Resolvability Of Components Research Articles

Spectral deconvolution of NMR cross polarization data sets

The COmponent-REsolved (CORE) strategy has been employed, for the first time to solid state NMR spectroscopy. CORE was used to extract two time-dependent spectral components in 24 29Si{ 1H} NMR spectra, recorded on a meso-structured silica material under conditions of cross polarization evolution. No prior assumptions were made about the component bandshapes, which were both found to be skewed to higher chemical shifts. For the silica fragments close to protons this skewness could be rationalized by a distribution of the degree of condensation in the silica network; however, for the other component the non-Gaussian shape was unexpected. We expect that the same strategy could be applied to a range of experiments in solid-state NMR spectroscopy, where spectral distributions or kinetic parameters need to be accurately extracted.

Global least-squares analysis of large, correlated spectral data sets and application to chemical kinetics and time-resolved fluorescence

A general, nonlinear least-squares, direct-search strategy for the analysis of the time-dependence of single or multicomponent spectral data sets is described. The generic procedure is named component resolved (CORE) spectroscopy, and has previously been successfully applied to the special case of Fourier transform pulsed field gradient spin-echo NMR data (FT-PGSE). The main purpose of the CORE processing is to improve the quality of evaluated data through its intrinsic S/N enhancement, and to confidently allow studies on multicomponent data sets characterized by either or both of (a) extensive spectral overlap and (b) only minor differences in (for example) individual component kinetic decay rates. The generality of the CORE approach is here illustrated through examples from chemical kinetics and time-resolved fluorescence. Unlike previous strategies for the same purpose, CORE can easily be adapted to a large variety of data set types, that can be of almost unlimited size. As a consequence of its direct approach to the underlying problem, the minimization approach is also perfectly stable. The generic CORE strategy also does appear to lend itself nicely to parallel processing, which would speed up the data processing by one or two orders of magnitude in future implementations.