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

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Electrochemical C-N coupling offers sustainable routes to valuable chemicals like urea.
• Limited mechanistic understanding hinders rational electrocatalyst design, relying on trial-and-error.
• Developing efficient electrocatalysts is crucial for addressing the energy crisis and promoting green chemistry.

Purpose of the Study:

• To elucidate the mechanism of electrochemical carbon-nitrogen (C-N) coupling.
• To construct activity and selectivity landscapes for C-N coupling on MXene surfaces.
• To develop a data-driven, high-throughput screening method for novel C-N coupling electrocatalysts.

Main Methods:

• Density Functional Theory (DFT) calculations were employed to study 54 MXene surfaces.
• Analysis of adsorption strengths (*CO and *N) to determine activity and selectivity descriptors.
• Machine learning (ML) models were developed to predict catalyst performance based on atomic features.

Main Results:

• Catalyst activity is primarily governed by *CO adsorption strength, while selectivity depends on co-adsorption of *N and *CO.
• An ideal MXene catalyst requires moderate *CO and stable *N adsorption.
• ML screening identified promising candidates, including Ta2W2C3, validated by DFT.

Conclusions:

• This study establishes a mechanistic understanding of C-N coupling on MXenes.
• The integration of ML provides an efficient high-throughput screening approach for electrocatalyst discovery.
• The methodology can be extended to accelerate the development of catalysts for various green chemical production processes.