The current capabilities of SASKTRAN – a radiative transfer model at the University of Saskatchewan in Saskatoon, Canada – are to accurately model the scattering of solar radiation within the earth’s atmosphere for the ultraviolet-visible (UV-Vis) and near infra-red (NIR) regions of the electromagnetic spectrum. However, the current model does not account for the radiation emitted by the ground and atmosphere, approximated by the blackbody spectrum. In the UV-Vis, this contribution is unimportant, but when transitioning to wavelengths longer than 2.5 μm, the solar spectrum decreases in intensity while radiation of terrestrial and atmospheric origin increases along the blackbody curve. At wavelengths longer than 5 μm in the far infra-red (FIR), the blackbody radiation is the dominant source in the atmosphere. A modification to the source code of SASKTRAN was made in order to include the additional effect of this “thermal” radiation – with the help of the spectral line database HITRAN – while still maintaining scattering capabilities of solar radiation. This would make SASKTRAN one of the first radiative transfer models with the ability to model radiation in the difficult region between 3 and 5 μm – the mid infra-red (MIR) region – where the solar and thermal radiation sources are equally diminished and are the same order of magnitude. An introduction is given to atmospheric physics with a focus on the science of infra-red active molecules like H2O, CO2, CH4, N2O, O3, and CO – the so-called “greenhouse gases” – and the measurement techniques used to determine their atmospheric distribution. A theoretical basis is then provided for general radiative transfer, and the physics of molecular absorption and emission is examined in detail. A summary of the implementation of thermal radiation within the SASKTRAN framework is given, followed by verification studies where the model’s radiative transfer calculations in the infra-red are compared against measurements, including those made by the ground-based instrument E-AERI, the space-borne instruments IASI and GOSAT, and against model results from the LBLRTM, another well-verified radiative transfer model.

This investigation evaluates the influences of optical property assumptions on nearinfrared diffuse correlation spectroscopy (DCS) flow index measurements. Independent variation is induced in optical properties, absorption coefficient (μa) and reduced scattering coefficient (μs’), of liquid phantoms with concurrent measurements of flow indices. A hybrid instrument is incorporated consisting of a dual-wavelength (785 and 830 nm) DCS flow device to obtain flow indices and a frequency-domain tissue-oximeter for optical properties. Flow indices are calculated with measured μa and μs’ or assumed constant μa and μs’. Inaccurate μs’ assumptions produced much larger flow index errors than inaccurate μa. Underestimated/overestimated μs’ from -35%/+175% lead to flow index errors of +110%/-80% and underestimated/overestimated μa from -40%/+150% lead to -20%/+40%, regardless of wavelength. Analysis of a clinical study involving human head and neck tumors indicates flow index errors due to inter-patient optical property variations up to +280%. Collectively, these findings suggest that studies involving significant μa and μs’ changes should measure flow index and optical properties simultaneously to accurately extract blood flow information. This study provides unique insight through the use of liquid phantoms, hybrid instrumentation, incorporation of measurement errors and a generalization into DCS flow index errors due to the influences of optical properties.

Electrospray ionization mass spectrometry (ESI-MS) has been applied to the realtime study of homogeneous organometallic reactions. ESI-MS as a soft ionization technique is amenable to fragile organometallic complexes, and as a fast and sensitive technique is ideal for detecting low concentration intermediates within reactions. Pressurized sample infusion (PSI) was used for continuous sample infusion into the mass spectrometer, granting the air-free conditions necessary for these reactions to be successful, and resulting in reaction profile data that contains information about the dynamics of speciation of the catalyst. Collision induced dissociation (CID) was used to probe the binding affinities of various bisphosphine ligands as well as in characterizing intermediates in reactions. PSI ESI-MS was applied to the hydroboration reaction of the alkene tert-butylethene using the amine-borane H3B⋅NMe3 catalyzed by [Rh(xantphos)]+ fragments to show how the reaction progresses from substrates to products. PSI ESI-MS was also applied to the hydrogenation of a charge-tagged alkyne [Ph3P(CH2)4C2H]+[PF6]-, catalyzed by a cationic rhodium complex [Rh(PcPr3)2(η6-FPh)]+[B{3,5-(CF3)2C6H3}4]– (PcPr3 = triscyclopropylphosphine, FPh = fluorobenzene). This work demonstrated the use of ESI-MS in conjunction with NMR, kinetic isotope effects and numerical modeling for determining a mechanism of reaction. The hydroacylation reaction of a β–S substituted aldehyde and an alkyne catalyzed by [Rh(PiPr2NMePiPr2)(η6-FPh)]+[B{3,5-(CF3)2C6H3}4]– (PiPr2 = diisopropylphosphine) was studied by PSI ESI-MS while employing charged tags, allowing for observation of reaction progress and some key intermediates. A new concept for mechanistic analysis has been developed: coupling of an orthogonal spectroscopic technique with PSI ESI-MS. This new method was applied to the same hydroacylation reaction studied with charged tags. The use of IR in conjunction with ESI-MS led to rate information about the overall reaction along with dynamic information about catalytic speciation. Coupling of these techniques allows for detection over many magnitudes of concentration. / Graduate

Layered metal sulfides MoS2 and WS2 exhibit highly anisotropic surface chemistry. Adsorption of molecules is stronger on the atomic layer edges than on atomic planes. The edges are catalytically active in the petroleum hydrodesulfurization, while the layer planes are inert. Dispersing MoS2 and WS2 on the nanometer scale can also lead to the onset of photocatalytic properties due to the bandgap tuning by quantum confinement. In this work, we aim at determining how the adsorption on surface sites is altered for the nanoparticles compared to the bulk sulfides (micron-sized particles). A comparative study of the MoS2 and WS2 nanoparticles and bulk materials is done by attempting the adsorption of small molecules (N2, CO, acetone, and acetonitrile) to probe the surface sites. MoS2 and WS2 nanoparticles were synthesized by thermal decomposition of the metal hexacarbonyls in presence of sulfur in high-boiling solvents. The size range is 5-30 nm from Transmission Electron Microscopy. Transmission Infrared Spectroscopy was used to monitor the spectra of the probe molecules. A dedicated experimental setup has been constructed that consists of a high-vacuum chamber with a base pressure of 5×10-7 Torr. At the lowest achievable temperature of the sample (-145°C), N2, CO, and acetone were found to not adsorb strongly enough to be retained in vacuum on these materials. Acetonitrile was found to adsorb on these materials at -145°C and to desorb between -90°C and -50°C. The nanomaterial samples adsorbed significantly more acetonitrile than the corresponding bulk sulfides, as judged by the infrared signals intensity. Qualitatively, adsorbed acetonitrile species on nanodispersed and bulk sulfides are the same. It is likely that most of the adsorbed acetonitrile observed is physisorbed as ice or adsorbed on the sulfur-terminated terraces. At the final stages of desorprtion, distinctly different adsorbed species are seen whose CN stretching IR bands are shifted to higher frequencies. It is likely that these minority species are at monolayer or submonolayer coverages. The exact nature of the species requires further studies. / Department of Chemistry

This thesis presents quantitative tools for the metabolomic analysis of amniotic fluid (AF) using vibrational spectroscopy. A total of 300 AF samples were collected for this retrospective cohort study and both Raman and near infrared (NIR) spectra were measured. Spectral data was compressed using a Haar wavelet transform and stage-wise multilinear regression (MLR). Calibration models were calculated for glucose, lactate and uric acid concentrations in AF. Birth weight, gestational diabetes mellitus (GDM) and gestational age were classified with the resulting compressed Raman and NIR spectra, using a genetic algorithm (GA) and a cross-validation approach. Results show that both Raman and NIR spectra of AF were not able to estimate the concentrations of glucose, lactate or uric acid with high precision. However, metabolomic analysis of AF Raman and NIR spectra was capable of estimating the development of GDM, abnormal birth weights as well as gestational ages with sensitivities >75% and specificities >77%. In addition, Raman and NIR metabolomic profiles showed a statistical difference in patients delivering preterm. Of the two spectroscopic analyses studied, NIR spectroscopy of AF has the potential to become a robust and non-invasive diagnostic tool for maternal and fetal health.

Hyperbaric pressure has been shown to affect the secondary structure of whey proteins such as beta-lactoglobulin (beta-lg). There is limited research, however, regarding the optimal conditions by which pressurization of whey proteins could lead to irreversible changes in secondary structure including the reduction of intramolecular disulfide bonds. Irreversible changes in protein conformation and breakage of disulfide bonds of whey proteins induced by high pressure might result in an increase in their digestibility and a reduction of allergenicity. Hence, the overall objective was to explore the capability of hyperbaric pressure to alter irreversibly the secondary structure of whey proteins and thereby alter their allergenic and nutritional properties. The behaviour of different genetic variants of beta-lg was studied employing variable-pressure Fourier transform infrared (FTIR) spectroscopy to establish the optimum pressures needed for their denaturation. The results showed reversible effects of pressures up to 12.0 kbar on the secondary structure of three main genetic variants of beta-lg. The individual response of the genetic variants to pressure was distinguishable despite their subtle structural differences. Pressure-induced conformational changes were studied separately in bovine serum albumin, Ca++-saturated alpha-lactalbumin and Ca ++-free alpha-lactalbumin by FTIR spectroscopy. The studies revealed that the presence of Ca++ ion and the number of disulfide bonds protects the protein molecules against pressure. As whey proteins appeared to be resistant to denaturation upon single applications of high pressure up to 1200 MPa, we developed a novel pressure processing using a combination of pulse and continuous modes at lower pressures of 400 MPa which led to irreversible denaturation of whey protein structure and disulphide bond breakage. Weanling rats fed with whey protein isolates treated by this novel low pressure processing technique showed enhanced grow

Near-infrared spectroscopy (NIRS) in an imaging technique that has gained much attention in brain-computer interfaces (BCIs). Previous NIRS-BCI studies have primarily employed temporal features, derived from the time course of hemodynamic activity, despite potential value contained in the spatial attributes of a response. In an initial offline study, we investigated the value of using joint spatial-temporal pattern classification with dynamic NIR topograms to differentiate intentional cortical activation from rest. With the inclusion of spatiotemporal features, we demonstrated a significant increase in achievable classification accuracies from those obtained using temporal features alone (p < 10-4). In a second study, we evaluated the feasibility of implementing joint spatial-temporal pattern classification in an online system. We developed an online system-paced NIRS-BCI, and were able to differentiate two cortical states with high accuracy (77.4±10.5%). Collectively, these findings demonstrate the value of including spatiotemporal features in the classification of functional NIRS data for BCI applications.

A genetic algorithm was employed to select the optimal combination of preprocessing variables, including data pretreatment, data manipulation and feature extraction procedures, for eventual clustering of a data set consisting of hyperspectral images acquired by a focal plane array Fourier transform infrared (FPA-FTIR) spectrometer. The data set consisted of infrared images of bacterial films, and the classification task investigated was the discrimination between Gram-positive and Gram-negative bacteria. The genetic algorithm evaluated combinations of variables pertaining to bacterial film thickness tolerances, baseline correction, pixel co-addition, outlier removal, smoothing, mean centering, normalization, derivatization, integration and principal component selection. Following numerous iterations of unsupervised processing, the genetic algorithm arrived at a sub-optimal solution yielding a clustering accuracy of 97.8% and a data utilization of 28.6%. The results provided insight into the co-dependencies of the pre-processing variables and their consequential effect on the selected data. The robustness of the classification model was evaluated and reinforced by the successful classification of two distinct validation sets. The overall success of the genetic algorithm suggests that it is an effective time saving resource for the optimization of pre-processing variables that does not require operator intervention.

Lipid oxidation has important consequences in the edible oil industry, producing compounds with sensory impact and thus reducing the economic value of the products. This work focused on the development of two Fourier transform infrared (FTIR) spectroscopy methods for the measurement of peroxide value (PV) and anisidine value (AV), representing the primary and secondary oxidation products of edible oils. / The infrared method developed for PV determination was based on a mathematical treatment by the partial least squares method of the information contained in the spectral region between 3750 and 3150 cm$ sp{-1}$. / The second method developed considered aldehyde content and anisidine value, a measure of secondary oxidation products. / The two methods developed are rapid ($ sim$2 min/sample) and have the advantage of being automatable. An infrared system coupled to a computer can collect the spectrum of an oil, analyze it and present a report without the need for personnel trained in FTIR spectroscopy. The cost of such a system would rapidly be absorbed through savings on personnel cost, time and chemical reagents required for conventional chemical methods and as such provides a useful advance in quality control methodology for the edible oils sector. (Abstract shortened by UMI.)

This represents a proof-of-concept study of the appropriateness of vibrational and NMR spectroscopy for predicting the molecular structure of large molecules on the basis of a library of small molecules. Density Functional Theory (DFT) B3LYP/6-311G was used generate all spectra. 20 model compounds comprising two multiple-ringed polynuclear aromatic hydrocarbons (PAHs) connected by varying aliphatic chain-lengths were investigated. A least squares optimization algorithm was developed to determine the contribution of molecular subunits in the model compounds. 1H and 13C NMR spectroscopy failed to identify subunits unambiguously even with a constrained library. By contrast, IR and Raman results independently identified 40% and 65% respectively and jointly more than 80 % of the aromatic groups present; however, the aliphatic chain-length was poorly defined in general. IR and Raman spectroscopy are a suitable basis for spectral decomposition and should play a greater role in the identification of ringed subunits present in ill-defined hydrocarbons / Chemical Engineering