Diffusive motion in confined three-site relaxation exchange was studied in Monte Carlo simulations. In thermodynamic equilibrium, the cross-peaks in NMR relaxation exchange maps are symmetric. Asymmetry is observed in mass-balanced driven equilibrium corresponding to circular flow. If this motion can be stimulated by external forces, it can be beneficial to heterogeneous catalysis.

Recent advances in machine learning have opened new opportunities toward the automated analysis and spectral reconstruction of highly complex NMR spectra, including ones encountered in metabolomics. We demonstrate the combined power of the deep neural network DEEP Picker 1D and the Voigt Fitter1D software for the quantitative streamlined analysis of 1D 1H NMR spectra, extending the reach of a wide range of NMR applications.

Quantitative analysis of mixtures is a challenge in applications ranging from foods to industrial chemistry. Nuclear magnetic resonance (NMR) is increasingly used for analysis; however, overlapping peaks in the spectrum pose a major difficulty, especially for the new generation of benchtop NMR instruments. We propose a fast and simple model-based approach to enhance the accuracy of quantification. We demonstrate it on industrially relevant test samples where the accuracy is increased by 40 %.

Nuclear magnetic resonance is a technique that allows the measurement of the nanoscale distances between the atoms of a molecule. It is often relied upon for finding the structure of a molecule and how atoms are bonded, but measurements become inaccurate for large distances and therefore for large biological molecules. This research presents a new software for calculating the distances between nuclei and unpaired electrons which offers more accurate long-range distances in biological molecules.