Accurate measurements of self‐diffusion coefficients with benchtop NMR using a QM model‐based approach
The measurement of self‐diffusion coefficients using pulsed‐field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well‐established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantag...
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Veröffentlicht in: | Magnetic resonance in chemistry 2022-12, Vol.60 (12), p.1113-1130 |
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Zusammenfassung: | The measurement of self‐diffusion coefficients using pulsed‐field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well‐established method. Recently, benchtop NMR spectrometers with gradient coils have also been used, which greatly simplify these measurements. However, a disadvantage of benchtop NMR spectrometers is the lower resolution of the acquired NMR signals compared to high‐field NMR spectrometers, which requires sophisticated analysis methods. In this work, we use a recently developed quantum mechanical (QM) model‐based approach for the estimation of self‐diffusion coefficients from complex benchtop NMR data. With the knowledge of the species present in the mixture, signatures for each species are created and adjusted to the measured NMR signal. With this model‐based approach, the self‐diffusion coefficients of all species in the mixtures were estimated with a discrepancy of less than 2 % compared to self‐diffusion coefficients estimated from high‐field NMR data sets of the same mixtures. These results suggest benchtop NMR is a reliable tool for quantitative analysis of self‐diffusion coefficients, even in complex mixtures.
Self‐diffusion coefficients in complex mixtures were measured using benchtop NMR spectrometers. The signal intensities of the individual species, from which the self‐diffusion coefficients can be estimated, were analysed with a QM model‐based approach. This work shows that benchtop NMR spectroscopy is a good alternative to expensive high‐field NMR spectroscopy, if it is supported by advanced analysis methods. |
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ISSN: | 0749-1581 1097-458X |
DOI: | 10.1002/mrc.5300 |