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The literature concerning the chemical and electrochemical reactions of nitric oxide, nitrous acid and nitrogen dioxide in aqueous solutions is reviewed briefly, with emphasis on electrochemical reductions at platinum electrodes in acidic solutions. The voltammetric behavior of NO and NO2 at a Pt electrode in perchloric acid is virtually identical to that for HNO2 and this is explained on the basis of a common electroactive precursor concluded to be NO+. Three cathodic waves are obtained for acidic solutions of NO, HNO2 and NO2. The first two waves correspond to reduction of NO+ to NO and N2O3 to NO, respectively. The presence of N2O3 results from decomposition of the parent compounds. The presence of Br- or Cl- in acidic solutions of the title compounds promotes the voltammetric reductions at lower H+ concentrations. This probably results from formation of electroactive nitrosyl halides. 相似文献
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Pierce KM Hope JL Johnson KJ Wright BW Synovec RE 《Journal of chromatography. A》2005,1096(1-2):101-110
A fast and objective chemometric classification method is developed and applied to the analysis of gas chromatography (GC) data from five commercial gasoline samples. The gasoline samples serve as model mixtures, whereas the focus is on the development and demonstration of the classification method. The method is based on objective retention time alignment (referred to as piecewise alignment) coupled with analysis of variance (ANOVA) feature selection prior to classification by principal component analysis (PCA) using optimal parameters. The degree-of-class-separation is used as a metric to objectively optimize the alignment and feature selection parameters using a suitable training set thereby reducing user subjectivity, as well as to indicate the success of the PCA clustering and classification. The degree-of-class-separation is calculated using Euclidean distances between the PCA scores of a subset of the replicate runs from two of the five fuel types, i.e., the training set. The unaligned training set that was directly submitted to PCA had a low degree-of-class-separation (0.4), and the PCA scores plot for the raw training set combined with the raw test set failed to correctly cluster the five sample types. After submitting the training set to piecewise alignment, the degree-of-class-separation increased (1.2), but when the same alignment parameters were applied to the training set combined with the test set, the scores plot clustering still did not yield five distinct groups. Applying feature selection to the unaligned training set increased the degree-of-class-separation (4.8), but chemical variations were still obscured by retention time variation and when the same feature selection conditions were used for the training set combined with the test set, only one of the five fuels was clustered correctly. However, piecewise alignment coupled with feature selection yielded a reasonably optimal degree-of-class-separation for the training set (9.2), and when the same alignment and ANOVA parameters were applied to the training set combined with the test set, the PCA scores plot correctly classified the gasoline fingerprints into five distinct clusters. 相似文献
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