Electronic Complexity of Active Site Models for FeNC Catalysts: A Systematic Study of Truncation Effects in Molecular and Periodic Models
While single-atom catalysts have been widely studied experimentally and computationally due to their high potential for small molecule activation reactions, the structures and electronic details of their active sites remain elusive. Much progress has been made with nuclei-specific spectroscopy methods, such as Mössbauer spectroscopy to probe FeNC catalysts for the oxygen reduction reaction. These studies are often complemented by computational studies on active site models. We here report on the optimal model size for computational studies of FeNC catalysts with molecular and periodic approaches using two prominent FeNC active site models, FeN4C10 (pyridinic nitrogen coordination) and FeN4C12 (pyrrolic nitrogen coordination). We furthermore unveil the electronic complexity of these models to include not only the expected low spin, intermediate spin and high spin configurations, but in addition intrasystem redox events and unpaired electrons in the graphene-like environment that ferromagnetically or antiferromagnetically couple with the unpaired electrons located on iron. A key conclusion is that square-planar structures fail to explain the experimentally observed high spin species. Instead, axial displacements of iron or binding of axial ligands are needed to stabilise the high spin configuration, which has implications for the interpretation of experimental data and thus the mechanism of the oxygen reduction reaction.
Authors
- Charlotte Gallenkamp (ORCID: https://orcid.org/0000-0002-8848-5772)
- Young-Joon Song (ORCID: https://orcid.org/0000-0002-2120-6142)
- Niklas von Rhein (ORCID: https://orcid.org/0009-0006-9258-8797)
- Vera Krewald (ORCID: https://orcid.org/0000-0002-4749-4357)
- Roser Valentí (ORCID: https://orcid.org/0000-0003-0497-1165)
- Genís Lleopart Motis
Institutions
- Goethe University Frankfurt (DE)
- Technische Universität Darmstadt (DE)
- Universitat de Barcelona (ES)
Publication Details
- Journal
- The Journal of Physical Chemistry C
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1021/acs.jpcc.5c02746
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52