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Catalysis

Catalysis

The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.

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Updated: Jan 9, 2026

Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction

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Published on: April 10, 2018

Screening copper-based single-atom alloy catalysts for electrochemical nitrogen reduction.

Hengzhi Liu¹, Yang-Gang Wang¹

¹Department of Chemistry, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China. wangyg@sustech.edu.cn.

Physical Chemistry Chemical Physics : PCCP

|December 2, 2025

Summary

Researchers screened copper-based single-atom alloy (SAA) catalysts for efficient electrochemical nitrogen reduction reaction (eNRR) to ammonia. Certain SAAs showed excellent performance, stability, and selectivity, paving the way for sustainable ammonia synthesis.

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Area of Science:

Materials Science
Electrochemistry
Computational Chemistry

Background:

Direct electrochemical nitrogen reduction reaction (eNRR) is key for sustainable ammonia synthesis.
Developing highly efficient and selective electrocatalysts is a major challenge.

Purpose of the Study:

To screen copper-based single-atom alloy (SAA) catalysts for eNRR using computational methods.
To identify promising SAA candidates and understand structure-performance relationships.

Main Methods:

Density functional theory (DFT) calculations were used to screen various single-atom alloy catalysts.
Interpretable machine learning (ML), including adaptive boosting and SHapley Additive exPlanations, was employed.
Feature selection algorithms identified key descriptors for catalyst performance.

Main Results:

Several V-, Nb-, Mo-, Ta-, and W@Cu(100) SAAs demonstrated exceptional eNRR performance with low limiting potentials (> -0.30 V).
These SAAs exhibited superior structural stability and selectivity compared to conventional single-atom catalysts.
ML models accurately predicted limiting potentials, highlighting electron accumulation and transition metal valence electrons as critical factors.

Conclusions:

The study provides a robust framework for designing SAA catalysts for sustainable ammonia synthesis.
Combining DFT and interpretable ML accelerates the discovery of efficient electrocatalysts.
This approach offers a pathway to advance green electrochemical technologies.