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Polycondensation as a Universal Method for Preparing High-Density Single-Atom Catalyst Libraries
Jaques-Christopher Schmidt1, Jan Romano-deGea1, Dragos C Stoian2
1Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland.
Researchers developed a universal method to create high-density single-atom catalysts (HD-SACs) using a bottom-up approach. This scalable technique enables diverse metal-support combinations for advanced catalytic applications.
Area of Science:
- Materials Science
- Catalysis
- Nanotechnology
Background:
- Single-atom catalysts (SACs) offer enhanced efficiency by maximizing metal dispersion.
- Existing methods for high-density SACs (HD-SACs) are limited by specific material combinations and high-temperature requirements.
- A need exists for versatile and scalable synthesis of HD-SACs.
Purpose of the Study:
- To introduce a universal bottom-up approach for synthesizing mono- and bimetallic HD-SACs.
- To demonstrate the broad applicability of this method across various metal-ligand systems.
- To validate the scalability and robustness of the synthetic strategy.
Main Methods:
- Utilized polycondensation of 1,2,4,5-benzenetetramine with metal monomers featuring 1,10-phenanthroline-5,6-dione ligands.
- Characterized synthesized materials for atomic dispersion and metal loading.
- Investigated catalytic performance in electrocatalytic and photocatalytic reactions.
Main Results:
- Achieved atomically dispersed mono- and bimetallic HD-SACs with metal loadings up to 27.5 wt%.
- Demonstrated high structural stability and remarkable operational stability of the catalysts.
- Successfully scaled up and automated the synthesis process, confirming robustness and reproducibility.
Conclusions:
- The developed bottom-up approach provides a universal and scalable route to synthesize diverse HD-SACs.
- These HD-SACs exhibit excellent catalytic activity and stability for various applications.
- The automated and scalable nature of this method facilitates data-driven materials discovery.
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