ABSTRACT
Controlling the selectivity of CO2 hydrogenation catalysts is a fundamental challenge. In this study, the selectivity of supported Ni catalysts prepared by the traditional impregnation method was found to change after a first CO2 hydrogenation reaction cycle from 100 to 800 °C. The usually high CH4 formation was suppressed leading to full selectivity toward CO. This behavior was also observed after the catalyst was treated under methane or propane atmospheres at elevated temperatures. In situ spectroscopic studies revealed that the accumulation of carbon species on the catalyst surface at high temperatures leads to a nickel carbide-like phase. The catalyst regains its high selectivity to CH4 production after carbon depletion from the surface of the Ni particles by oxidation. However, the selectivity readily shifts back toward CO formation after exposing the catalysts to a new temperature-programmed CO2 hydrogenation cycle. The fraction of weakly adsorbed CO species increases on the carbide-like surface when compared to a clean nickel surface, explaining the higher selectivity to CO. This easy protocol of changing the surface of a common Ni catalyst to gain selectivity represents an important step for the commercial use of CO2 hydrogenation to CO processes toward high-added-value products.
ABSTRACT
The discovery of low-modulus Ti alloys for biomedical applications is challenging due to a vast number of compositions and available solute contents. In this work, machine learning (ML) methods are employed for the prediction of the bulk modulus (K) and the shear modulus (G) of optimized ternary alloys. As a starting point, the elasticity data of more than 1800 compounds from the Materials Project fed linear models, random forest regressors, and artificial neural networks (NN), with the aims of training predictive models for K and G based on compositional features. The models were then used to predict the resultant Young modulus (E) for all possible compositions in the Ti-Nb-Zr system, with variations in the composition of 2 at. %. Random forest (RF) predictions of E deviate from the NN predictions by less than 4 GPa, which is within the expected variance from the ML training phase. RF regressors seem to generate the most reliable models, given the selected target variables and descriptors. Optimal compositions identified by the ML models were later investigated with the aid of special quasi-random structures (SQSs) and density functional theory (DFT). According to a combined analysis, alloys with 22 Zr (at. %) are promising structural materials to the biomedical field, given their low elastic modulus and elevated beta-phase stability. In alloys with Nb content higher than 14.8 (at. %), the beta phase has lower energy than omega, which may be enough to avoid the formation of omega, a high-modulus phase, during manufacturing.
