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Cited by 232

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Metal–Organic Frameworks for Energy

Hou, Chun‐Chao, Xu, Qiang·2019·Advanced Energy Materials

Abstract

Serious environmental problems, growing demand for energy, and the pursuit of environmental‐friendly, sustainable, and effective energy technologies to store and transform clean energy have all drawn great attention recently. As a part of the special issue “Energy Research in National Institute of Advanced Industrial Science and Technology (AIST)” this review systematically summarizes the research progress of metal–organic framework (MOF) composites and derivatives in energy applications, including catalytic CO oxidation, liquid‐phase chemical hydrogen storage, and electrochemical energy storage and conversion. Furthermore, the correlation between MOF‐based structures, synthetic strategies, and their corresponding performances is carefully discussed. The further scope and opportunities, expected improvements and challenges are also discussed. This review will not only benefit development of more feasible protocols to fabricate nanostructures for energy systems but also stimulate further interest in MOF composites and derivatives, for energy applications.

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Crossref · 10.1002/aenm.201801307

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Metadata

Authors
Hou, C., & Xu, Q.
Venue
Advanced Energy Materials
Publisher
Wiley
Published
2018-07-05
Volume
9
Issue
23
ISSN
  • 1614-6832 (ISSN-L)
  • 1614-6840
OpenAlex
W2829375043
Cited by
232
References
5
FWCI
8.33
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metal–Organic Frameworks for Energy. https://app.cochat.ai/papers/10.1002/aenm.201801307
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Hou, C., & Xu, Q. (2019). Metal–Organic Frameworks for Energy. Advanced Energy Materials. https://doi.org/10.1002/aenm.201801307

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.