Catalytic tango in diatomic catalysts: from precision-guided pair construction to machine-learning-driven
Bowen Jiang1, Jishui Huang2, Kuibo Yin1
1SEU-FEI Nano-Pico Center, Key Lab of MEMS of Ministry of Education, Southeast University, Nanjing 210096, P. R. China. yinkuibo@seu.edu.cn.
Diatomic catalysts (DACs) feature two adjacent metal centers working together, enhancing catalytic activity. This review explores their construction, cooperative mechanisms, and machine learning-assisted design for advanced catalysis.
Area of Science:
- Catalysis
- Materials Science
- Nanotechnology
Background:
- Single-atom catalysts offer precision but lack cooperative effects.
- Diatomic catalysts (DACs) bridge single atoms and clusters, enabling cooperative two-site catalysis.
- DACs utilize tunable composition, geometry, and microenvironment for enhanced performance.
Purpose of the Study:
- To review strategies for constructing diatomic catalysts (DACs).
- To discuss modes of intersite cooperativity in DACs.
- To survey advanced characterization and design approaches for DACs.
Main Methods:
- Precision-guided pair construction using density generators, MOFs, COFs, and 2D frameworks.
- Ordered coordination networks and multinuclear metal-organic complexes for pre-organized metal dimers.
- Atomic-resolution electron microscopy and correlative imaging combined with machine learning.
Main Results:
- DACs enable cross-site electronic coupling and dual-site adsorption.
- Intersite cooperativity includes charge redistribution, orbital hybridization, and sequential reaction pathways.
- Machine learning aids in identifying, classifying, and screening neighboring metal sites.
Conclusions:
- DACs offer a unique platform for cooperative catalysis beyond single-atom limits.
- Advanced construction and characterization methods are crucial for DAC development.
- Machine learning shows promise for accelerating the discovery and design of novel diatomic catalysts.
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