Kinetic Understanding of Catalytic Selectivity and Product Distribution of Electrochemical Carbon Dioxide Reduction Reaction

In Situ Electropolymerizing Toward EP-CoP/Cu Tandem Catalyst for Enhanced Electrochemical CO2-to-Ethylene Conversion

Electrochemical CO2 reduction has garnered significant interest in the conversion of sustainable energy to valuable fuels and chemicals. Cu-based bimetallic catalysts play a crucial role in enhancing *CO concentration on Cu sites for efficient C─C coupling reactions, particularly for C2 product generation. To enhance Cu's electronic structure and direct its selectivity toward C2 products, a novel strategy is proposed involving the in situ electropolymerization of a nano-thickness cobalt porphyrin polymeric network (EP-CoP) onto a copper electrode, resulting in the creation of a highly effective EP-CoP/Cu tandem catalyst. The even distribution of EP-CoP facilitates the initial reduction of CO2 to *CO intermediates, which then transition to Cu sites for efficient C─C coupling. DFT calculations confirm that the *CO enrichment from Co sites boosts *CO coverage on Cu sites, promoting C─C coupling for C2+ product formation. The EP-CoP/Cu gas diffusion electrode achieves an impressive current density of 726 mA cm-2 at -0.9 V versus reversible hydrogen electrode (RHE), with a 76.8% Faraday efficiency for total C2+ conversion and 43% for ethylene, demonstrating exceptional long-term stability in flow cells. These findings mark a significant step forward in developing a tandem catalyst system for the effective electrochemical production of ethylene.

Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism

Carbon-carbon (C-C) coupling is essential in the electrocatalytic reduction of CO2 for the production of green chemicals. However, due to the complexity of the reaction network, there remains controversy regarding the underlying reaction mechanisms and the optimal direction for catalyst material design. Here, we present a global perspective to establish a comprehensive data set encompassing all C-C coupling precursors and catalytic active site compositions to explore the reaction mechanisms and screen catalysts via big data set analysis. The 2D-3D ensemble machine learning strategy, developed to target a variety of adsorption configurations, can quickly and accurately expand quantum chemical calculation data, enabling the rapid acquisition of this extensive big data set. Analyses of the big data set establish that (1) asymmetric coupling mechanisms exhibit greater potential efficiency compared to symmetric coupling, with the optimal path involving the coupling CHO with CH or CH2, and (2) C-C coupling selectivity of Cu-based catalysts can be enhanced through bimetallic doping including CuAgNb sites. Importantly, we experimentally substantiate the CuAgNb catalyst to demonstrate actual boosted performance in C-C coupling. Our finding evidence the practicality of our big data set generated from machine learning-accelerated quantum chemical computations. We conclude that combining big data with complex catalytic reaction mechanisms and catalyst compositions will set a new paradigm for accelerating optimal catalyst design.

The Selectivity Origins in Ag-Catalyzed CO2 Electroreduction

Ag exhibits high selectivity of electrochemical CO2 reduction (CO2R) toward C1 products, while the hydrogenation involving the concerted proton-electron transfer (CPET) or sequential electron-proton transfer (SEPT) mechanism is still in debate. Toward a better understanding of the Ag-catalyzed electrochemical CO2R, we employed a microkinetic model based on the Marcus electron transfer theory to thoroughly investigate the selectivity of C1 products of electrochemical CO2R over the Ag(111) surface. We found that at an acidic condition of pH = 1.94, formate is the main product when U < -0.94 V via the CPET mechanism, whereas CO becomes the primary product when U > -0.94 V via the SEPT mechanism. Conversely, at an alkaline condition of pH = 13.95, formate is the main product following the SEPT mechanism. Our findings provide novel insights into the influence of external factors (applied potential and pH) on the product selectivity and hydrogenation mechanism of electrochemical CO2R.

Quantitative Analysis and Manipulation of Alkali Metal Cations at the Cathode Surface in Membrane Electrode Assembly Electrolyzers for CO2 Reduction Reactions

The stable operation of the CO2 reduction reaction (CO2RR) in membrane electrode assembly (MEA) electrolyzers is known to be hindered by the accumulation of bicarbonate salt, which are derived from alkali metal cations in anolytes, on the cathode side. In this study, we conducted a quantitative evaluation of the correlation between the CO2RR activity and the transported alkali metal cations in MEA electrolyzers. As a result, although the presence of transported alkali metal cations on the cathode surface significantly contributes to the generation of C2+ compounds, the rate of K+ ion transport did not match the selectivity of C2+, suggesting that a continuous supply of high amount of K+ to the cathode surface is not required for C2+ formation. Based on these findings, we achieved a faradaic efficiency (FE) and a partial current density for C2+ of 77 % and 230 mA cm-2, respectively, even after switching the anode solution from 0.1 M KHCO3 to a dilute K+ solution (<7 mM). These values were almost identical to those when 0.1 M KHCO3 was continuously supplied. Based on this insight, we successfully improved the durability of the system against salt precipitation by intermittently supplying concentrated KHCO3, compared with the continuous supply.

A feasible strategy for designing cytochrome P450-mimic sandwich-like single-atom nanozymes toward electrochemical CO2 conversion

Carbon dioxide electroreduction (CO2ER) presents a promising strategy for environmentally friendly CO2 utilization due to its low energy consumption. Single-atom nanozymes (SANs), amalgamating the benefits of single-atom catalysts and nanozymes, have become a hot topic in catalysis. Inspired by the intricate structure of cytochrome P450, we designed 81 sandwich-like SANs using Group-VIII transition metals (TMN4-S-TM'N4) and evaluated their performance in CO2ER using density functional theory (DFT). Our investigation revealed that most SANs display superior catalytic activity and improved specific product selectivity in comparison to the Cu (211) surface. Notably, IrN4-S-TMN4 (TM = Co, Rh, Pd) exhibited selective CO2 reduction to CO with remarkable limiting potentials (UL) of -0.11, -0.07, and -0.09 V, respectively, demonstrating potential as artificial CO dehydrogenases. Furthermore, RuN4-S-RuN4 exhibited formate dehydrogenase-like activity, resulting in selective production of HCOOH at a UL of -0.10 V. Machine learning analysis elucidated that the exceptional activity and selectivity of these SANs stemmed from precise modulation of electron density on sulfur atoms, achieved by varying transition metals in the subsurface. Our research not only identifies exceptional SANs for CO2ER but also provides insights into innovative methods for regulating non-bonding interactions and achieving sustainable CO2 conversion.

Micro-kinetic modelling of the CO reduction reaction on single atom catalysts accelerated by machine learning

Electrochemical CO2 transformation to fuels and chemicals is an effective strategy for conversion of renewable electric energy into storable chemical energy in combination with reducing green-house gas emission. Metal-nitrogen-carbon (M-N-C) single atom catalysts (SAC) have shown great potential in the electrochemical CO2 reduction reaction (CO2RR). However, exploring advanced SACs with simultaneously high catalytic activity and high product selectivity remains a great challenge. In this study, density functional theory (DFT) calculations are combined with machine learning (ML) for rapid and high-throughput screening of high performance CO reduction catalysts. Firstly, the electrochemical properties of 99 M-N-C SACs were calculated by DFT and used as a database. By using different machine learning models with simple features, the investigated SACs were expanded from 99 to 297. Through several effective indicators of catalyst stability, inhibition of the hydrogen evolution reaction, and CO adsorption strength, 33 SACs were finally selected. The catalytic activity and selectivity of the remaining 33 SACs were explored by micro-kinetic simulation based on Marcus theory. Among all the studied SACs, Mn-NC2, Pt-NC2, and Au-NC2 deliver the best catalytic performance and can be used as potential catalysts for CO2/CO conversion to hydrocarbons with high energy density. This effective screening method using a machine learning algorithm can promote the exploration of CO2RR catalysts and significantly reduce the simulation cost.