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Cited by 549

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Metal-organic framework-derived materials for electrochemical energy applications

Liang, Zibin, Zhao, Ruo, Qiu, Tianjie, Zou, Ruqiang, Xu, Qiang·2019·EnergyChem

Abstract

Abstract As emerging crystalline porous organic-inorganic hybrid materials, metal-organic frameworks (MOFs) have been widely used as sacrificial precursors for the synthesis of carbon materials, metal/metal compounds, and their composites with tunable and controllable nanostructures and chemical compositions for electrochemical energy applications. Herein, recent progress of MOF-derived nanomaterials for various electrochemical energy storage and conversion applications including Li-ion batteries, Li-S batteries, Na-ion batteries, supercapacitors, water splitting, and oxygen reduction reaction is reviewed. Structural and compositional design of MOF-derived nanomaterials is systematically summarized, which may hopefully offer inspirations and guidances for future development of MOF-derived nanomaterials for more efficient and more durable electrochemical energy applications.

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Crossref · 10.1016/j.enchem.2019.100001

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Metadata

Authors
Liang, Z., Zhao, R., Qiu, T., Zou, R., & Xu, Q.
Venue
EnergyChem
Publisher
Elsevier BV
Published
2019-07
Volume
1
Issue
1
Pages
100001
ISSN
2589-7780 (ISSN-L)
OpenAlex
W2953937574
Cited by
549
References
9
FWCI
36.21
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metal-organic framework-derived materials for electrochemical energy applications. https://app.cochat.ai/papers/10.1016/j.enchem.2019.100001
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Liang, Z., Zhao, R., Qiu, T., Zou, R., & Xu, Q. (2019). Metal-organic framework-derived materials for electrochemical energy applications. EnergyChem. https://doi.org/10.1016/j.enchem.2019.100001

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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.