Raman Spectra Of Mixtures Research Articles

Nanotube abundance from non-negative matrix factorization of Raman spectra as an example of chemical purity from open source machine learning

The chemical purity of materials is important for semiconductors, including the carbon nanotube material system, which is emerging in semiconductor applications. One approach to get statistically meaningful abundances and/or concentrations is to measure a large number of small samples. Automated multivariate classification algorithms can be used to draw conclusions from such large data sets. Here, we use spatially-mapped Raman spectra of mixtures of chirality-sorted single walled carbon nanotubes dispersed sparsely on flat silicon/silicon oxide substrates. We use non-negative matrix factorization (NMF) decomposition in scikit-learn, an open-source, python language “machine learning” package, to extract spectral components and derive weighting factors. We extract the abundance of minority species (7,5) nanotubes in mixtures by testing both synthetic data, and real samples prepared by dilution. We show how noise limits the purity level that can be evaluated. We determine real situations where this approach works well, and identify situations where it fails.

The Effect of Noise in Raman Spectra on the Reconstruction of the Concentration of Amino Acids in the Mixture by Multivariate Curve Resolution (MCR) Analysis

Changes in the concentration of free amino acids in biological tissues is a sign of impaired protein metabolism in patients with cancer. Recently, Raman spectroscopy has been used for early diagnostics of oncological diseases. The concentrations of individual components of biological tissue (for instance, the concentrations of amino acids) can be obtained by decomposing the tissue Raman spectrum. This study was designed to evaluate the effect of noise in the Raman spectra of individual amino acids on the result of the decomposition of the spectra of an amino acid mixture. As a decomposition method, we used Multivariate Curve Resolution-Alternating Least Squares (MCR–ALS) analysis and investigate experimental Raman spectra of amino acids and mathematically simulated Raman spectra of amino acid mixtures. Noise with different signal-to-noise ratios (SNR) was artificially added to both the experimental spectra of pure amino acids and the spectra of the mixtures. Concentration values for each amino acid obtained as a result of applying the MCR–ALS analysis have been compared with the corresponding true values and the correlation coefficients have been calculated. The results show a less pronounced negative effect of noise in the case when the spectra of pure amino acids (which were used as a basis for the MCR–ALS analysis) are noisy, and a more pronounced negative effect when the spectrum of the mixture is noisy. The accuracy of reconstruction of an amino acid is also negatively affected by strong background fluorescence in the amino acid spectrum. Moreover, the results indicate that using the basis spectra with a high SNR (SNR = 5) makes it possible to successfully estimate the amino acid concentrations in a mixture even when the Raman spectrum of the mixture is noisy and has a low SNR (SNR < 5).

Application of Raman Spectroscopy for Determination of Syngas Composition.

Raman spectroscopy is a unique tool for fast analysis of multicomponent gas media. In this work, we studied the features of application of this method for monitoring the syngas (mixture of CO + H2 + CH4 + CO2 + N2) composition. To determine concentrations, we used contour fit method, where the Raman spectrum of mixture is compared with a synthetically calculated spectrum. The effects of pressure changes and various exposure times on the accuracy of measurements are investigated. It was found that effect of pressure and environment on band contours results in measurement errors several times higher than the errors caused by deviations of the signal intensities.

Quantitative analysis of the Raman spectra of mixtures of weakly interacting components by factor analysis methods

A system of 17 overlapped Raman spectra in the 250–350 cm–1 spectral region of the weakly interacting component mixture of 1,2-dichloroethane, dichloromethane and carbon tetrachloride has been subjected to the statistical methods of target factor analysis (TFA) and evolving factor analysis (EFA). The results have demonstrated that concentrations of substances with intense bands are successfully determined; however, there are difficulties in analyzing weak bands of one of the components, especially when applying a TFA technique. The results of such an analysis can be improved by minimal changes in one of the coordinates of a rotation vector. Comparison of these results with a synthetic spectral model suggests that problems with analysis of weak bands are caused by the presence of a strong band that greatly overlaps the weak one.

A Raman study of the lonization of thiocyanic acid

Raman spectra of mixtures of hydrochloric acid and ammonium thiocyanate have been determined and separated by means of a curve resolver into contributions due to the thiocyanate ion and the thiocyanic acid molecule. By this meanspKa for the latter species is found to be −1.1±0.3 in aqueous solution at 20°C.

Raman spectra of liquid mixtures (cyclohexanol and ethyl acetate)

The Raman spectrum of ethyl acetate has been studied usingλ 4358 excitation. 27 Raman lines were observed out of which 12 lines have been recorded for the first time. An assignment of the observed Raman lines has been made by a comparison with its infra-red spectrum. The Raman spectra of the mixtures of cyclohexanol and ethyl acetate at various concentrations were studied. It was found that the OH stretching frequency of cyclohexanol shifts to lower frequencies as the concentration of cyclohexanol increases. The C=O stretching frequency of ethyl acetate was found to become more and more diffuse towards shorter wavelength as the relative concentration of ethyl acetate decreased and at the lowest concentration used, the C=O was split up into two. This has been attributed to hydrogen bonding.