosis of ovarian cancer (OC) remains an unmet clinical challenge, the main focus of this thesis is on uncovering the metabolic profile of OC and identifying potential biomarkers for its early diagnosis. In addition, ML methods were applied to identify the urinary metabolic profile of renal cell carcinoma (RCC). Kidney cancer is one the most lethal urinary cancers out of which 90% are renal cell carcinomas (RCC). Diagnosis of RCC is typically performed with expensive imagining tests and biopsies which not only are invasive but also are prone to sampling errors. Due to the proximity of the tumor to the urine, urine metabolomic profiling provides an excellent opportunity to study the metabolic rewiring of RCC. Chapter 1 introduces OC and the application of metabolomics to identify the metabolic reprogramming associated with OC pathogenesis. An overview of the analytical platforms and techniques in metabolomics is given, and the general workflow, including the use of ML in metabolomics data handling, is outlined. The commonly used statistical approaches and ML assisted metabolomics analysis for cancer research are provided. Furthermore, an overview of various MS based metabolomics studies of OC is given, describing the metabolic phenotype of OC. The diagnostic potential of the metabolite panels, as identified by the described studies, is also given. Finally, the biological implications of the potentially important metabolic alterations in OC are described. Chapter 2 presents a longitudinal serum metabolomics profiling of a triple-mutant (TKO) mouse model of high-grade serous carcinoma (HGSC), a subtype of OC. Two complementary ultrahigh performance liquid chromatography (UHPLC) – MS techniques were used to profile both the serum lipidome and the polar metabolome of TKO and TKO control mice. Sequentially collected serum samples from TKO mice starting from 8 weeks of age until death were analyzed, and a comprehensive metabolic map associated HGSC onset, development, and progression was revealed. These UHPLC-MS experiments were complemented with spatial lipidomic profiling of the entire reproductive system of the TKO mice. Ultrahigh-resolution matrix assisted laser desorption/ionization (MALDI) mass spectrometry was used to visualize the lipid alterations in HGSC. The longitudinal analysis of the serum metabolome revealed specific temporal trends for 17 lipid classes, as well as for other polar metabolites including amino acids, TCA acids, bile acids, bile acid derivatives, progesterone metabolites, and metabolites of arachidonic acid. Spatial lipidomic experiments provided a map of the metabolic alterations of HGSC that showed accumulation and reduction of various lipid classes, indicating the observed changes in the serum are in fact a signature of OC. Chapter 3 describes the serum lipidomic alterations in another mouse model of HGSC. For this study, sequentially collected serum samples from a double knockout mouse model (DKO) of HGSC were analyzed with reverse phase (RP) UHPLC-MS. Similar to the serum collection protocol followed for the TKO mouse in chapter 2, serum samples from these DKO mice were collected biweekly starting from 8 weeks of age until death or humane endpoint for sacrifice. Longitudinal lipidomic alterations in the DKO were investigated via machine learning (ML) approaches. A hierarchical clustering analysis identified 4 main lipid trajectory clusters. These clusters mainly consisted of glycerophospholipids including ether linked phospholipids and sphingolipids. Of note, early disease stages were marked by changes in phospholipids and sphingolipids while late disease stages showed more diverse changes. Furthermore, the ML based approaches characterized lipidome alterations at various disease stages. Five ML algorithms were used for classification purposes and a 5-lipid panel discriminated early stage DKO mice from DKO control mice with the area under curve (AUC) value of 0.80, indicating the possibility of employing circulating lipid markers for early detection. Animal models of OC provide a simpler, better-controlled model to study metabolic alterations which can later potentially be translated to humans. In addition, as obtaining early-stage OC samples from humans is challenging, animal models provide a rare opportunity to study early disease stages. However, the goal of these metabolomics studies is to develop clinically relevant biomarkers that aid in selecting therapeutic strategies, and thus investigating and validating metabolic changes in humans is crucial for assessing their translational implications. Additionally, besides detecting metabolic alterations in OC compared to controls with no gynecological malignancies, distinguishing OC from other benign or cancerous gynecological malignancies remains a challenge with significant implications on patient survival. Better results are observed when women with OC are correctly diagnosed and ensured the optimal treatment. In Chapter 4, a comprehensive serum lipidome profiling of OC and various other gynecological malignancies including benign ovarian tumor, benign uterine tumor, and cervical cancer (non-OC), is performed with RP UHPLC-MS. This study used serum samples from two independent tissue banks (Dongsan Hospital Human Tissue Bank and Gangnam Severance Hospital Gene Bank) in South Korea. Age matched patient cohort included 208 women with OC of various histological types and disease stages (mean age 51.9 years) and 137 non-OC patients (mean age 49.9 years). Among the OC patients, 93 women had early stage (I and II) OC out of which 31% were of serous histology. Serous tumors accounted for 86% of late stage (III and IV) cases while the remaining 14% of the cases included clear cell, transitional, mucinous, and carcinosarcoma subtypes. The serum lipidome of OC showed most lipid species to be reduced in OC with some lipid classes, including ceramides and triglycerides, showing increased abundance. Stage-stratified analyses were conducted to investigate if lipids show distinct alterations in early stage (I and II) or late stage (III and IV) OC versus non-OC. While both early OC or later OC vs. non-OC conditions showed alterations in various lipid classes including phospholipids and sphingolipids, changes in certain lipid species such as diglycerides, fatty acids and cholesterol were distinctive of advanced stage OC vs. non-OC. Moreover, results indicated that lipidome alterations in OC were present when the cancer was localized, and those changes amplified as the disease progresses. Besides, OC of different histological types showed similar lipidome changes for most lipid classes. Additionally, a panel of 10 top discriminating lipids, consisting mainly of ether phospholipids, phosphatidylcholine species, and one sphingomyelin species, was selected that differentiated OC from non-OC conditions with AUC of 0.91 and 0.74 in training and test sets, respectively. These results provided a systemic analysis of circulating lipidomic alterations in OC patients, highlighting the potential of lipids as a complementary class of blood-based biomarkers for OC diagnosis. Although UHPLC-MS remains one of the major tools in metabolomics studies, the use of complementary analytical techniques holds promise to significantly improve metabolite coverage. Capillary electrophoresis (CE) coupled with high-resolution MS (HRMS) offers high selectivity and sensitivity for charged polar metabolites and provides a complementary view of the metabolome. Recent advances in CE-MS technologies include small, chip-based CE systems coupled with nanoelectrospray ionization (nanoESI), enabling fast and sensitive analysis. In Chapter 5, a microchip CE (µCE)-MS based targeted metabolomics assay was developed to analyze 40 key metabolites related to cancer progression. A commercial µCE-MS system from 908 devices (Boston MA) was coupled with high-resolution accurate mass Q Exactive plus mass spectrometer (Thermo Fisher, MA). The developed method was applied to biweekly collected serum samples from 3 TKO and 3 TKO control mice of HGSC. The µCE-MS method produced sharp baseline resolved peak shapes and calibration curves maintained good linearity. When applied to serum samples from the TKO mouse, 30 metabolites were successfully detected. These included amino acids, amino acids derivatives, and nucleotides. The data collected from this µCE-MS platform were compared with the previously collected UHPLC-MS data in Chapter 2. Time-resolved data for the 5 metabolites detected with both platforms showed identical temporal trends, indicating the µCE-MS method performed satisfactorily in capturing granular time-course data in complex biological matrices. Chapter 6 presents the major conclusions drawn from this thesis work. Metabolic alterations in the two animal models of OC are discussed, highlighting the similarities and differences observed in each case. A summary of conclusions from the lipidomic profiling of human ovarian cancer samples is presented, and the results from animal models of OC are compared with alterations observed in humans. Possible future directions to continue with this work are also discussed. Appendix A presents a pilot study to investigate the feasibility of liquid-based Papanicolaou (Pap) tests as biospecimens for OC detection. As past studies have observed OC cells in Pap tests, we hypothesized that lipid alterations that are observed in serum and ovarian tissues can also be present in cells obtained in a Pap test. To test this hypothesis, a UHPLC-MS based lipidomics pipeline is developed and a liquid-based Pap test sample from a woman with normal cytology was analyzed. Cells were pelleted out followed by an extraction protocol to extract lipids. Results showed that lipids can be detected in these cell pellets. The detected lipids included sphingolipids, phospholipids, ether linked phospholipids, and glycerolipids. These results suggest a possibility of developing non-invasive techniques for OC diagnosis and detection. Appendix B outlines collaborative metabolomics work that combined both nuclear magnetic resonance (NMR) and UHPLC-MS datasets and used ML to discover urine-based candidate biomarkers for RCC prediction. Urine samples from patients at Emory University Hospital with a solid renal mass that were confirmed to be RCC were used for this study. Controls were identified during the annual physical exam. The study cohort consisted of 105 RCC patients and 179 controls, which is larger than most previously published studies. ML based feature selection led to a panel of seven metabolites that discriminated RCC from controls with 88% accuracy, 94% sensitivity, 85% specificity and AUC of 0.98 in the test cohort. This panel consisted of metabolites that were detected on the MS platform. High resolution MS and tandem MS experiments were conducted to assign metabolite annotation. The annotated metabolites included 2-phenylacetamide, lysine-isoleucine or lysine-leucine, hippuric acid, a mannitol hippurate derivative, N-acetyl-glucosaminic acid and two exogenous metabolites: 2-mercaptobenzothiazole, and dibutylamine. Furthermore, Appendix C presents a machine learning based urine metabolomics study to identify the metabolites associated with RCC staging and to estimate RCC tumor size. The same dataset that was collected for the analysis in Appendix B was used in this study. The metabolites associated with RCC progression included 3-hydroxyanthranilic acid, lysyl-glycine, glycine, and citrate. Overall, this multiplatform metabolomics study provided a broad coverage of metabolites and provided a complementary view of the urine metabolome of RCC. These results suggest the use of urinary metabolomics profiling as a promising platform for RCC detection.
Development of Functional Enyne Molecules to Control Degradability and Architecture in Metathesis Polymers

(Georgia Institute of Technology, 2023-04-19) Sui, Xuelin

Cascade metathesis reactions using Grubbs-catalysts are intramolecular reorganization reactions between adjacent unsaturated bonds to produce a conjugated 1,3-diene or triene, which has driven new insights into the preparation of functional polymers. However, the usage of the efficient cascade metathesis has not been well-studied with respect to other aspects of metathesis polymerization. In this dissertation, various functional enyne reagents were explored to mediate polymer degradation, ruthenium metal residue removal, and cyclic topology control of metathesis polymerization. Utilizing the unexpected metathesis reaction between terminal alkynes and ruthenium Fisher carbenes, various enyne molecules (Chapter 2) or diyne molecules (Chapter 3) are shown to copolymerize with cyclic enol ethers with low strain energy to give degradable alternating copolymers. Additionally, a fluorous enyne molecule can efficiently terminate the active ruthenium chain end in ring-opening metathesis polymerization (ROMP) to facilitate efficient ruthenium removal via straightforward sequestration methods (Chapter 4). Finally, the enyne derivatives can be utilized to give cyclic Grubbs-type initiators for the construction of cyclic macromolecular structures via ring-expansion metathesis polymerization (REMP) (Chapter 5).
A Study into the Design, Synthesis, and Application of Asymmetric Catalyst Systems

(Georgia Institute of Technology, 2023-04-17) Benda, Meghan

The importance of asymmetry, or chirality, in the natural world cannot be overstated and its role has been extensively studied in the fields of chemistry, biology, physics, materials and more. As a result, researchers have dedicated immense time, resources, and funding to the development of chiral resolution and asymmetric induction techniques. In this thesis, we focus on the design, synthesis, and applications of asymmetric catalyst systems. First, in a demonstration of the utility of asymmetric catalysis in the production of medicinally relevant materials, we disclose the total synthesis of three 7-keto-9-hydroxy-8,4’-oxyneolignans in up to >99% ee featuring a key Sharpless asymmetric dihydroxylation step. Additionally, we disclose the synthesis and reactivity of a pair of C2-symmetric thiophene-fused cyclopentadienyl ferrocenes. Finally, the attempted formation of a binaphthyl-based chiral calcium disulfonimide complex is discussed including its observed reactivity toward ring-opening cyclizations.