Unlocking High Alcohol Product Selectivity in Methane-to-Ethanol Conversion at Practically Relevant Current Density via Dual-Site Driven Cascade Electrocatalysis

Abstract

Electrochemical conversion of methane (CH4) represents a sustainable route for converting greenhouse gases into valuable liquid fuels and chemicals. However, achieving high-yield products at industrially relevant current densities remains a formidable challenge. Here we report a machine learning-guided Mo-Cu dual-site cascade catalytic strategy, enabling selective modulation of key *CH3O and yielding ethanol (EtOH) electrosynthesis. This system delivers a current density of 103 mA cm-2 with an EtOH Faradaic efficiency of 55.8% ± 0.2%, establishing new performance benchmarks. Mechanistic and DFT analyses reveal that CH4 is activated by a three-electron *O2--mediated oxidation pathway, while *CH3 spillover from Mo to Mo-Cu active sites facilitates exothermic C-C coupling, leading to high-efficiency EtOH production. Techno-economic analysis projects that integrating renewable electricity can lower the CH4-to-EtOH production cost from $2.12 kg-1 to $1.50 kg-1 within a decade, offering a 53% energy return. This work establishes a cascade-regulated, dual-site framework for efficient CH4-to-EtOH conversion, and offers a framework for machine learning-assisted catalyst design, contributing to cleaner energy technologies and substantial reductions in greenhouse gas emissions.