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Predicting Internal Versus External Nanoparticle Formation in Zr-Based Metal-Organic Frameworks.
Zhaomin Su1, Yuhang Song1, Yibin Jiang1
1iChem, State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.
Journal of the American Chemical Society
|June 17, 2026
Summary
Controlling where metal nanoparticles form on metal-organic frameworks (MOFs) is now predictable. A new model guides the placement of nanoparticles within MOF pores or on surfaces for tailored catalysis.
Area of Science:
- Materials Science
- Catalysis
- Nanotechnology
Background:
- Metal nanoparticles (NPs) on metal-organic frameworks (MOFs) can be located inside pores or on external surfaces, creating different catalytic sites.
- Controlling NP spatial localization is crucial for catalyst design but is often an empirical process.
- Understanding and predicting NP placement is key to developing advanced MOF-based catalysts.
Purpose of the Study:
- To develop a predictive model for nanoparticle spatial localization in zirconium-based MOFs.
- To identify key factors governing whether NPs form internally or externally within MOFs.
- To guide the rational design of MOF-supported catalysts with controlled NP positions.
Main Methods:
- A controlled double-solvent method was used to introduce metal precursors into MOF pore systems.
- An experimental dataset of 10 transition metals across 11 Zr-based MOFs was created.
- Transmission electron microscopy (TEM) classified NP positions (internal vs. external).
- Machine learning combined metal descriptors (affinity, mobility) and MOF chemistry (linker properties) to build a predictive model.
Main Results:
- A predictive model was developed, accurately separating internal NP confinement from external NP formation.
- The model identified a localization boundary governed by metal-oxygen node affinity, metal mobility on linkers, and linker heteroatom chemistry.
- External validation on new MOFs confirmed the model's predictive power.
Conclusions:
- Nanoparticle spatial localization in MOF-supported catalysts is a predictable outcome based on metal-framework interactions.
- The developed model provides a framework for controlling NP position in porous materials.
- This work enables the rational design of MOF catalysts with optimized nanoparticle distribution for enhanced performance.
