Ab initio data-analytics study of carbon-dioxide activation on semiconductor oxide surfaces

The excessive emissions of carbon dioxide (CO$_2$) into the atmosphere threaten to shift the CO$_2$ cycle planet-wide and induce unpredictable climate changes. Using artificial intelligence (AI) trained on high-throughput first principles based data for a broad family of oxides, we develop a strategy for a rational design of catalytic materials for converting CO$_2$ to fuels and other useful chemicals. We demonstrate that an electron transfer to the $\pi^*$-antibonding orbital of the adsorbed molecule and the associated bending of the initially linear molecule, previously proposed as the indicator of activation, are insufficient to account for the good catalytic performance of experimentally characterized oxide surfaces. Instead, our AI model identifies the common feature of these surfaces in the binding of a molecular O atom to a surface cation, which results in a strong elongation and therefore weakening of one molecular C-O bond. This finding suggests using the C-O bond elongation as an indicator of CO$_2$ activation. Based on these findings, we propose a set of new promising oxide-based catalysts for CO$_2$ conversion, and a recipe to find more

Oxide-supported carbonates reveal a unique descriptor for catalytic performance in the oxidative coupling of methane (OCM)

The oxidative coupling of methane (OCM) is a promising reaction for direct conversion of methane to higher hydrocarbons. The reaction can be performed over oxide-based catalysts with very diverse elemental composition. Yet, despite decades of research, no general common structure-activity relationship has been deduced. Our recent statistical meta-analysis across a wide range of catalyst compositions reported in the literature suggested that only the catalysts combining thermodynamically stable (under reaction conditions) carbonate and thermally stable oxide support exhibit good catalytic performance. Guided by these findings we explore now experimentally correlations between descriptors for structure, stability and decomposition behavior of supported metal carbonates vs. the materials’ respective performance in OCM catalysis. In this study, carbonates of Rb, Cs and Mg were supported on oxides of Sm, Y, Gd, Ce, Sr and Ba, tested in OCM and studied by IR spectroscopy and thermal analysis. From the evaluation of six proposed property-descriptors we derive a statistically robust volcano-type correlation between the onset temperature of carbonate decomposition and the C2 yield, indicating the importance of CO2 adsorption and surface carbonates in selective methane conversion. Moreover, we discuss mechanisms that can account for the observed property-performance correlation across a wide range of OCM catalysts. Carbonate species are suggested to block highly reactive sites during OCM catalysis, which reduces overoxidation and enables the formation of C2 products