Activity and Selectivity Roadmap for C–N Electro-Coupling on MXenes

Abstract

Electrochemical coupling between carbon and nitrogen species to generate high-value C–N products, including urea, presents significant economic and environmental potentials for addressing the energy crisis. However, this electrocatalysis process still suffers from limited mechanism understanding due to the complex reaction networks, which restricts the development of electrocatalysts beyond trial-and-error practices. In this work, we aim to improve the understanding of the C–N coupling mechanism. This goal was achieved by constructing the activity and selectivity landscape on 54 MXene surfaces by density functional theory (DFT) calculations. Our results show that the activity of the C–N coupling step is largely determined by the *CO adsorption strength (E ad‑CO), while the selectivity relies more on the co-adsorption strength of *N and *CO (E ad‑CO and E ad‑N). Based on these findings, we propose that an ideal C–N coupling MXene catalyst should satisfy moderate *CO and stable *N adsorption. Through the machine learning-based approach, data-driven formulas for describing the relationship between E ad‑CO and E ad‑N with atomic physical chemistry features were further identified. Based on the identified formula, 162 MXene materials were screened without time-consuming DFT calculations. Several potential catalysts were predicted with good C–N coupling performance, such as Ta2W2C3. The candidate was then verified by DFT calculations. This study has incorporated machine learning methods for the first time to provide an efficient high-throughput screening method for selective C–N coupling electrocatalysts, which could be extended to a wider range of electrocatalytic reactions to facilitate green chemical production.

Linking CO to Urea Production from CO2 and NO3–/NO2– Co-Electrolysis on Transition Metals

“…Prior theoretical studies have indicated that bond formation reactions are not favored on strong binding metals . This pattern is particularly important for reactions occurring via the Langmuir–Hinshelwood mechanism due to the simultaneous weakening of two strong adsorbate–surface interactions . On the other hand, this trend is expected to be less important for reactions via the Eley–Rideal mechanism as it involves only weakening one adsorbate–surface interaction.…”

“…Prior theoretical studies have indicated that bond formation reactions are not favored on strong binding metals . This pattern is particularly important for reactions occurring via the Langmuir–Hinshelwood mechanism due to the simultaneous weakening of two strong adsorbate–surface interactions .…”

High-Throughput Screening of Sulfur Reduction Reaction Catalysts Utilizing Electronic Fingerprint Similarity

“…Huang et al used machine learning to reveal a strong correlation between the decomposition barrier of Na 2 S and the number of outermost electrons of the metal element . Based on the valence electrons and electronegativity of MXene surface and subsurface atoms, several potential MXene catalysts with good C–N coupling performance were discovered …”

Insights into the Structure-Sensitive Catalytic Performance of Palladium–Copper Alloys in Electrochemical Urea Synthesis

“…Numerous studies have shown that the adsorption strength of reaction intermediates on bimetallic alloys is mainly affected by geometric (e.g., coordination number, interatomic distances) − and electronic (atomic combinations) effects. − Thanks to the advances in theoretical simulation techniques, now it is possible to predict activity and selectivity based on the scaling relationship between the adsorption energy of key intermediates and geometry/electronic descriptors. , For example, Xiao et al built a Gibbs free energy landscape and constructed a volcano plot to predict potential two-dimensional (2D) metal borides (MBenes) catalysts for electrochemical urea synthesis. Qiao et al revealed that the activity of the C–N coupling step is largely determined by the *CO adsorption strength, while the selectivity relies more on the coadsorption strength of *N and *CO. Zhang et al constructed a volcano plot as a function of the free energy of *NCON intermediate to predict MBenes for urea formation. Zhu et al proposed a five-step high-throughput screening method for excellent catalytic activity and a five-aspect high-throughput screening strategy for outstanding catalytic selectivity.…”