Getting insights into the edge effect of FeN<sub>4</sub>C catalysts on the electroreduction of CO to methane by density functional theory calculations
Isolated FeN<sub>4</sub> atomically dispersed on graphene (FeN<sub>4</sub>C) has emerged as a versatile catalyst for the electrochemical carbon monoxide reduction reaction (CORR). However, there is still a lack of underlying understanding of the impact of the local coordination environment of FeN<sub>4</sub> on CORR performance, particularly the intrinsic activity differences among various FeN<sub>4</sub> sites. Herein, by using density functional theory, we investigated the electrocatalytic performance of FeN<sub>4</sub> embedded in the interior, armchair- and zigzag-edges of carbon sheets for converting CO to valuable C1 products. The Gibbs free energy profile analyses along the possible reaction pathways identify that the armchair-edge FeN<sub>4</sub> site (FeN<sub>4</sub>@A) exhibits the best catalytic activity and the highest CH<sub>4</sub>-selectivity through the *CHO key intermediate with the lowest free energy change of 0.31 eV. The corresponding kinetic analyses also confirm that FeN<sub>4</sub>@A possesses the fastest kinetic CORR activity with an activation barrier of 1.31 eV for the rate-determining step. In addition, the d-band center calculations suggest that the local environment of the FeN<sub>4</sub> site can regulate the overlap of the d-orbitals of the center Fe and the p-orbitals of coordination N atoms to change the position of the d-band center relative to the Fermi level, which significantly affects the catalytic activity of the FeN<sub>4</sub> site. The d-band center of FeN<sub>4</sub>@A is very close to the Fermi level in energy, indicating that the armchair-edge of graphene favors the FeN<sub>4</sub> site to achieve the high CORR performance. These results provide insight into the relationship of electronic structure-catalytic activity of different-type FeN<sub>4</sub>C catalysts, which may guide the design of novel electrode materials with improved performances for achieving efficient multi-intermediate electrocatalysis.
Authors
- Hongjing Dai
- Xiaohua Chen (ORCID: https://orcid.org/0000-0003-4567-970X)
- Di Li (ORCID: https://orcid.org/0000-0003-1674-0110)
- Yuxin Xie
Institutions
- Chongqing University (CN)
- Qiannan Normal College For Nationalities (CN)
Publication Details
- Journal
- Physical Chemistry Chemical Physics
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5cp00495k
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52