Master of Science (MS), Wright State University, 2024, Chemistry

This study examines the complexation behavior of methylthymol blue (MTB) with strontium, particularly focusing on its application in addressing environmental concerns related to radiotoxic 90Sr. Using spectrophotometric techniques, the binding interactions were analyzed at pH = 7.50, 9.60, and 12.20. Results indicate that MTB forms a 1:1 complex with strontium at pH = 7.50, while at pH = 9.60 and 12.20 were assumed to influence complexation due to changes in ligand deprotonation and electrostatic interactions. Equilibrium constants (K1) = 22 (± 2) and molar absorptivities, ε1 = 6.3 x 10^3 (± 0.0008) L·mol⁻¹·cm⁻¹, and ε2 = 9.73 x (± 0.2)10^3 L·mol⁻¹·cm⁻¹ were determined using Beer's law, alpha fraction plots, and 1:1 Single K model fitting was done in Igor Pro 9 software. These findings highlight MTB's potential for environmental remediation of strontium in aqueous solution, with future research suggested to optimize MTB effectiveness as metal ion indicator.

Doctor of Philosophy in Clinical-Bioanalytical Chemistry, Cleveland State University, 2024, College of Arts and Sciences

Over the past 60 years, mass spectrometry (MS) has emerged as a premier analytical technique for fatty acid (FA) analysis due to its exceptional sensitivity and selectivity. Techniques combining chromatography with MS, such as liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS), have significantly enhanced the accuracy and robustness of FA quantification. Among FA metabolites, polyunsaturated fatty acids (PUFAs), particularly arachidonic acid-derived eicosanoids, play crucial roles in inflammation and disease pathology. Dysregulated eicosanoids are implicated in various diseases, underscoring the need for precise quantification in biological samples. LC-MS is the preferred method for eicosanoid analysis due to their low concentrations in biological matrices. The first part of this dissertation presents the development and validation of an LC-MS/MS method for quantifying 11-dehydrothromboxane B2 (11-DHTXB2) in human urine, a stable metabolite of thromboxane A2. Elevated urinary 11-DHTXB2 levels serve as a robust prognostic biomarker for atherosclerosis progression, offering the potential for monitoring disease progression and evaluating the efficacy of anti-atherogenic therapies. Short-chain fatty acids (SCFAs), produced by bacterial fermentation of undigested dietary components, are vital gut health biomarkers. Alterations in SCFA concentrations reflect gut microbial imbalances and are linked to inflammatory bowel disease (IBD), which arises from genetic, environmental, and microbial factors leading to an aberrant immune response. IBD-associated gut dysbiosis involves a reduction in SCFA-producing bacteria such as Bacteroides, Firmicutes, and Lactobacillus, resulting in significantly altered SCFA levels. GC-MS, widely regarded as the gold standard for analyzing volatile and semi-volatile compounds, excels in SCFA analysis due to its superior separation capabilities. The second part of this dissertation explores the use (open full item for complete abstract)

Doctor of Philosophy in Clinical-Bioanalytical Chemistry, Cleveland State University, 2024, College of Arts and Sciences

Phenolic acids are microbial products of non-absorbed fibers that are fermented by bacteria using indigestible components that reach the gastrointestinal (GI) tract. It has been shown that phenolic acid compounds significantly impact the human body, particularly their microbial metabolism and their ability to influence several aspects of gut homeostasis. For this reason, more attention should be placed on investigating these metabolites. Multiple factors may influence the biosynthesis of phenolic acids, including age, medications, and several environmental factors, but the production of gut metabolites is primarily affected by diet. In this research we aim to enhance the knowledge on the synergistic relationship between gut microbes, diets, and the biosynthesis of phenolic acids. Chapter I summarizes our current understanding of phenolic acid in the GI tract, fermentative pathways, etiology, and beneficial impacts of phenolic acids. In chapter II we aim to construct an in vitro fermentation model to find efficient ways to increase the generation of phenolic acids in the gut using different combinations of probiotics, prebiotics, and other factors, including fats, sugars, and amino acids. Chapter III discusses the development and complete validation of a high-throughput, fast, and reliable liquid chromatography-mass spectrometry method to quantify phenolic acids (ferulic acid, caffeic acid, and gallic acid) in biological samples. In conclusion, there are many studies that support the benefits of prebiotics on our health, but further investigations are needed to understand the interactions between prebiotics and the gut microbiota as they alter the biosynthesis of phenolic acids, which may be vital in enhancing health benefits, including reduced levels of oxidative stress, reduction of inflammation, enhancement of gut microbiota composition, improvement of metabolic health, and contribution to the prevention of chronic diseases such as obesity, diabetes, cardiovascula (open full item for complete abstract)

PhD, University of Cincinnati, 2024, Arts and Sciences: Chemistry

Neurochemicals play a significant role in our health and in devastating diseases. In the last several decades, since they were discovered, they have mainly been researched in the context of their effect on the nervous system. Recently, scientists have uncovered that many neurochemicals play significant roles in the immune system. There is a need to understand what cells release these neurochemicals, and the effect that these molecules have on the surrounding area. In this dissertation, we address these limitations by developing tools to study neuropeptide release in both the nervous and immune systems. Additionally, we worked to resolve neurochemical signaling capabilities in immune cells like CD4 + T cells. Chapter 1 provides a review on the important components of the nervous system and the immune system that are relevant to neurochemical signaling in the spleen and from immune cells. It broadly reviews the function of both DNA and adaptive immune systems along with the cells they use, and how the nervous system operates any important components for neuroimmune signaling in the spleen and with immune cells. Chapter 2 details our development of an electrochemical aptamer-based sensor for neuropeptide Y. This sensor is capable of making dynamic measurements of neuropeptide Y across a biologically relevant concentration range with high specificity. Chapter 3 gives an overview of the state of the field for fluorescent and electrochemical neuropeptide sensors. We highlight the advancements and the current limitations of each of these techniques, with a focus on how the field needs to advance electrochemical sensors to fill the current gaps in neuropeptide signaling. Chapter 4 details are extensive research into neurochemical signaling from CD4+ T cells. In this research, we have discovered that CD4+ T cells release a wide variety of neurochemicals, including serotonin and dopamine. We have also resolved the subsecond signaling of serotonin from the cells. The d (open full item for complete abstract)

PhD, University of Cincinnati, 2024, Arts and Sciences: Chemistry

Surface-enhanced Raman scattering (SERS) has emerged as a versatile tool for biological analysis due to its high sensitivity, high information content, and preparation-free analysis. Gold nanostars (NS) have proven to be particularly effective in SERS applications because of their tunable localized surface plasmon resonance (LSPR) and ease of synthesis. In this work, we explored the reshaping behavior of surfactant-free gold NS, investigating how changes in their morphology affect the LSPR and SERS signal. By monitoring the reshaping kinetics and correlating them with LSPR shifts, we developed a predictive model for optimal SERS substrate fabrication with only a 3% deviation in LSPR. Using SERS substrates, we explored the integration of SERS with an electrokinetic microchip to enhance liquid biopsy capabilities, particularly in cancer diagnostics. By combining this approach with machine learning (ML) and explainable AI methods, we successfully modeled the vibrational fingerprints of small extracellular vesicles (sEVs), providing interpretable predictions for cancer diagnostics. Additionally, we demonstrated SERS' ability to rapidly assess the molecular responses of Escherichia coli to antibacterial agents, differentiating between various mechanisms of action through multivariate analysis. Our study highlights SERS' potential as a robust, non-invasive tool for both diagnostic applications and antibacterial research, offering significant advantages over traditional methods such as genome sequencing and mass spectrometry. To expand this work, we explored surface modifications on plasmonic surfaces, resulting in improving SERS response for the target analyte.

Doctor of Philosophy, The Ohio State University, 2024, Chemistry

Micrometer-size particles of the entropy-stabilized transition metal-based oxide (Mg0.2Co0.2Ni0.2Cu0.2Zn0.2)O (TM-HEO) have demonstrated long-term cycling stability and excellent rate performance against lithium-ion batteries. Such a feat has only been achieved with nanometer-size transition metal binary oxides. This electrochemical performance has been attributed to the structural stabilization effect of magnesium, the intrinsic role of cobalt as the main redox active species, and the entropy stabilization effect demonstrated in this material. Here, we demonstrate the formation of single-phase so-called “medium-entropy” oxides, (Co0.25Ni0.25Cu0.25Zn0.25)O (TM-MEO(–Mg)) and (Mg0.25Ni0.25Cu0.25Zn0.25)O (TM-MEO(–Co)), and show that their electrochemical behavior is similar to that of TM-HEO. The slight difference in capacities is attributed to the number of charges stored per formula unit of material rather than the nature of TM-HEO. The mechanism of lithium interaction with these materials is still poorly understood, partly due to the difficulties characterizing structure at the nanoscale. Operando 7Li nuclear magnetic resonance (NMR) and electrochemical techniques are used to demonstrate that the (de)lithiation of these compounds proceeds via a partially reversible conversion-type reaction mechanism involving the reduction of the transition metal cations to their metallic form during lithiation and the oxidation of these individual metal particles to their oxides form, losing the initial single-phase compound after the first lithiation cycle. This proposed lithiation/delithiation mechanism highly contradicts existing ones in the literature. In addition, the 7Li NMR and electrochemical methods reveal that the conductive carbon black used as an electronic conductor can store a significant amount of charge at low voltage, indicating that it is a major contributor to the additional capacity observed in these entropy-stabilized oxides and transition metal salts. Such (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), University of Dayton, 2024, Mechanical Engineering

Aviation turbine fuel is a complex mixture comprised of thousands of compounds. While organo-oxygen, nitrogen, and sulfur heteroatomic compounds are present in minute quantities (<0.1% by mass), their presence significantly influences fuel thermal stability. In response to the limitations of existing analytical methods, this study developed and validated a novel analytical approach employing hydrophilic interaction liquid chromatography (HILIC) in conjunction with high performance liquid chromatography (HPLC) with electrospray ionization and quadrupole time-of-flight mass spectrometry (ESI-QTOF). The HILIC method offers numerous advantages including rapid and straightforward sample preparation, without the need for extraction, thereby preserving compounds of interest. Moreover, it offers a way to capture precise compound data enabling chemometric analysis for the prediction of the behavior of the complex mixture that is aviation turbine fuel. Development of the HILIC method found column configuration, mobile phase composition, solvent gradient, re-equilibration time, injection volume, dilution factor, and sample solvent to be significant factors effecting separation efficiency and repeatability. For a sample dataset, optimized using a single aviation turbine fuel, retention time shift was able to be reduced from 0.4 minutes and 2.0% relative standard deviation (RSD) to approximately 0.1 minutes with RSD of 0.4% using the newly developed method. In addition, a high number of untargeted molecular features (944) and targeted amines (121) were able to be identified when using optimal method conditions. The optimized HILIC method was used to measure the heteroatom make up of a set of aviation turbine fuels; the subsequent data was then subjected to a rigorous statistical analysis using multiple techniques. Statistical analysis tools including principal component analysis (PCA) and fold change (FC) analysis offer a look inside the chemically complex composition of (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Chemistry

Colon cancer is projected to become the third leading cause of death amongst Americans by 2024. Early onset colorectal cancer (CRC) is also increasing, with predictions showing CRC to be the leading cause of cancer mortality in people between the ages of 20 and 49 in the United States (US) by 2030. Increasing our understanding of this disease will allow quicker, less invasive, and more accurate diagnosis and treatment options. One such tool to deepen our chemical understanding of CRC is mass spectrometry. Mass spectrometry (MS) has allowed us to broadly survey the proteins (proteomics), lipids (lipidomics), and metabolites (metabolomics) within a biological sample. All of these -omics disciplines using MS are able to identify a peptide and/or small molecule by matching various tandem MS spectra to either previously collected or in silico generated library of tandem MS spectra. Statistical analysis of the normalized intensities of these molecules are compared to control samples to determine if a particular analyte is differentially expressed. A literature review of these topics is described in Chapter 1 of this thesis. Starting in Chapter 2, this thesis describes how MS-based omics has been used to deepen our understanding of the biology of a three-dimensional CRC cell culture model when exposed to various different chemotherapeutics. Chapters 2 and 3 describe the distinct molecular differences between two different CRC cell lines inhibited with two generations of Fatty Acid Synthase (FAS) inhibitors. FAS is the enzyme responsible for synthesizing the 16- chain saturated fatty acid (FA) palmitate, supplying the cellular FA pool with palmitate to be used to synthesize more complex lipids. FAS is also observed to be upregulated in various cancers, increasing the interest in drugging this target for therapy. It was observed that the first generation inhibitor caused drastic morphological changes to CRC spheroids. Using untargeted (open full item for complete abstract)

Doctor of Philosophy in Clinical-Bioanalytical Chemistry, Cleveland State University, 2024, College of Arts and Sciences

This dissertation explores the influence of mobile phase (MP) polarity on the separation of steroid enantiomer pairs in reversed-phase high-performance liquid chromatography. Previous studies varied the concentration of a single organic modifier in binary MPs, affecting both the MP strength and polarity. This work isolates the effect of MP polarity on separation alone by using a ternary MP. In this work, MP strength is kept constant by varying the percent water, while the MP polarity is changed by varying the ratio of the two organic modifiers. The combined ET(30) and Kamlet-Taft polarity model for MP polarity components of acidity, basicity, and dipolarity/polarizability was studied. The bile acid enantiomer pairs analyzed were alpha-muricholic acid and beta-muricholic acid, and omega-muricholic acid and gamma-muricholic acid. The separation factor (α) did not correlate with the total MP polarity, but did with a specific combined component polarity. The α versus the summed MP dipolarity/polarizability and basicity plot for all non-acetonitrile (ACN) ternary MPs experiments considered as a whole (six different organic pairs of methanol, isopropanol, dioxane, tetrahydrofuran in water), showed good correlation, except for the dioxane-containing MPs. Examination of previously obtained data of five other steroid enantiomer pairs (3β5β+3α5β-, 3β5α+3α5α-, 5α+5β-abiraterones, 17α- + 17β-estradiol, 11α- +11β-hydroxyprogesterone) showed good correlation with component polarity, excluding different aberrant organic modifier (isopropanol or methanol) MPs for several of these pairs. ACN-containing MPs demonstrated different separation characteristics, showing no correlation with non-ACN phases. The dissertation also reports the separation versus component polarity of non-enantiomer compounds (theophylline, caffeine), revealing that changing the polarity of the non-ACN-containing MPs did not result in a significant change in separation. Plots for the ACN-containing MPs wer (open full item for complete abstract)

Doctor of Philosophy in Clinical-Bioanalytical Chemistry, Cleveland State University, 2024, College of Arts and Sciences

Metabolomics is the study of small molecules (also termed as metabolites) in biological or botanical specimens using highly sophisticated analytical instruments, such as ultrahigh-performance liquid chromatography coupled with mass spectrometry (UHPLC-MS). The concept of computational chemistry is using software and algorithm-based analysis to pinpoint biomarkers by uncovering the molecular mechanisms that are helpful for treatment strategies. Integrating these two techniques (metabolomics and computational chemistry) enables a deeper understanding of the molecular mechanisms underlying diseases and therapeutic interventions, facilitating the identification of novel drug targets, biomarkers, and personalized treatment strategies. This integrative approach enhances our ability to unravel the complexity of biological networks and improve drug development and precision medicine. In the first half of the work, we developed a UHPLC-QTOF/MS-based untargeted metabolomics method to establish a biological model for allergic rhinitis by identifying, analyzing the regulation, and semiquantitative the key metabolites and exploring the impacted biological pathways. A computational chemistry approach was developed and applied to identify the potential therapeutic bioactive components of Astragalus radix, a traditional Chinese medicine, for allergic rhinitis treatment. In this, the protein targets were mined, and networks were constructed and analyzed to identify vital protein targets. Then, molecular docking simulations were conducted to identify the potential therapeutic components. Following this, the previously established metabolomics approach was used to investigate the therapeutic effects of the bioactive components on allergic rhinitis-induced human mast cells. In the second half of this work, we developed and validated a targeted metabolomic UHPLC-MS/MS analytical method for star anise samples. Building on our previous laboratory work, we quantified the three therapeuti (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Chemistry

The development of commercially available single particle inductively coupled plasma time of flight mass spectrometers (spICP-TOFMS) has made it possible to measure the elemental compositions, mass equivalent diameters, and number concentrations (number of particles detected per volume of analyzed sample) of thousands of individual multi-element nanoparticles and microparticles using a small volume (<10 mL) of sample suspension in minutes. Effective particle analysis by spICP-TOFMS requires a deep understanding of the technical challenges and limitations of the technique. Solution-NP and NP# transport efficiency methods can vary up to a factor of two, resulting in a 20% difference in particle mass equivalent diameters and a factor of two difference in particle number concentrations. Particle transport efficiency depends on the uptake rate. Nanoparticles transport efficiency is ~30% and ~20% at 20 and 60 µL/minute, respectively. Particle transport efficiency decreases for particles larger than ~500 nm. Element- and sample-dependent quasi-continuous backgrounds limit the smallest particle mass equivalent diameter that can be determined in samples. Diluting a sample can reduce element thresholds by a factor of ~2. The smallest detectable amount of each element is sample and isotope dependent. The linear dynamic required to measure nanoparticles and fine microparticles is over seven orders of magnitude. Particles as large as 3170 nm are vaporized, atomized, and ionized in the ICP but produce signals outside the linear dynamic range the instrument using optimized sensitivity. Four ice core samples from the Alto Dell'Ortles glacier, Italy, from pre-Roman (780 BCE) to modern (1955 CE) times with a focus on lead (Pb)-bearing particles were measured by spICP-TOFMS. The number concentration and mass equivalent diameter distributions of all detected insoluble mineral particles were similar in the four samples. However, the number concentration and the mass fraction of Pb i (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Chemistry

Detecting, quantifying, and identifying flowing samples is imperative to many applications including pharmaceutical development and manufacturing, diagnostics, forensics, and environmental studies. Surface enhanced Raman spectroscopy (SERS) is a nondestructive, water insensitive, and highly sensitive method that can be applied to these areas. Raman spectroscopy provides information regarding molecular bonds based on the inelastic scattering of light. SERS increases the efficacy of Raman spectroscopy by utilizing plasmonic metal nanostructures to enhance signals by up to 108. Utilizing SERS detection for flowing solutions allows for minimal sample preparation, short analysis time, and sample recovery after analysis. This dissertation focuses on applying SERS in flow to low concentration samples to detect single molecules and analytes that are traditionally difficult to detect and differentiate. Chapter 1 introduces Raman and SERS as analytical tools and describes how these methods can be used for flow through analysis of both single molecules and sugars. Past literature of SERS in flow experiments and single molecule experiments are discussed to support the work shown in later chapters. Additional information regarding sugars, glycosylation, and chemometrics is provided. Chapter 2 focuses on a method for single molecule detection and quantification in flow. By utilizing fast acquisition SERS, a planar silver substrate, and chemometric analysis, the linear dynamic range of this technique is able to be lowered into the stochastic, or “event counting” regime instead of the traditional ensemble or “intensity based” regime. This allows for the limit of detection of Nile Blue A to be lowered by 16 times, and single molecules to be detected and counted. Chapter 3 discusses a method for detecting and differentiating monosaccharides in both flowing and static environments using a simple benchtop conjugation reaction, SERS detection, and chemometric methods for ana (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Chemistry

My dissertation research develops Raman spectroscopy-based sensors to measure aspects of human and plant health or disease states at the point of need, specifically in areas where current sensing methods are insufficient. The first main project area involves monitoring plant health, specifically soil ecology, in real time without harvesting the plant. Sensors are needed to non-invasively observe chemical changes expressed in plant leaves which result from nutrition conditions in the soil. These sensors would be especially useful to inform fertilization practices, increasing efficiency and sustainability. The second major project area focuses on developing a rapid and accurate diagnostic assay for COVID-19. The limitations of established testing methods, such as at-home antigen tests and polymerase chain reaction (PCR) assays, motivate the exploration of alternative techniques that do not sacrifice accuracy for speed. To tackle these sensing challenges, my research employs Raman spectroscopy, which uses light to probe the molecular composition of a sample. Each molecule has a unique Raman signature, and Raman signal is proportional to the concentration of molecules present in the sample, making the technique highly advantageous for identification and quantification. Raman signals can be collected quickly and non-destructively with minimal sample preparation. To detect low concentrations of analytes or poorly scattering analytes, we use surface enhanced Raman spectroscopy (SERS), a technique in which metal nanostructures amplify the Raman signals of the molecules near the nanostructures. Overall, this dissertation work focuses on optimizing portable Raman and SERS methods to non-invasively assess plant health and to detect COVID, all in a matter of seconds. Chapter 1 introduces the background and motivation for these projects, as well as the analytical techniques used to address them. Chapter 2 describes the development of handheld Raman techniques to monitor th (open full item for complete abstract)

Doctor of Philosophy in Clinical-Bioanalytical Chemistry, Cleveland State University, 2024, College of Arts and Sciences

Barth Syndrome is a rare life-threatening X-linked recessive disease caused by a mutation in the TAZ gene, which codes for the protein taffazin. A mutation in the taffazin gene causes an inborn error in phospholipid metabolism that affects multiple physiological systems. This disease is characterized by cardiomyopathy, skeletal muscle weakness, neutropenia, growth retardation, 3-methylglutaconic (3-MGC), low plasma arginine, muscle hypoplasia, and weakness/exercise intolerance. TAZ mutations lead to mitochondrial dysfunction leading to reduced energy production and increased reactive oxygen species. There is no correlation between a specific mutation and the severity of symptoms. This leads to the need for personalized medicine. Currently, there is no FDAapproved treatment for Barth syndrome. We proposed an isogenic Barth Syndrome model utilizing human-induced pluripotent stem cells (iPSCs) with or without the TAZ mutation. We also implored the use of patient-derived fibroblasts, with or without the Barth Syndrome mutation. We have investigated TAZ metabolism via stable isotope tracing, targeted colorimetric assay, and untargeted metabolomics. These investigations revealed the possibility of anaplerotic supplementation as well as the phenotypic variability of the TAZ mutation. The research presented here offers new insights into iPSC and fibroblast models that express key characteristics of Barth syndrome and resemble the human phenotype. Various therapeutic treatments were used demonstrating variability in TAZ mutation response and demonstrating that XJB-5-131 and Triheptanoin may be promising therapeutic treatments.

Master of Science (MS), Wright State University, 2024, Chemistry

Previously, mineral nucleation was described by Classical Nucleation Theory (CNT), a theory based on the thermodynamic properties of bulk materials. However, an alternative model, known as the Prenucleation Cluster Pathway, has been increasingly used to describe nucleation of minerals. This model considers the presence of thermodynamically stable ion clusters known as “prenucleation clusters” (PNCs). In the present research, the nucleation mechanism of calcium fluoride was investigated to identify evidence of PNCs by means of the ion-selective electrode (ISE) method, where a CaCl2 solution was titrated into a NaF solution while investigating the effects of saturation level and the aqueous ratio of [Ca2+] to [F-]. It was concluded that the nucleation of fluorite is better supported by the Prenucleation Cluster Pathway rather than CNT. However further analysis of the accuracy of ion-pair formation constants is necessary in order to confirm.

Doctor of Philosophy, The Ohio State University, 2024, Chemistry

Mass spectrometry-based techniques are powerful analytical tools for observing and characterizing the structure of proteins at various levels, ranging from primary to quaternary structures. The advancement of mass spectrometry instrumentation and methods has enabled researchers to not only measure an analyte's mass-to-charge (m/z) ratio but also to probe gas-phase dissociation behaviors and conformations of peptides, proteins, and protein complexes. These advancements have greatly expanded the capabilities of researchers in the field. In the past few decades, native mass spectrometry (nMS) has become a popular technique for probing the intact structures of proteins and protein complexes. Using tandem mass spectrometry, specifically surface-induced dissociation (SID), to fragment precursor ions of interest helps in understanding protein complex connectivity, stoichiometry, and gas-phase structural rearrangement. However, most of the mass spectrometers currently on the market are not designed for nMS, and the SID is also not commercially available for most of them. By collaborating with industrial partners, we adapted a commercial trapped ion mobility spectrometry time-of-flight mass spectrometer (TIMS-Q-TOF), timsTOF Pro, for nMS with a TIMS convex cartridge device, which has a low radio frequency (RF) driver to trap high m/z ions. A low RF quadrupole driver is also designed for this instrument to extend the isolation m/z range for high m/z ions. To design and add a SID device to this instrument for SID capability, it is crucial to understand the fundamentals of SID, especially the surface material of the SID surface (Chapter 2). Based on the outcomes, we designed a 2-lens version SID device with a stainless-steel surface for this prototype instrument iii (Chapter 3). This simplified 2-lens SID device streamlines the operation and allows for SID parameters to be saved with the instrument tune file. With these advantages, we coupled a high-performance liquid chrom (open full item for complete abstract)

Master of Science (M.S.), University of Dayton, 2024, Chemistry

Linear polyenes are a class of compounds containing two or more alternating carbon-carbon double and single bonds that have a range of applications from antioxidants in nature to non-linear optical switches. More recently, other related π conjugated systems (like cinnamaldehyde) with heteroatoms within the π-conjugation are being used as chemical additives in fracturing fluids (or “fracking”) in our continued exploration of crude oil. Though this class of chemicals is considered non-hazardous, they are susceptible to oxidative additions initiated with a free radical oxidant. Because of their relative insolubility in water and high solubility in non-polar solvents, the protonation of conjugated polyenes represents a potential pathway to create novel cyclic conjugated structures in one step or hazardous products in the environment. I will present a systematic approach to analyzing the numerous products from the reaction of linear polyenes with trifluoroacetic acid under Airfree conditions to compare to the relative product distributions. The data generated in this project also provides insight into the relative stability of carbocation intermediates that are potentially created. Based on the structure of the products, we will propose mechanisms for the resulting reactions based on multivariate spectroscopic data sets. Some of the spectroscopic techniques include 1D and 2D NMR, UV-vis spectroscopy, fluorescence spectroscopy, and Mass Spectroscopy. The products that were isolated thus far indicate that inter- and intramolecular cyclization are occurring after the protonation of the polyene occurs.

Master of Science (M.S.), University of Dayton, 2024, Chemical Engineering

There is a continuous thrust for cleaner and more sustainable alternatives for energy conversion with the increasing global energy demand. Among them, photovoltaics, specifically thin film solar cells are highly promising and are one of the fastest growing clean energy technologies in the United States. This research presents the synthesis and characterization of a set of novel multinary copper chalcogenide semiconductor nanocrystals (NCs), CuZn2ASxSe4-x consisting primarily of earth-abundant elements for applications in photovoltaic devices. A modified hot-injection method was used to synthesize these semiconductor NCs containing both S and Se chalcogens. The novelty of the new semiconductor NCs lies in the incorporation of multiple cations as well as two different chalcogen anions within the crystal lattice, which is an achievement from the materials synthesis aspect. The composition-controlled optical and photoluminescence properties of the CuZn2ASxSe4-x NCs were investigated via multi-modal material characterization including x-ray diffraction (XRD), ultraviolet-visible (UV-vis) spectroscopy, and photoluminescence spectroscopy (PL). The crystal structure, as determined from the XRD primarily consisted of the metastable wurtzite (P63mc) phase. The NCs exhibited direct band gap in the visible range that could be tuned both by varying the group III cation within the composition as well as the ratio of S/Se, based on the Tauc plot obtained from the UV-vis characterization. This work lays the groundwork for future investigations into the practical applications of copper chalcogenide NCs in solar energy conversion.

Doctor of Philosophy, Miami University, 2024, Chemistry and Biochemistry

Detection of environmental pollutants, both inorganic and organic, has been an ongoing area of research for nearly three-quarters of a century. In recent years there has been growing research in the discovery of complementary methods for detection of environmental pollutants that are decentralized, rapid, and can be used onsite. A promising approach is the application of biosensors. Two common sensor classes are DNAzymes and Bacteria-Based Sensors (BB sensors). DNAzyme sensors have been discovered for the detection of many different metals. However, DNAzyme sensors have historically had issues with non-specific activity due to the presence of other metal ions with similar valency as their native metal cofactor. Further, they are limited in multiplex detection of environmental pollutants due to the limited number of orthogonal fluorophores. Moreover, there are limited sources in the literature for the design of DNAzyme sensors for the detection of organic pollutants, e.g., per- and polyfluoroalkyl substances (PFAS). In contrast, BB sensors have been developed to detect a broad range of compounds from metals to organic substances. Although many BB sensors have been developed for the detection of organic pollutants, there have been limited reports in the literature of BB sensors designed for the detection of PFAS. This dissertation aims to leverage the nonspecific activity of DNAzyme sensors by using a pattern-based readout to identify metal pollutants. Towards this goal, we investigated the DNAzyme cross-reactivity on five DNAzymes (17E, GR-5m EtNa, Ag10c, and NaA43) with eight different metals, and analyzed the data through several statistical methods. Through this approach, we were able to correctly identify four validation solutions, of which, one validation solution consisted of 100 μM Zn2+, which is a known interferent for 17E. Following this, the dissertation explored a new DNAzyme reporter architecture, DzNanoporeSeq, where sequencing is used as a signal readou (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Chemistry

Systems biology has made great strides in the comprehensive characterization of the molecular complexity of living systems, such as nuclei acids and proteins. When it comes to the world of metabolites, however, many spectroscopic signatures observed in complex biological mixtures remain undefined and belong to unknown molecules, also known as the “dark metabolome.” Strikingly, there is currently no standardized approach for the identification and characterization of the structures of such molecules, many of which can serve as biomarkers for disease. This dissertation describes new strategies that combine nuclear magnetic resonance (NMR) with mass spectrometry (MS) to comprehensively identify known and unknown metabolites from complex mixtures. Chapter 1 introduces metabolomics and gives an overview of NMR and MS metabolomics methods. Chapter 2 presents COLMARppm, a tool developed for rapid and accurate NMR chemical shift prediction of small molecules. It relies on high quality NMR spectra from the COLMAR database to predict chemical shifts for molecules that are unknown to NMR. Chapter 3 focuses on the implementation of COLMARppm into a new strategy for determining chemical structures of unknown metabolites: COLMARu. The COLMARu workflow uses experimental 2D NMR-derived chemical shifts and high-resolution accurate mass (MS) information directly obtained from the complex biological mixture of interest to assemble candidate molecules that match its unknown metabolites. Candidates are ranked based on a combined MS/NMR accuracy score to prioritize the most reasonable molecules for characterization. The COLMARu strategy was applied to several complex mixtures, e.g., mouse urine and Staphylococcus aureus, resulting in the identification of several unknown metabolites.