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

Electrochemical coupling between carbon and nitrogenspecies togenerate high-value C-N products, including urea, presentssignificant economic and environmental potentials for addressing theenergy crisis. However, this electrocatalysis process still suffersfrom limited mechanism understanding due to the complex reaction networks,which restricts the development of electrocatalysts beyond trial-and-errorpractices. In this work, we aim to improve the understanding of theC-N coupling mechanism. This goal was achieved by constructingthe activity and selectivity landscape on 54 MXene surfaces by densityfunctional theory (DFT) calculations. Our results show that the activityof the C-N coupling step is largely determined by the *CO adsorptionstrength (E (ad-CO)), while the selectivityrelies 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 couplingMXene catalyst should satisfy moderate *CO and stable *N adsorption.Through the machine learning-based approach, data-driven formulasfor describing the relationship between E (ad-CO) and E (ad-N) with atomic physicalchemistry features were further identified. Based on the identifiedformula, 162 MXene materials were screened without time-consumingDFT calculations. Several potential catalysts were predicted withgood C-N coupling performance, such as Ta2W2C3. The candidate was then verified by DFT calculations.This study has incorporated machine learning methods for the firsttime to provide an efficient high-throughput screening method forselective C-N coupling electrocatalysts, which could be extendedto a wider range of electrocatalytic reactions to facilitate greenchemical production.