Developing micro-kinetic model for electrocatalytic reduction of carbon dioxide on copper electrode

Multiscale Modeling of CO2 Electrochemical Reduction on Copper Electrocatalysts: A Review of Advancements, Challenges, and Future Directions

Although CO2 contributes significantly to global warming, it also offers potential as a raw material for the production of hydrocarbons such as CH4, C2H4 and CH3OH. Electrochemical CO2 reduction reaction (eCO2RR) is an emerging technology that utilizes renewable energy to convert CO2 into valuable fuels, solving environmental and energy problems simultaneously. Insights gained at any individual scale can only provide a limited view of that specific scale. Multiscale modeling, which involves coupling atomistic-level insights (density functional theory, DFT) and (Molecular Dynamics, MD), with mesoscale (kinetic Monte Carlo, KMC, and microkinetics, MK) and macroscale (computational fluid dynamics, CFD) simulations, has received significant attention recently. While multiscale modeling of eCO2RR on electrocatalysts across all scales is limited due to its complexity, this review offers an overview of recent works on single scales and the coupling of two and three scales, such as "DFT+MD", "DFT+KMC", "DFT+MK", "KMC/MK+CFD" and "DFT+MK/KMC+CFD", focusing particularly on Cu-based electrocatalysts as copper is known to be an excellent electrocatalyst for eCO2RR. This sets it apart from other reviews that solely focus exclusively on a single scale or only on a combination of DFT and MK/KMC scales. Furthermore, this review offers a concise overview of machine learning (ML) applications for eCO2RR, an emerging approach that has not yet been reviewed. Finally, this review highlights the key challenges, research gaps and perspectives of multiscale modeling for eCO2RR.

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.

Recent Progress on Copper-Based Bimetallic Heterojunction Catalysts for CO2 Electrocatalysis: Unlocking the Mystery of Product Selectivity

Copper-based bimetallic heterojunction catalysts facilitate the deep electrochemical reduction of CO2 (eCO2RR) to produce high-value-added organic compounds, which hold significant promise. Understanding the influence of copper interactions with other metals on the adsorption strength of various intermediates is crucial as it directly impacts the reaction selectivity. In this review, an overview of the formation mechanism of various catalytic products in eCO2RR is provided and highlight the uniqueness of copper-based catalysts. By considering the different metals' adsorption tendencies toward various reaction intermediates, metals are classified, including copper, into four categories. The significance and advantages of constructing bimetallic heterojunction catalysts are then discussed and delve into the research findings and current development status of different types of copper-based bimetallic heterojunction catalysts. Finally, insights are offered into the design strategies for future high-performance electrocatalysts, aiming to contribute to the development of eCO2RR to multi-carbon fuels with high selectivity.

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.

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

CO₂ can be electrochemically reduced to different products depending on the nature of catalysts. In this work, we report comprehensive kinetic studies on catalytic selectivity and product distribution of the CO₂ reduction reaction on various metal surfaces. The influences on reaction kinetics can be clearly analyzed from the variation of reaction driving force (binding energy difference) and reaction resistance (reorganization energy). Moreover, the CO₂RR product distributions are further affected by external factors such as electrode potential and solution pH. A potential-mediated mechanism is found to determine the competing two-electron reduction products of CO₂ that shifts from thermodynamics-controlled product formic acid at less negative electrode potentials to kinetic-controlled product CO at more negative electrode potentials. Based on detailed kinetic simulations, a three-parameter descriptor is applied to identify the catalytic selectivity of CO, formate, hydrocarbons/alcohols, as well as side product H₂. The present kinetic study not only well explains the catalytic selectivity and product distribution of experimental results but also provides a fast way for catalyst screening.

Effects of surface diffusion in electrocatalytic CO2 reduction on Cu revealed by kinetic Monte Carlo simulations

Kinetic Monte Carlo (KMC) methods are frequently used for mechanistic studies of thermally driven heterogeneous catalysis systems but are underused for electrocatalysis. Here, we develop a lattice KMC approach for electrocatalytic CO₂ reduction. The work is motivated by a prior experimental report that performed electroreduction of a mixed feed of ¹²CO₂ and ¹³CO on Cu; differences in the ¹³C content of C2 products ethylene and ethanol (Δ¹³C) were interpreted as evidence of site selectivity. The lattice KMC model considers the effect of surface diffusion on this system. In the limit of infinitely fast diffusion (mean-field approximation), the key intermediates ¹²CO* and ¹³CO* would be well mixed on the surface and no evidence of site selectivity could have been observed. Using a simple two-site model and adapting a previously reported microkinetic model, we assess the effects of diffusion on the relative isotope fractions in the products using the estimated surface diffusion rate of CO* from literature reports. We find that the size of the active sites and the total surface adsorbate coverage can have a large influence on the values of Δ¹³C that can be observed. Δ¹³C is less sensitive to the CO* diffusion rate as long as it is within the estimated range. We further offer possible methods to estimate surface distribution of intermediates and to predict intrinsic selectivity of active sites based on experimental observations. This work illustrates the importance of considering surface diffusion in the study of electrochemical CO₂ reduction to multi-carbon products. Our approach is entirely based on a freely available open-source code, so will be readily adaptable to other electrocatalytic systems.

Theoretical Insights on Au-based Bimetallic Alloy Electrocatalysts for Nitrogen Reduction Reaction with High Selectivity and Activity

Electrochemical reduction of nitrogen to produce ammonia at moderate conditions in aqueous solutions holds great prospect but also faces huge challenges. Considering the high selectivity of Au-based materials to inhibit competitive hydrogen evolution reaction (HER) and high activity of transition metals such as Fe and Mo toward the nitrogen reduction reaction (NRR), it was proposed that Au-based alloy materials could act as efficient catalysts for N₂ fixation based on density functional theory simulations. Only on Mo₃ Au(111) surface the adsorption of N₂ is stronger than H atom. Thermodynamics combined with kinetics studies were performed to investigate the influence of composition and ratio of Au-based alloys on NRR and HER. The binding energy and reorganization energy affected performance for the initial N₂ activation and hydrogenation process. By considering the free-energy diagram, the computed potential-determining step was either the first or the fifth hydrogenation step on metal catalysts. The optimum catalytic activity could be achieved by adjusting atomic proportion in alloys to make all intermediate species exhibit moderate adsorption. Free-energy diagrams of N₂ hydrogenation via Langmuir-Hinshelwood mechanism and hydrogen evolution via Tafel mechanism were compared to reveal that the Mo₃ Au surface showed satisfactory catalytic performance by simultaneously promoting NRR and suppressing HER. Theoretical simulations demonstrated that Au-Mo alloy materials could be applied as high-performance electrocatalysts for NRR.