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Cited by 484

The DOI matches its Crossref record; 164 of the 164 references it cites were found in a registry or an index.

Metal-organic framework-based materials as an emerging platform for advanced electrochemical sensing

Liu, Chun-Sen, Li, Jingjing, Pang, Huan·2020·Coordination Chemistry Reviews

Abstract

Abstract Metal-organic frameworks (MOFs) are surprisingly porous materials that have attracted enormous attention in recent years owing to their fascinating structures and widespread potential applications. In this review, the recent developments of MOF-based materials for advanced electrochemical sensing applications are systematically summarized. A detailed discussion about the structure-property relationship, and the different strategies used for the rational design and controllable synthesis of versatile MOF-based materials based on the specific requirements of the final electrochemical sensing applications are outlined. The challenges and opportunities of developing MOF-based materials as novel electrochemical sensing platform are also discussed. Hopefully, this review will provide some creative inspiration to advance future studies on MOF-based materials in this emerging field.

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Metadata

Authors
Liu, C.-S., Li, J., & Pang, H.
Venue
Coordination Chemistry Reviews
Publisher
Elsevier BV
Published
2020-05
Volume
410
Pages
213222
ISSN
0010-8545 (ISSN-L)
OpenAlex
W3007591143
Cited by
484
References
164
FWCI
48.04
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Metal-organic framework-based materials as an emerging platform for advanced electrochemical sensing. https://app.cochat.ai/papers/10.1016/j.ccr.2020.213222
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Liu, C.-S., Li, J., & Pang, H. (2020). Metal-organic framework-based materials as an emerging platform for advanced electrochemical sensing. Coordination Chemistry Reviews. https://doi.org/10.1016/j.ccr.2020.213222

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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.