Matrix-assisted diffusion-ordered spectroscopy:application of surfactant solutions to the resolution of isomer spectra

The component spectra of a mixture of isomers with nearly identical diffusion coefficients cannot normally be distinguished in a standard diffusion-ordered spectroscopy (DOSY) experiment but can often be easily resolved using matrix-assisted DOSY, in which diffusion behaviour is manipulated by the addition of a co-solute such as a surfactant. Relatively little is currently known about the conditions required for such a separation, for example, how the choice between normal and reverse micelles affects separation or how the isomer structures themselves affect the resolution. The aim of this study was to explore the application of sodium dodecyl sulfate (SDS) normal micelles in aqueous solution and sodium 1,4-bis(2-ethylhexyl)sulfosuccinate (AOT) aggregates in chloroform, at a range of concentrations, to the diffusion resolution of some simple model sets of isomers such as monomethoxyphenols and short chain alcohols. It is shown that SDS micelles offer better resolution where these isomers differ in the position of a hydroxyl group, whereas AOT aggregates are more effective for isomers differing in the position of a methyl group. For both the normal SDS micelles and the less well-defined AOT aggregates, differences in the resolution of the isomers can in part be rationalised in terms of differing degrees of hydrophobicity, amphiphilicity and steric effects. Copyright © 2012 John Wiley & Sons, Ltd.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.