Influence of strong π-acceptor ligands on Cr-K-edge X-ray absorption spectral signatures and consequences for the interpretation of surface sites in the Phillips catalyst.

X-ray absorption spectroscopy (XAS) has been central to the study of the Phillips polymerization catalyst (CrO/SiO). As Cr K-edge XAS signatures are sensitive to the oxidation state, geometry and types of ligands on surface (active) sites, the superposition of these effects makes their interpretation challenging. Notably, CO has been particularly used as a reductant to generate low valent Cr sites from CrO/SiO and as a structural IR probe for analysing reduced Cr surface sites.

From a humorous post to a detailed quantum-chemical study: isocyanate synthesis revisited.

Isocyanates play an essential role in modern manufacturing processes, especially in polyurethane production. There are numerous synthesis strategies for isocyanates both under industrial and laboratory conditions, which do not prevent searching for alternative highly efficient synthetic protocols. Here, we report a detailed theoretical investigation of the mechanism of sulfur dioxide-catalyzed rearrangement of phenylnitrile oxide into phenyl isocyanate, which was first reported in 1977.

Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization.

The most in-demand instrumental methods for new functional nanomaterial diagnostics employ synchrotron radiation, which is used to determine a material's electronic and local atomic structure. The high time and resource costs of researching at international synchrotron radiation centers and the problems involved in developing an optimal strategy and in planning the control of the experiments are acute. One possible approach to solving these problems involves the use of deep reinforcement learning agents.

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra.

Application of machine learning (ML) algorithms to spectroscopic data has a great potential for obtaining hidden correlations between structural information and spectral features. Here, we apply ML algorithms to theoretically simulated infrared (IR) spectra to establish the structure-spectrum correlations in zeolites. Two hundred thirty different types of zeolite frameworks were considered in the study whose theoretical IR spectra were used as the training ML set.