Abstract

Metal-organic framework (MOF) nanoparticles, also called porous coordination polymers, are a major part of nanomaterials science, and their role in catalysis is becoming central. The extraordinary variability and richness of their structures afford engineering synergies between the metal nodes, functional linkers, encapsulated substrates, or nanoparticles for multiple and selective heterogeneous interactions and activations in these MOF-based nanocatalysts. Pyrolysis of MOF-nanoparticle composites forms highly porous N- or P-doped graphitized MOF-derived nanomaterials that are increasingly used as efficient catalysts especially in electro- and photocatalysis. This review first briefly summarizes this background of MOF nanoparticle catalysis and then comprehensively reviews the fast-growing literature reported during the last years. The major parts are catalysis of organic and molecular reactions, electrocatalysis, photocatalysis, and views of prospects. Major challenges of our society are addressed using these well-defined heterogeneous catalysts in the fields of synthesis, energy, and environment. In spite of the many achievements, enormous progress is still necessary to improve our understanding of the processes involved beyond the proof-of-concept, particularly for selective methane oxidation, hydrogen production, water splitting, CO<sub>2</sub> reduction to methanol, nitrogen fixation, and water depollution.

Keywords

Nanomaterial-based catalystMetal-organic frameworkNanotechnologyChemistryPhotocatalysisCatalysisNanomaterialsHeterogeneous catalysisNanoparticleElectrocatalystMaterials scienceOrganic chemistry

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Publication Info

Year
2019
Type
review
Volume
120
Issue
2
Pages
1438-1511
Citations
2252
Access
Closed

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2252
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Cite This

Qi Wang, Didier Astruc (2019). State of the Art and Prospects in Metal–Organic Framework (MOF)-Based and MOF-Derived Nanocatalysis. Chemical Reviews , 120 (2) , 1438-1511. https://doi.org/10.1021/acs.chemrev.9b00223

Identifiers

DOI
10.1021/acs.chemrev.9b00223
PMID
31246430

Data Quality

Data completeness: 81%