DeLyle Eastwood

High-temperature photoluminescence in sol-gel silica containing SiC/C nanostructures

Silicon carbide and carbon nanostructures were produced by pyrolysis of organosilane or aromatic compounds in nanoporous sol-gel silica glasses. Intense photoluminescence was observed in the visible and the near infrared regions, depending on material processing. Emission bands at 2.97, 2.67, 2.53, 2.41, 2.24, 2.09, 1.93, 1.13, 1.00, and 0.85 eV were observed in samples prepared at temperatures between 870 and 1220 K. Phosphorescence emission showed two lifetime components at 300 K: a 0.03 s component and a very long component of 0.5–4 s that depends on the precursors and sample processing. These lifetimes approximately doubled at 77 K. The visible emission increased significantly as the temperature was elevated from 77 to 950 K, suggesting thermally assisted light emission from sites in the silica glasses containing SiC/C nanostructures. Surface SiC vacancy defects modeled using integrated ab initio quantum mechanics/molecular mechanics calculations suggest phosphorescence may originate from C vacancy (S...

Infrared detection of volatile compounds from microorganisms

Most microorganisms evolve a suite of volatile metabolites. Some microorganism cultures evolve distinctive odors suggesting that the volatile compounds produced by microorganisms might be used to quickly distinguish microorganism types. We have measured infrared spectra of volatiles from common soil microorganisms. FTIR measurements were performed using the Bomem MB157 Fourier transform infrared spectrophotometer with ZnSe optics, using a MCT detector (500 cm-1 cut off). Spectral signatures of cultures dominated by coccus microorganisms differed from those with bacillus microorganisms. With improved infrared detection, IR signatures of microbial volatiles may be useful to characterize microorganism consortia and the predominant metabolite.

Infrared spectral classification with artificial neural networks and classical pattern recognition

Infrared spectroscopy is an important technique for measuring airborne chemicals, for pollution monitoring and to warn of toxic compound releases. Infrared spectroscopy provides both detection and identification of airborne components. Computer-assisted classification tools, including pattern recognition and artificial neural network techniques, have been applied to a collection of infrared spectra of organophosphorus compounds, and these have successfully discriminated commercial pesticide compounds from military nerve agents, precursors, and hydrolysis products. Infrared spectra for previous tests came from a commercial infrared library, with permission, from military laboratories, and from defense contractors. In order to further test such classification tools, additional infrared spectra from the NIST gas-phase infrared library were added to the data set. These additional spectra probed the tendency of the trained classifiers to misidentify unrelated spectra into the trained classes.

Pattern recognition and image processing for environmental monitoring

Pattern recognition (PR) and signal/image processing methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications employing analytical techniques for chemometrics such as gas chromatography, fluorescence spectroscopy, etc. An advantage of PR approaches is that they make no a prior assumption regarding the structure of the patterns. However, a majority of these systems rely on human judgment for parameter selection and classification. A PR problem is considered as a composite of four subproblems: pattern acquisition, feature extraction, feature selection, and pattern classification. One of the basic issues in PR approaches is to determine and measure the features useful for successful classification. Selection of features that contain the most discriminatory information is important because the cost of pattern classification is directly related to the number of features used in the decision rules. The state of the spectral techniques as applied to environmental monitoring is reviewed. A spectral pattern classification system combining the above components and automatic decision-theoretic approaches for classification is developed. It is shown how such a system can be used for analysis of large data sets, warehousing, and interpretation. In a preliminary test, the classifier was used to classify synchronous UV-vis fluorescence spectra of relatively similar petroleum oils with reasonable success.

EXPERT SYSTEM FOR CHARACTERIZATION OF FLUORESCENCE SPECTRA FOR ENVIRONMENTAL APPLICATIONS

A potentially very powerful and feasible expert system for intelligent characterization of spectra is proposed. Unlike the conventional expert systems it does not solely rely on human expert knowledge; instead, it has the capability to generate the appropriate domain dependent problem solving knowledge itself. The structure of the system is based on supervised information processing techniques and machine-based pattern recognition methods. Feature extraction techniques are used to measure appropriate spectral characteristics and then a hierarchical pattern classifier is used to characterize the spectra. This system has the potential of using different classifiers as appropriate at each of the nodes. This system was used with reasonable results on a relatively simple example of 24 ultraviolet-visible synchronous fluorescence spectra of petroleum oils (heavy crudes and No. 6 fuel oils) with Euclidean distance as a measure of dissimilarity.

Electronic Spectroscopy, Environmental Applications

Environmental analysis, most often for the detection and quantification of environmental contaminants, holds many challenges in terms of chemical variety, complexity, and quantity. Electronic spectroscopic techniques, such as UV-visible absorption and fluorescence, provide a comprehensive suite of techniques for characterizing environmental contaminants, particularly when combined with chromatographic separations.

Spectral pattern recognition: the methodology

Spectral pattern recognition (SPR) methods are among the most powerful tools currently available for noninvasively examining the spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications in chemometric systems such as gas chromatography, fluorescence spectroscopy, etc. An advantage of SPR approaches is that they made no a priori assumption regarding the structure of the spectra. However, a majority of these systems rely on human judgement for parameter selection and classification.

Photoluminescence and vibrational spectroscopic studies on weathered uranium oxides

Spectroscopic studies were performed both on uranium oxides as baseline and on uranium oxides artificially weathered under known laboratory conditions in air, varying humidity, carbon dioxide concentration, temperature and exposure to UV light. Spectroscopic techniques included photoluminescence and diffuse reflectance FTIR. Photoluminescence measurements were made using a Spex Fluorolog-3TM spectrofluorometer with phosphorimeter. FTIR measurements were made using a Bomem MB157 FTIR spectrophotometer with DTGS detector and approximately 450 cm-1 cut-off and a Graseby SelectorTM diffuse reflectance accessory with special cells and diamond dust as diluent and internal standard. Weathered-related reactions involving the uranium oxides that have been studied include oxidation and the formation of hydroxides and carbonates. Data are discussed with respect to both the reactions of the uranium oxides in the study and in context of reaction chemistry and mechanisms that have been previously documented. The results will be discussed in the context of environmental monitoring.

Environmental Applications of Electronic Spectroscopy

Environmental analysis, most often for the detection and quantification of environmental contaminants, holds many challenges in terms of chemical variety, complexity, and quantity. Electronic spectroscopic techniques, such as UV-visible absorption and fluorescence, provide a comprehensive suite of techniques for characterizing environmental contaminants, particularly when combined with chromatographic separations.

Optimal feature selection in the classification of synchronous fluorescence of petroleum oils

Pattern classification of UV-visible synchronous fluorescence of petroleum oils is performed using a composite system developed by the authors. The system consists of three phases, namely, feature extraction, feature selection and pattern classification. Each of these phases are briefly reviewed, focusing particularly on the feature selection method. Without assuming any particular classification algorithm the method extracts as much information (features) from spectra as conveniently possible and then applies the proposed successive feature elimination process to remove the redundant features. From the remaining features a significantly smaller, yet optimal, feature subset is selected that enhances the recognition performance of the classifier. The successive feature elimination process and optimal feature selection method are formally described. These methods are successfully applied for the classification of UV-visible synchronous fluorescence spectra. The features selected by the algorithm are used to classify twenty different sets of petroleum oils (the design set). A proximity index classifier using the Mahalanobis distance as the proximity criterion is developed using the smaller feature subset. The system was trained on the design set. The recognition performance on the design set was 100%. The recognition performance on the testing set was over 93% by successfully identifying 28 out of 30 samples in six classes. This performance is very encouraging. In addition, the method is computationally inexpensive and is equally useful for large data set problems as it always partitions the problem into a set of two class problems. The method further reduces the need for a careful feature determination problem which a system designer usually encounters during the initial design phase of a pattern classifier.