NMR is a tool of choice for the measurement of diffusion coefficients of species in solution. The DOSY experiment, a 2D implementation of this measurement, has been proven to be particularly useful for the study of complex mixtures, molecular interactions, polymers, etc. However, DOSY data analysis requires to resort to the inverse Laplace transform, in particular for polydisperse samples. This is a known difficult numerical task for which we present here a novel approach. A new algorithm based on a splitting scheme and on the use of proximity operators is introduced. Used in conjunction with a Maximum Entropy and hybrid regularisation, this algorithm converges rapidly and produces results robust against experimental noise. This method has been called PALMA. It is able to reproduce faithfully monodisperse as well as polydisperse systems, and numerous simulated and experimental examples are presented. It has been implemented on the server where users can have their datasets processed automatically.
In this paper we present a general approach for the blind identification of compounds from solutions using NMR spectroscopy and blind source separation algorithms.
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