Membrane electrode assembly (MEA) electrolyzers offer a means to scale up CO2-to-ethylene electroconversion using renewable electricity and close the anthropogenic carbon cycle. To date, excessive CO2 coverage at the catalyst surface with limited active sites in MEA systems interferes with the carbon-carbon coupling reaction, diminishing ethylene production. With the aid of density functional theory calculations and spectroscopic analysis, here we report an oxide modulation strategy in which we introduce silica on Cu to create active Cu-SiOx interface sites, decreasing the formation energies of OCOH* and OCCOH*—key intermediates along the pathway to ethylene formation. We then synthesize the Cu-SiOx catalysts using one-pot coprecipitation and integrate the catalyst in a MEA electrolyzer. By tuning the CO2 concentration, the Cu-SiOx catalyst based MEA electrolyzer shows high ethylene Faradaic efficiencies of up to 65% at high ethylene current densities of up to 215 mA cm−2; and features sustained operation over 50 h.

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External electric fields can modify binding energies of reactive surface species and enhance catalytic performance of heterogeneously catalyzed reactions. In this work, we used density functional theory (DFT) calculations assisted and accelerated by a deep learning algorithmto investigate the extent to which ruthenium-catalyzed ammonia synthesis would benefit from application of such external electric fields. This strategy allows us to determine which electronic properties control a molecule's degree of interaction with external electric fields. Our results show that (1) field-dependent adsorption/reaction energies are closely correlated to the dipole moments of intermediates over the surface, (2) a positive field promotes ammonia synthesis by lowering the overall energetics and decreasing the activation barriers of the potential rate-limiting steps (e.g., NH 2 hydrogenation) over Ru, (3) a positive field (>0.6 V/Å) favors the reaction mechanism by avoiding kinetically unfavorable NN bond dissociation over Ru(1013), and (4) local adsorption environments (i.e., dipole moments of the intermediates in the gas phase, surface defects, and surface coverage of intermediates) influence the resulting surface adsorbates' dipole moments and further modify field-dependent reaction energetics. The deep learning algorithm developed here accelerates field-dependent energy predictions with acceptable accuracies by five orders of magnitudes compared to DFT alone and has the capacity of transferability, which can predict field-dependent energetics of other catalytic surfaces with high-quality performance using little training data.

Hybrid organic-inorganic heterogeneous catalytic interfaces, where traditional catalytic materials are modified with selfassembled monolayers (SAMs), create promising features to control a wide range of catalytic processes through the design of dual organic-inorganic active sites and the induced confinement effect. To provide a fundamental insight, we investigated CO 2 electroreduction into valuable C 2 chemicals (CO 2 RR-to-C 2 ) over SAM-modulated Cu. Our theoretical results show that 1/4 monolayer aminothiolates improve the stability, activity and selectivity of CO 2 RR-to-C 2 by: (1) decreasing surface energy to suppress surface reconstruction; (2) facilitating CO 2 activation and CÀ C coupling through dual organic-inorganic (i. e., À NH, Cu) active sites; (3) promoting CÀ C coupling via confinement effects that enlarge the adsorption energy difference between CO* and COH*; (4) inducing local electric fields to Cu surface and changing its dipole moment and polarizability to be in favor of CÀ C coupling under electrode/electrolyte interfacial electric field.

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