Cu supported on SiO2 can be used to directly convert methane to methanol in a stepwise process with no intrinsic need for a zeolite support. Effects of parameters such as the O2 activation temperature, activation time, CH4 reaction temperature, CH4 partial pressure (p CH4) and Cu wt. % on methanol yield were investigated. Increasing the O2 activation temperature in the 200-800 °C range significantly improved the methanol yield and when carried out at 800 °C , a methanol yield of 11.5 µmol/gcatalyst was obtained after reaction with methane at 200 °C for the sample with 2 wt. % Cu. Yield per mole of Cu increased exponentially from 1.0 to 59.1 mmol with decreased Cu wt. % from 30 to 1, respectively. The increase in the O2 activation time also strongly

X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom. However, the quantitative analysis of these spectra is not straightforward. Even with the most recent advances in this area, for a given spectrum, it is not clear a priori which structural parameters can be refined and how uncertainties should be estimated. Here, we present an alternative concept for the analysis of XANES spectra, which is based on machine learning algorithms and establishes the relationship between intuitive descriptors of spectra, such as edge position, intensities, positions, and curvatures of minima and maxima on the one hand, and those related to the local atomic and electronic structure which are the coordination numbers, bond distances and angles and oxidation state on the other hand. This approach overcoms the problem of the systematic difference between theoretical and experimental spectra. Furthermore, the numerical relations can be expressed in analytical formulas providing a simple and fast tool to extract structural parameters based on the spectral shape. The methodology was successfully applied to experimental data for the multicomponent Fe:SiO2 system and reference iron compounds, demonstrating the high prediction quality for both the theoretical validation sets and experimental data.

CH3OH formation rates in CO2 hydrogenation on Cu‐based catalysts sensitively depend on the nature of the support and the presence of promoters. In this context, Cu nanoparticles supported on tailored supports (highly dispersed M on SiO2; M=Ti, Zr, Hf, Nb, Ta) were prepared via surface organometallic chemistry, and their catalytic performance was systematically investigated for CO2 hydrogenation to CH3OH. The presence of Lewis acid sites enhances CH3OH formation rate, likely originating from stabilization of formate and methoxy surface intermediates at the periphery of Cu nanoparticles, as evidenced by metrics of Lewis acid strength and detection of surface intermediates. The stabilization of surface intermediates depends on the strength of Lewis acid M sites, described by pyridine adsorption enthalpies and 13C chemical shifts of ‐OCH3 coordinated to M; these chemical shifts are demonstrated here to be a molecular descriptor for Lewis acid strength and reactivity in CO2 hydrogenation.