Carbon nanotube (CNT)-supported Pt nanoparticles with an average diameter of 3.8 {plus minus} 0.7 nm are synthesized using water-in-hexane microemulsion as a template. The Pt nanoparticles are distributed evenly on the outside surface of every CNT and exhibit high catalytic activities for hydrogenation reactions including conversion of benzene to cyclohexane. The kinetics of the conversion of benzene to cyclohexane catalyzed by the CNT-supported Pt catalyst was studied by proton NMR and by fluorescence, which is a good way of monitoring the decrease of benzene concentration in the system. The catalytic hydrogenation process follows a zero order reaction with a rate constant of 1.16 × 10-3 mole/h for neat benzene and 3.58 x 10-3 mole/h for benzene dissolved in ethanol.
A pattern classification system for the identification of UV-visible synchronous fluorescence of petroleum oils is developed. The system is a composite of three phases, namely, feature extraction, feature selection and pattern classification. These phases are briefly described, focusing particularly on the classification method. A method called successive feature elimination process (SFEP) is used for feature selection and a proximity index classifier (PIC) is
developed for classification. The feature selection method extracts as many features from spectra as conveniently possible and then applies the SFEP process to remove the redundant features. From the remaining features a significantly smaller feature subset is selected that enhances the recognition performance of the PIC classifier. The SFEP and PIC methods are formally described. These methods are successfully applied to 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 system was trained on the design set on which the recognition performance was 100%. The 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.
Multivariate Optical Computing (MOC) devices have the potential of greatly simplifying as well as reducing the cost of applying the mathematics of multivariate regression to problems of chemical analysis in the real world. These devices utilize special optical interference coatings known as multivariate optical elements (MOEs) that are encoded with pre-determined spectroscopic patterns to selectively quantify a chemical species of interest in the presence of other interfering species. A T-format prototype of the first optical computing device is presented utilizing a multilayer MOE consisting of alternating layers of two metal oxide films (Nb2O5 and SiO2) on a BK-7 glass substrate. The device was tested by using it to quantify copper uroporphyrin in a quaternary mixture consisting of uroporphyrin (freebase), tin uroporphyrin, nickel uroporphyrin, and copper uroporphyrin. A standard error of prediction (SEP) of 0.86(mu) M was obtained for copper uroporphyrin.
A new algorithm for the design of optical computing filters for chemical analysis otherwise known as Multivariate Optical Elements (MOEs), is described. The approach is based on the nonlinear correlation of the MOE layer thicknesses to the standard error in sample prediction for the chemical species of interest using a modified version ofthe Gauss-Newton nonlinear optimization algorithm. The design algorithm can either be initialized by random layer thicknesses or by a pre-existing design. The algorithm has been successfully tested by using it to design a MOE for the determination of copper uroporphynn in a quaternary mixture of uroporphyrin (freebase), nickel uroporphyrin, copper uroporphynn, and tin uroporphyrin.
The broader definition of chemometrics includes methods such as pattern recognition (PR) and signal/image processing for noninvasive analysis and interpretation of data. These methods are among the most powerful tools currently available for noninvasively examining spectroscopic and other chemical data. Using spectral data, these systems have found a variety of applications employing analytical techniques for gas chromatography, fluorescence IR or NMR spectroscopy, etc. An advantage of PR approaches is that they make no a priori assuniption regarding the stmcture of the spectra. However, a majority of these systems rely on hunianjudgment for parameter selection and classification of spectra. Generally a spectral pattern recognition (SPR) problem is considered as a group of several subproblems. We considered a SPR problem as a group of five subproblems: spectra acquisition, feature extraction, feature selection, spectra organization, and spectra classification. One of the basic issues in PR approaches is to determine and measure the discriminatory features useful for successful classification. A spectral pattern classification system, combining spectral feature extraction and selection, and decision-theoretic approaches, is developed. It is shown how such a system can be used for analysis of large data analysis, warehousing, and interpretation.
12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.
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 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 (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.
We examine the use of artificial neural networks to classify IR spectra of organophosphorus pesticides and chemically related compounds. The spectra used were contributed from commercial libraries, government agencies, and government contractors and include spectra of pesticides, industrial precursors, hydrolysis products and other organophosphorus compounds. The data were pretreated to reduce artifacts arising from the variety of collection sources. The treated spectra were divided into spectral 'bins' of equal frequency width and transduced into data vectors whose elements consisted of the average absorbance value of the corresponding spectral bin. The spectral data vectors served as inputs to neural networks examined as spectral classifiers.
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.
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.
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.
The analysis of polychlorinated biphenyls (PCBs) generally requires selectivity and sensitivity. Even after cleanup, PCBs are usually at ultratrace levels in field samples, mixed in with other halocarbons, hydrocarbons, lipids, etc. The levels of PCBs typically found in water, soil, tissue, food, biota, and other matrices of interest are in the parts per billion (ppb) range. Most current measurement techniques for PCBs require chromatographic separations and are not practical for routine analysis. There is a strong need to have rapid and simple techniques to screen for PCBs under field conditions. The use of field screening analysis allows rapid decisions in remedial actions and reduces the need for sample preparations and time- consuming laboratory analyses. Field screening techniques also reduce the cost of clean-up operations. This paper describes a simple screening technique based on room temperature phosphorescence (RTP) and provides an overview of both this analytical procedure to detect trace levels of PCBs in environmental samples.
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