Role of Coordination Environment in Synergistic Catalysis: A Molecular Orbital Perspective on M <sub>1</sub> M <sub>2</sub> N <sub>6</sub> Catalysts
High Resolution Image Download MS PowerPoint Slide Visualizing the coordination environment-dependent synergistic mechanisms that govern the structural stability and adsorption behavior of dual-atom catalysts (DACs) is pivotal for precise catalyst design. However, insights into these mechanisms at the molecular orbital level remain elusive. Herein, we present large-scale density functional theory calculations to elucidate how synergistic effects arise from the combination of d atomic orbitals into molecular orbitals between M 1 and M 2 sites, with notable variations observed from M 1 –N 3 –M 2 –N 3 –C to M 1 –N 4 –M 2 –N 4 –C. We identify an average weakening of M 1 /M 2 –N bond strength in M 1 –N 3 –M 2 –N 3 –C relative to M 1 –N 4 –M 2 –N 4 –C, which is attributed to a shift from direct d x 2 –y 2 orbital overlap to nitrogen-mediated interactions involving the hybridization of bridge nitrogen 2 p orbitals. Using hydrogen as a model adsorbate, we demonstrate that hydrogen adsorption on M 1 –N 3 –M 2 –N 3 –C shifts from wild modulation via a bridge configuration to mild modulation through an end-on configuration, signifying a selective orbital coupling from d x 2 –y 2 to d z 2 orbitals. In contrast, hydrogen adsorption on M 1 –N 4 –M 2 –N 4 –C exhibits only mild modulation via an end-on configuration. This behavior is ascribed to the symmetry constraints of antibonding ( d - d / d *)- p * molecular orbitals near the Fermi level, mediated by nitrogen-mediated d z 2 - d z 2 * molecular orbitals. Furthermore, machine learning analyses corroborate these coordination environment-dependent synergistic mechanisms. Our findings provide a comprehensive molecular orbital-level understanding of how the interplay between coordination environments and electronic structures influences the properties of M 1 M 2 N 6 catalysts, thereby establishing a theoretical framework for enhanced DACs design.
Authors
- Ziyang Wu (ORCID: https://orcid.org/0000-0003-0779-2285)
- Zhangsheng Shi (ORCID: https://orcid.org/0000-0003-2341-2422)
- Xin Wang (ORCID: https://orcid.org/0000-0003-2686-466X)
- Zheng Shu
Institutions
- National University of Singapore (SG)
- City University of Hong Kong (HK)
- Donghua University (CN)
- Division of Materials Science and Engineering (AU)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2026-07-16
- DOI
- https://doi.org/10.1021/jacs.6c08871
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- City University of Hong Kong
- Research Grants Council, University Grants Committee
- Hong Kong Jockey Club