Towards the Computational Design of Single Atom Alloys for Methane to Ethylene Conversion

28 February 2025, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Direct conversion of methane to value-added chemicals has been a longstanding challenge in leveraging abundant natural gas resources due to unfavorable C-H bond activation and coke formation. We recently evaluated stability and reactivity of single atom alloys (SAAs) formed by atomically doping 3d-5d transition metals on Cu(111) as catalysts for direct methane conversion to C2 hydrocarbons using density functional theory calculations. Here, to further develop catalyst design principles for this chemistry, we systematically evaluate kinetics of methane dehydrogenation and C-C coupling steps on ten promising Cu(111)-based SAAs and unearth descriptors that correlate with catalyst activity and selectivity. Our results show that ethylene formation is kinetically favored over ethane formation across all SAAs studied. Notably, catalytic activity of SAAs highly correlates with their selectivity for direct methane conversion to C2 products, highlighting the synergy between dopant and host metal in enhancing methane activation and preference towards C-C coupling. In addition, we identify C2H4 adsorption energy as an effective descriptor that guides the SAA reactivity for methane activation to ethylene. Combining all analyses, we discover that iridium dispersed on copper (Ir/Cu) SAA stands out as a highly active and selective catalyst for methane to ethylene conversion. These findings pave the way for high-throughput screening of a vast SAA chemical space for the chemistry of methane transformation.

Keywords

single atom alloy
methane activation

Supplementary materials

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The authors acknowledge support by the donors of ACS Petroleum Research Fund under Doctoral New Investigator Grant 68077-DNI5, by the National Science Foundation under Award No. 2349619, and by Northeastern University, Chemical Engineering Department under start-up funding. This work was carried out using computational resources from Discovery, Research Computing at Northeastern University.
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