Emissions from natural ecosystems, broadly classified as biogenic volatile organic compounds (BVOCs), contribute 90\% to the VOC budget. Individual BVOCs vary widely in their reaction rates with atmospheric oxidants, making their atmospheric impact highly dependent on VOC composition. Their emissions are also dependent on vegetative make up and a number of meteorological and ecological variables. However, the ecological and physical drivers of their emissions is becoming more variable in a changing climate, leading to greater uncertainties in models. Increasing the monitoring of individual compounds can improve our understanding of the drivers of these emissions and the impact of individual chemical species on atmospheric composition. Improved understanding of BVOC composition can better emission models and, SOA and ozone formation predictions. To study the atmospheric impacts and physical drivers of BVOCs, a GC-FID was adapted for automated hourly sampling and analysis. The details of the hardware and software used for the system are described in detail to enable future long-term BVOC measurements in additional locations. The instrument was deployed at a measurement tower in a forest in central Virginia for year-round collection of BVOC concentrations. Using two years of collected hourly data, this work assesses the chemical impacts of individual BVOCs on time scales ranging from hour to year. This work identifies the importance of both concentration and chemical structure in determining atmospheric impacts. Additionally, seasonality in the concentration of some biogenic species has large implications for atmospheric reactivity in the warmest months of the year, particularly ozone reactivity. Using ecological and meteorological data collected at the site in conjunction with the BVOC data, the drivers of BVOC concentrations and their seasonality are identified. Comparison between this data and current models, reveal important deviations which may lead to large modeled uncertainties. Furthermore, the collected data has been made publicly available to aid in future research regarding BVOCs. / Doctor of Philosophy / The earth hosts a number of sources of atmospheric emissions. These range from human-driven sources such as vehicles and factories, to natural sources such as trees and grass. The content of these emissions, amongst others, become a part of a large reactor (the atmosphere), that interact with each other. The interaction of these emissions with atmospheric oxidants forms a gas (ozone) with implications for human and ecosystem health, and secondary organic aerosol (the leading component to smog). However, the extent to which these emissions react with atmospheric oxidants is largely dependent on the structure of individual compounds. A major focus of this dissertation is to show that compounds with reactive structures can have a large impact on atmospheric composition, and that the quantity of emissions can be as important as compound structure. Understanding the impact of individual compounds in the atmosphere requires improved measurement techniques, capable of detecting the compounds of interest over long time periods. Therefore, another focus of this work was the adaptation and deployment of an instrument capable of detecting some of the most reactive species in the atmosphere, volatile organic compounds emitted from forests. The instrument deployed in this work was a gas chromatography flame ionization detector (GC-FID), which detects compounds largely composed of carbon and hydrogen. The instrument was adapted to run automatically through the development of an electronics box and software program interfaced with the GC-FID. Following development, the instrument was deployed to a remote forest research site for two years. The data collected from this work was used to determine the impact of individual compounds on atmospheric composition. Findings from this work could be used to improve a range of atmospheric models. Small changes in emissions (human or plant) contribute to the total VOC budget which can have large implications for the formation of ozone and SOA. Therefore, increased understanding of the BVOC concentrations and emission driver will aid in predicting these atmospheric components.

A method has been developed for the quantitative extraction of nitrotoluenes (2,6-dinitrotoluene, 2,4-dinitrotoluene, and trinitrotoluene) from water. Three types of solid sorbents were investigated: two 47 mm Empore disks™ - octadecylsilane (C18) and styrene-divinylbenzene (SDVB); and one Bakerbond spe*™ Phenyl stationary phase. The phenyl sorbent yielded the highest recoveries. The average SPE recoveries for spike standards ranged from 80 to 95 percent for Millipore water and 55 to 95 percent from well and surface water in the low ppb and ppt levels. After the nitrotoluenes were trapped on the solid sorbents they were quantitatively eluted by first doping the bed with toluene and then extracting with supercritical carbon dioxide. Doping with toluene was found to increase the rate of extraction. The extracts were analyzed off-line via GC-ECD using an internal standard. Extraction losses are due to analyte break through, and not from poor SFE recoveries. This demonstrates that supercritical fluid extraction is a suitable elution technique for analytes trapped on solid phase extraction (SPE) cartridges. A method has also been developed and evaluated for the direct on-line coupling of SPE to GC. SPE-SFE-GC-ECD analysis eliminates off-line collection and subsequent handling of hazardous materials. SFE is an ideal means of directly coupling SPE to GC, since carbon dioxide is a gas at ambient temperatures and pressures and thus easily removed. One potential problem for SPE-SFE on-line GC is the presence of residual water trapped on the active sites of the bonded silica sorbent. The presence of water can interfere with the cryogenic trapping of the analytes on the capillary GC column. The water becomes ice at cryogenic temperatures and in large quantities blocks the GC column. This problem has been avoided by using a split injection interface previously described by Hawthorne. The quantitative reproducibility of this interface will be investigated for nanogram quantities of nitroaromatics. / Ph. D.

Identification of disease-related alterations in molecular and cellular mechanisms may reveal useful biomarkers for human diseases including cancers. High-throughput omic technologies for identifying and quantifying multi-level biological molecules (e.g., proteins, glycans, and metabolites) have facilitated the advances in biological research in recent years. Liquid (or gas) chromatography coupled with mass spectrometry (LC/GC-MS) has become an essential tool in such large-scale omic studies. Appropriate LC/GC-MS data preprocessing pipelines are needed to detect true differences between biological groups. Challenges exist in several aspects of MS data analysis. Specifically for biomarker discovery, one fundamental challenge in quantitation of biomolecules is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based omic studies. Purification of mass spectometric data is highly desired prior to subsequent differential analysis. In this research dissertation, we majorly target at addressing the purification problem through probabilistic modeling. We propose an intensity-level purification model (IPM) to computationally purify LC/GC-MS based cancerous data in biomarker discovery studies. We further extend IPM to scan-level purification model (SPM) by considering information from extracted ion chromatogram (EIC, scan-level feature). Both IPM and SPM belong to the category of topic modeling approach, which aims to identify the underlying "topics" (sources) and their mixture proportions in composing the heterogeneous data. Additionally, denoise deconvolution model (DMM) is proposed to capture the noise signals in samples based on purified profiles. Variational expectation-maximization (VEM) and Markov chain Monte Carlo (MCMC) methods are used to draw inference on the latent variables and estimate the model parameters. Before we come to purification, other research topics in related to mass spectrometric data analysis for cancer biomarker discovery are also investigated in this dissertation. Chapter 3 discusses the developed methods in the differential analysis of LC/GC-MS based omic data, specifically for the preprocessing in data of LC-MS profiled glycans. Chapter 4 presents the assumptions and inference details of IPM, SPM, and DDM. A latent Dirichlet allocation (LDA) core is used to model the heterogeneous cancerous data as mixtures of topics consisting of sample-specific pure cancerous source and non-cancerous contaminants. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum and tissue proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis. Chapter 5 elaborates these applications in cancer biomarker discovery, where typical single omic and integrative analysis of multi-omic studies are included. / Ph. D. / This dissertation documents the methodology and outputs for computational deconvolution of heterogeneous omics data generated from biospecimens of interest. These omics data convey qualitative and quantitative information of biomolecules (e.g., glycans, proteins, metabolites, etc.) which are profiled by instruments named liquid (or gas) chromatography and mass spectrometer (LC/GC-MS). In the scenarios of biomarker discovery, we aim to find out the significant difference on intensities of biomolecules with respect to two specific phenotype groups so that the biomarkers can be used as clinical indicators for early stage diagnose. However, the purity of collected samples constitutes the fundamental challenge to the process of differential analysis. Instead of experimental methods that are costly and time-consuming, we treat the purification task as one of the topic modeling procedures, where we assume each observed biomolecular profile is a mixture of hidden pure source together with unwanted contaminants. The developed models output the estimated mixture proportion as well as the underlying “topics”. With different level’s purification applied, improved discrimination power of candidate biomarkers and more biologically meaningful pathways were discovered in LC/GC-MS based multi-omic studies for liver cancer. This research work originates from a broader scope of probabilistic generative modeling, where rational assumptions are made to characterize the generation process of the observations. Therefore, the developed models in this dissertation have great potential in applications other than heterogeneous data purification discussed in this dissertation. A good example is to uncover the relationship of human gut microbiome with the host’s phenotypes of interest (e.g., disease like type-II diabetes). Similar challenges exist in how to infer the underlying intestinal flora distribution and estimate their mixture proportions. This dissertation also covers topics of related data preprocessing and integration, but with a consistent goal in improving the performance of biomarker discovery. In summary, the research help address sample heterogeneity issue observed in LC/GC-MS based cancer biomarker discovery studies and shed light on computational deconvolution of the mixtures, which can be generalized to other domains of interest.

Measuring lipid oxidation is useful as a means of monitoring oxidative stress, such as that induced by clinical conditions or environmental exposure. Characteristic volatile compounds, often with low threshold odors, are secondary products of lipid oxidation reactions. Metallic flavor in food and beverages has been linked with oxidation of lipids in the oral cavity. Breath, an emerging medium for analysis of internal condition, is one means of measuring the metal-induced lipid oxidation responsible for this flavor. This project analyzes the breath of human subjects, as well as lipid oxidation of in vitro samples to identify compounds responsible for producing metallic flavor, which result from the oxidation of lipids in the oral cavity. Because these analytes are found at extremely low (picomolar to nanomolar) concentrations, preconcentration of samples prior to gas chromatography-mass spectrometry analysis is crucial. This study utilizes both solid phase microextraction (SPME) and micromachined silicon micropreconcentrators to concentrate compounds in breath to optimize analysis. / Master of Science

Instrument miniaturization is led by the desire to perform rapid diagnosis in remote areas with high throughput and low cost. In addition, miniaturized instruments hold the promise of consuming small sample volumes and are thus less prone to cross-contamination. Gas chromatography (GC) is the leading analytical instrument for the analysis of volatile organic compounds (VOCs). Due to its wide-ranging applications, it has received great attention both from industrial sectors and scientific communities. Recently, numerous research efforts have benefited from the advancements in micro-electromechanical system (MEMS) and nanotechnology based solutions to miniaturize the key components of GC instrument (pre-concentrator/injector, separation column, valves, pumps, and the detector). The purpose of this dissertation is to address the critical need of developing a micro GC system for various field- applications. The uniqueness of this work is to emphasize on the importance of integrating the basic components of μGC (including sampling/injection, separation and detection) on a single platform. This integration leads to overall improved performance as well as reducing the manufacturing cost of this technology. In this regard, the implementation of micro helium discharge photoionization detector (μDPID) in silicon-glass architecture served as a major accomplishment enabling its monolithic integration with the micro separation column (μSC). For the first time, the operation of a monolithic integrated module under temperature and flow programming conditions has been demonstrated to achieve rapid chromatographic analysis of a complex sample. Furthermore, an innovative sample injection mechanism has been incorporated in the integrated module to present the idea of a chip-scale μGC system. The possibility of using μGC technology in practical applications such as breath analysis and water monitoring is also demonstrated. Moreover, a nanotechnology based scheme for enhancing the adsorption capacity of the microfabricated pre-concentrator is also described. / Ph. D.

The purpose of this study was to evaluate the effects of several factors which might alter the analysis of chloroform in aqueous samples by the liquid-liquid extraction (LLE) method developed by Glaze at North Texas State University. A Bendix gas chromatograph (GC) was used. The factors examined were the effect of the ionic strength of the sample on the extraction method, the relative extraction efficiency when three pentane-to-sample ratios were used, the change in the response of the GC to a set of samples during a several hours lengthy analysis, the precision of the data obtained by the analysis procedure, and the precision of analyses of aqueous chloroform standards made from one set of secondary standards. These factors were examined to aid in the development in this laboratory of an analysis program to study the role of algal extracellular metabolites as trihaloomethanes precursors. Ionic strength up to 0.0116 (strengths of algal media) did not affect the results. Few differences in the extent of linearity or extraction efficiency were detected when pentane-to-sample ratios of 1:1, 2:1, or 1:2 were used. The extraction efficiencies of the three ratios deviated 11 percent of their mean, with four values not included. The GC's response to a set of samples analyzed at the beginning and end of a nine-to-twelve hour experiment may change significantly. Standards prepared in pentane did not change during the test period. Four replicate analyses of the same sample were found to be highly precise. / Master of Science

This thesis in microscale gas chromatography (μGC) creates a paradigm shift in rapidly analyzing chemicals in the environment or analytes. We are looking for unexpected chemical changes that have been added purposefully or unintentionally. The work examines various aspects of μGC technology, including the optimization of ionic liquid stationary phase coatings for microfabricated columns, achieving up to 8300 theoretical plates per meter for naphthalene using 1-butylpyridinum bis(trifluoromethylsulfonyl)imide [BPY][NTf2] at 240°C. The development of portable systems for fuel adulteration detection is demonstrated, capable of discriminating 5% kerosene adulterated diesel fuel with four seconds of chromatogram analysis. The research also presents a novel parallel column configuration using three ionic liquid-coated semi-packed columns, each 1 m long and 240 μm deep, for complex gas analysis of up to 46 compounds. Key innovations discussed include optimized coating procedure of GC separation columns and implementation of GC based miniaturized electronic nose with the integration of machine learning algorithms. An evaluation of a prototype modular electric and fluidic μGC was evaluated and validated for benzene toluene, ethylbenzene, and xylene (BTEX). This research highlights the versatility of μGCs in applications ranging from environmental monitoring to quality control in the fuel industry, showcasing their potential as powerful tools for on-site chemical analysis with improved selectivity, resolution, and portability. / Doctor of Philosophy / This thesis advances the development of miniature chemical analytical systems, specifically gas chromatography, which is the gold standard for detecting volatile organic compounds in the environment. The work encompasses comprehensive improvements to these systems, from optimizing fabrication and coating of separation columns for better chemical separation to developing rapid prototyping methods for both hardware and software components. Through the integration of machine learning and innovative system designs, the thesis demonstrates significant improvements in detection capabilities, including identifying fuel tampering within seconds and monitoring harmful air pollutants at parts-per-billion levels over extended periods. These advances pave the way for making sophisticated chemical analysis accessible outside of traditional laboratories, enabling direct testing at locations where immediate results are crucial for safety and quality control.

No / This study examines resinous deposits from the interior surfaces of sherds of imported Canaanite amphorae and locally produced bowls from the 18th Dynasty site of Tell el-Amarna, Egypt. Archaeological evidence indicates that the Canaanite amphorae were used for resin transport, whilst the bowls are associated with burning resin as incense. A number of characteristic triterpenoids identify all the resinous deposits from both vessel types as Pistacia spp. No other resins were observed and there was no evidence of mixing with oils or fats. The composition of the archaeological resins is more complex than that of modern pistacia resin, due to degradation and generation of new components. Experimental heating alters the relative abundance of the triterpenoid composition of modern pistacia resin. One component, the triterpenoid 28-norolean-17-en-3-one, is produced by such heating; however, an increase in its relative abundance in ancient samples is not matched by the archaeological evidence for heating. It is therefore not possible to use this component reliably to identify heated resin. However, additional unidentified components with a mass spectral base peak at m/z 453 have been associated with seven (out of 10) bowls and are not observed in resins associated with Canaanite amphorae. It is proposed that these components are more reliable molecular indicators of heating.

No / In this paper, molecular modelling was used to investigate the nature of probe/surface interactions during the analysis of Dß-mannitol using inverse gas chromatography (IGC). IGC was used to experimentally measure the dispersive components of surface free energy () and the specific components of free energy of adsorption () of Dß-mannitol by calculating the retention time of non-polar (n-alkanes) and polar (tetrahydrofuran and chloroform) probes, respectively. The results showed that Dß-mannitol surface is acidic in nature because the basic probe had more interaction with the surface as compared to acidic probe. Cerius2 software package was used to model the two morphologically important surfaces, which showed the presence of surface hydroxyl groups. Molecular dynamics simulations were performed in Cerius2 to model the adsorption of the same probes (n-alkanes, tetrahydrofuran and chloroform) on the Dß-mannitol surfaces. The adsorption energies calculated from the simulation showed a close match to those determined experimentally. The calculated values are slightly higher for all probes except chloroform, but as a single perfect crystal was modelled without considering the effect of impurities, solvent and other physical factors this is not unexpected.

No / Five fragments of Late Neolithic clay zoomorphic vessels from northern Greece have been analysed for organic residues by gas chromatography - mass spectrometry. The results showed that the containers had been used in connection with a number of substances, in particular lower terpenoids, an oil or fat, possibly fossil fuel and in one case possibly beeswax. The paper considers likely interpretations of such combinations of materials in relation to possible functions of these symbolically enhanced artefacts. It appears that substances may have been used in the vessels because of their aromatic and/or medicinal and combustible properties, possibly in order to produce light, fragrance and/or smoke.