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Chemical warfare agents are a toxic class of compounds that are incredibly harmful to human health. Methods of detoxification and decontamination currently exist, however they all suffer from problems that involve logistical transport or involve technologies that directly address liquid threats instead of vapors. One promising method of detoxification involves the oxidation of these compounds into less-harmful species. The relatively large chemical size and complexity of modern-day chemical warfare agents, however, precludes a straightforward analysis of the chemical transformations that take place on novel decontaminating materials. Additionally, a fundamental understanding of reaction mechanisms that occur on novel material surfaces is required before improved materials can be developed. To this end, the oxidation of three simpler, smaller organic molecules were studied over a variety of materials in order to build up a chemical understanding of the systems under study. The photoepoxidation of propene into propene oxide was observed to readily occur over an in-house developed dual titania-silica catalyst created by atomic layer deposition. The subsequent photoinduced degradation of produced propene oxide was observed to occur over the novel catalyst. Next, the oxidation of CO was studied over a Pt/TiO2 catalyst while in the presence of humidity. The addition of water was shown to enable an alternative, low energy pathway that closely followed the water gas shift, but ended upon the production of stable surface-bound formates. Gaseous oxygen was found to subsequently oxidize these surface formates into the full oxidation product, CO2. Next, the oxidation of methanol was studied over the same Pt/TiO2 catalyst. It was discovered that the water produced when methanol initially adsorbs to the catalyst surface is responsible for unlocking the oxidative capacity of the material. Finally, a custom packedbed reactor was designed and built that enabled unique experimental capabilities not yet available in commercial systems, and will be used in the future to directly test the oxidative capabilities of novel materials for chemical warfare agent destruction. / Doctor of Philosophy / The chemical interactions and reactions that occur between gases and surfaces are incredibly important for a multitude of technologies employed by governments, militaries, and citizens alike. The precise methods in which these gases interact with materials of interest determine whether said material can be used in a catalytic fashion. Much like how an automobile catalytic converter does not have to be replaced each time the vehicle is started; a catalyst is able to be used repeatedly without loss of function. Catalysts in general are unique in that they function to create or allow for chemical reactions to proceed through alternative, lower energy pathways that are more likely to occur under milder environmental conditions. In order to understand the chemical reactions that occur on a catalyst, a combination of specialized spectroscopic methods was used that allowed for tracking the precise chemical bonds that were formed or broken during reaction. A few different model chemical reactions are explored in this work, ranging from the conversion of carbon monoxide into CO2, and the oxidation of methanol, a small alcohol commonly found in fuel cells. The experimental techniques employed herein allowed for precise chemical mechanisms to be tracked, and the information gained will certainly be useful for the design of next-generation materials by future research.

This degree project studies implementation and comparison of different AI models to predict (1) solid biofuel elements including carbon, hydrogen, nitrogen, and oxygen as well as moisture content, ash content, and higher heating value (HHV) of the fuel and (2) flue gas compositions such as concentration of carbon dioxide, carbon monoxide, nitrogen, nitrogen oxides, and water content using near-infrared spectroscopy and chemometric approaches. The study executes these predictions by simulating the operation of a combined heat and power plant (CHP) that is equipped with carbon capture and storage (CCS). The focus of this study is to investigate the possibility of using Near-Infrared spectroscopy (NIRs) technology to predict the emissions from a CHP plant, which can further improve the performance of the CCS system by providing the necessary fuel data in real time. The acquired NIR data is used to develop the Artificial Intelligence (AI) models using several regression algorithms including Linear regression, Gaussian process, Support Vector Machine, Artificial Neural Network, Ensemble Trees, and Tree regression. NIR data has been pre-processed using Savitzky-Golay (SG) and Multi scatter correction (MSC) techniques. Highest accuracy has been achieved for moisture content of the fuel using Exponential Gaussian Process, where an RMSE of 2.5687 and an R2-value of 0.9 has been obtained. Indeed, only a handful of regression algorithms have shown reasonable accuracy when predicting the fuel elements, where the HHV of the fuel has been predicted poorly as none of the algorithms have been able to execute the prediction successfully which leads to negative values of R2. Prediction of flue gas composition has been done resulting in reasonable accuracies for CO2 fraction with values of 0.1051 and 0.6 for RMSE and R2 respectively. Furthermore, the computational time of the algorithms has been investigated, which indicates that some of the pre-processing techniques could improve the computational time for a certain regression model, but not for all of them. It is conclusively possible to predict fuel elements and flue gas compositions based on data acquired from NIR spectroscopy. However, great effort must be put into model development including data treatment and AI model calibration to achieve desirable precision and reliable results.

Extracellular matrix (ECM) is a key component and regulator of many biological tissues. Several cardiovascular pathologies are associated with significant changes in the composition of the matrix. Better understanding of these pathologies and the physiological phenomenon behind their development depends on reliable methods that can measure and characterize ECM content and structure. In this dissertation, infrared spectroscopic methodologies are developed to study the changes in extracellular matrix of cardiovascular tissue in two cardiovascular pathologies; myocardial infarction and abdominal aortic aneurysm. The specific aims of this dissertation were: 1. To develop a Fourier transform infrared imaging spectroscopy (FT-IRIS) methodology for creating distribution maps of collagen in remodeled cardiac tissue sections after myocardial infarction, and to quantitatively compare maps created by FT-IRIS with conventional staining techniques. 2. To develop an FT-IRIS method to assess elastin and collagen composition in the aortic wall. This will be accomplished using ex vivo animal aorta samples, where the primary ECM components of the wall will be systematically enzymatically degraded. 3. To apply the newly developed FTIR imaging methodology to evaluate changes in the primary ECM components (collagen and elastin) in the wall of human AAA tissues. The infrared absorbance band centered at 1338 cm-1, was used to map collagen deposition across heart tissue sections of a rat model of myocardial infarction, and was correlated strongly in the size of the scar (R=0.93) and local intensity of collagen deposition (R=0.86). In enzymatically degraded pig aorta samples, as a model of ECM degradation in abdominal aortic aneurysm (AAA), partial least squares (PLS) models were created to predict collagen and elastin content in aorta based on collected FTIR spectra and biochemically measured values. PLS models based on FT-IRIS spectra were able to predict elastin and collagen content of the samples with strong correlations (R2=0.90 and 0.70 respectively). Elastin content prediction from IFOP spectra was successful through a PLS regression model with high correlation (R2=0.81). The PLS regression coefficient from the FT-IRIS models were used to map collagen and elastin human AAA biopsy tissue sections, creating a similar map of each component compared to histologically stained images. The mean value of collagen deposition in each tissue was calculated for 13 pairs of AAA samples where stress had been calculated using finite element modeling. In most pairs with stress values higher than 5 N/m2, collagen content was lower in the sample with higher stress value. Collagen maturity had a weak negative correlation (R=-0.35) with collagen content in these samples. These results confirm that infrared spectroscopy is a powerful tool that can be applied to replace or complement conventional methods such as histology and biochemical analysis to characterize ECM components in cardiovascular tissues. Furthermore, infrared spectroscopy has the potential for translation to a clinical environment to examine ECM changes in aorta in a minimally invasive fashion using fiber optic technology. / Mechanical Engineering

There is considerable evidence to suggest that object affordances (see Gibson, 1966) can serve to moderate volitional responses by “priming” the visuomotor system toward certain actions (e.g., Tucker & Ellis, 1998). Typically, these studies assume that shorter voluntary reaction time latencies reflect more efficient movement planning. Questions remain however, as to whether object affordances offer the same motor priming benefits in situations where the temporal window to initiate motor action precludes volitional movements (e.g., during an unexpected balance perturbation). The efficiency of balance reactions to a perturbation is dependent upon the ability for the motor system to generate short latency actions at the onset of instability. Due to the rapid nature of these actions, they are suggested to be regulated by information received prior to the perturbation. In this study, participants sat in a custom-built chair that delivered posterior perturbations and, on each trial, were presented with two of three types of stimuli within their reach (two graspable poles that varied in orientation and a flat non-graspable control). They were instructed to reach and grasp one of the poles at the moment of perturbation so as to mitigate the tilt. To assess cortical activity that may be indicative of motor planning in response to the perception of object affordances, changes in oxyhemoglobin (oxy-Hb) in the right and left premotor cortices were measured using a continuous wave fNIRS system. Results revealed a significant increase (F= 4.62, p= .043) in oxy-Hb in the right and left hemisphere (M = .023 µM) in response to objects that afford an optimal form of grasping action (mitigating excessive supination or pronation of the hand), compared to when no grasping opportunity was present (M = -.051 µM). These results suggest that affordances may be used to prime the system in the event of a balance threat. / Thesis / Master of Science (MSc)

Biomedical optics is a rapidly expanding field of research focusing on the development of methods to detect, diagnose, and treat disease using light. While there are a myriad of optical systems that have been developed for biological tissue imaging, optical diagnostics, and optical therapeutics, all of these methods suffer severely limited penetration depths due to attenuation of light by tissue constituent chromophores, including cells, water, blood, and protein structures. Tissue optical clearing is a recent area of study within biomedical optics and photonics, where chemical agents have been used to alter tissue optical properties, reducing optical absorption and scattering and enabling light delivery to and collection from deeper tissue regions. However, there are concerns as to the safety and efficacy of these chemical clearing agents in vivo, especially in the skin, where the projective barrier function of the stratum corneum must be removed. Mechanical optical clearing is a recently developed technology which utilizes mechanical loading to reversibly modify light transport through soft tissues, and much of the work published on this technique has focused on applications in skin tissue. This clearing technique enables deeper light delivery into soft tissues but does not require use of exogenous chemicals, nor does it compromise the skin barrier function. While this clearing effect is thought to be resultant from interstitial water and blood transport, the underlying mechanism has not been concretely identified nor characterized. The hypothesis of this body of work was that interstitial transport of tissue chromophores (e.g. water and blood) causes intrinsic optical property changes, reduces tissue optical absorption and scattering, and improves light delivery in diagnostic applications. To test this hypothesis, we first developed a mathematical framework to simulate mechanical optical clearing, using both mechanical finite element models and optical Monte Carlo simulations. By directly simulating interstitial water transport in response to loading, data from mechanical simulations was combined with optical Monte Carlo simulations, which enabled prediction of light transmission measurements made during mechanical indentation experiments. We also investigated changes in optical properties during mechanical indentation using diffuse reflectance spectroscopy. These studies used controlled flat indentation by a fiberoptic probe to dynamically measure intrinsic optical properties as they changed over time. Finally, we apply mechanical optical clearing principles to functional near-infrared spectroscopy for neuroimaging. By building a prototypical mechanical optical clearing device for measuring cerebral hemodynamics, we demonstrated that mechanical optical clearing devices modify measured cerebral hemodynamic signals in human subjects, improving signal quality. / Ph. D.

Purpose: Infrared (IR) spectroscopy has the potential for tumor delineation in neurosurgery. Previous research showed that IR spectra of brain tumors are generally characterized by reduced lipid-related and increased protein-related bands. Therefore, we propose the exploitation of these common spectral changes for brain tumor recognition. Methods: Attenuated total reflection IR spectroscopy was performed on fresh specimens of 790 patients within minutes after resection. Using principal component analysis and linear discriminant analysis, a classification model was developed on a subset of glioblastoma (n = 135) and non-neoplastic brain (n = 27) specimens, and then applied to classify the IR spectra of several types of brain tumors. Results The model correctly classified 82% (517/628) of specimens as “tumor” or “non-tumor”, respectively. While the sensitivity was limited for infiltrative glioma, this approach recognized GBM (86%), other types of primary brain tumors (92%) and brain metastases (92%) with high accuracy and all non-tumor samples were correctly identified. Conclusion: The concept of differentiation of brain tumors from non-tumor brain based on a common spectroscopic tumor signature will accelerate clinical translation of infrared spectroscopy and related technologies. The surgeon could use a single instrument to detect a variety of brain tumor types intraoperatively in future clinical settings. Our data suggests that this would be associated with some risk of missing infiltrative regions or tumors, but not with the risk of removing non-tumor brain.

In the electronics industry, several greenhouse gases (GHGs) are used as process gases in manufacturing processes. The organization RF360 as a Qualcomm Inc. subsidiary is using GHGs as input gases in the manufacturing processes dry etching, Chemical Vapor Deposition (CVD) and trimming in the fabrication plant in Munich.The estimation of GHG emissions from the use of process gases under Scope 1 requires a global and comprehensive approach to determine emission sources. This work provides the basis for the GHG emission estimation from process gas use under consideration of the 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Dry etching and CVD process GHG emissions arecalculated using the Tier 2c method with process specific default emission factors. The process GHG emissions from trimming are characterized under Tier 3a, by determination of site-specific process emission factors. These emission factors are obtained from FTIR measurements in the inline. The measurement results show the input gas NF3 is largely not converted or destroyed in the trimming process. The total GHG emissions resulting from process gas use in the considered processes are determined by emissions of NF3, CF4 and N2O. The implementation and improvement of the approach requires further measurements of site-specific emission factors in the processes and Destruction Removal Efficiencies of the abatement systems.