Learning from virtual experiments to assist users of Small Angle Neutron Scattering in model selection.

In this work, we combine the advantages of virtual Small Angle Neutron Scattering (SANS) experiments carried out by Monte Carlo simulations with the recent advances in computer vision to generate a tool that can assist SANS users in small angle scattering model selection. We generate a dataset of almost 260.000 SANS virtual experiments of the SANS beamline KWS-1 at FRM-II, Germany, intended for Machine Learning purposes.

First characterization of chemical environments using energy dispersive inelastic x-ray scattering induced by an x-ray tube.

Energy Dispersive Inelastic X-ray Scattering (EDIXS) is a reliable technique for the discrimination and characterization of local chemical environments. By means of this methodology, the speciation of samples has been attained in a variety of samples and experimental conditions, such as total reflection, grazing incidence, and confocal setups. Until now, due to the requirement of a monochromatic and intense exciting beam, this tool had been applied using exclusively synchrotron radiation sources.