High-Throughput Computational Screening of Bioinspired Dual-Atom Alloys for CO2 Activation

Abstract

CO2 activation is an integral component of thermocatalytic and electrocatalytic CO2 conversion to liquid fuels and value-added chemicals. However, the thermodynamic stability of CO2 and the high kinetic barriers to activating CO2 are significant bottlenecks. In this work, we propose that dual atom alloys (DAAs), homo- and heterodimer islands in a Cu matrix, can offer stronger covalent CO2 binding than pristine Cu. The active site is designed to mimic the Ni-Fe anaerobic carbon monoxide dehydrogenase CO2 activation environment in a heterogeneous catalyst. We find that combinations of early transition metals (TMs) and late TMs embedded in Cu are thermodynamically stable and can offer stronger covalent CO2 binding than Cu. Additionally, we identify DAAs that have CO binding energies similar to Cu, both to avoid surface poisoning and to ensure attainable CO diffusion to Cu sites so that the C-C bond formation ability of Cu can be retained in conjunction with facile CO2 activation at the DAA sites. Machine learning feature selection reveals that the more electropositive dopants are primarily responsible for attaining the strong CO2 binding. We propose seven Cu-based DAAs and two single atom alloys (SAAs) with early TM late TM combinations, (Sc, Ag), (Y, Ag), (Y, Fe), (Y, Ru), (Y, Cd), (Y, Au), (V, Ag), (Sc), and (Y), for facile CO2 activation.