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Cited by 319

The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

Mussel‐Inspired Materials: Self‐Healing through Coordination Chemistry

Krogsgaard, Marie, Nue, Vicki, Birkedal, Henrik·2016·Chemistry – A European Journal

Abstract

Improved understanding of the underwater attachment strategy of the blue mussels and other marine organisms has inspired researchers to find new routes to advanced materials. Mussels use polyphenols, such as the catechol‐containing amino acid 3,4‐dihydroxyphenylalanine (DOPA), to attach to surfaces. Catechols and their analogues can undergo both oxidative covalent cross‐linking under alkaline conditions and take part in coordination chemistry. The former has resulted in the widespread use of polydopamine and related materials. The latter is emerging as a tool to make self‐healing materials due to the reversible nature of coordination bonds. We review how mussel‐inspired materials have been made with a focus on the less developed use of metal coordination and illustrate how this chemistry can be widely to make self‐healing materials.

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Metadata

Authors
Krogsgaard, M., Nue, V., & Birkedal, H.
Venue
Chemistry – A European Journal
Publisher
Wiley
Published
2015-11-12
Volume
22
Issue
3
Pages
844-857
ISSN
  • 0947-6539 (ISSN-L)
  • 1521-3765
OpenAlex
W2138526680
Cited by
319
References
16
FWCI
9.57
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Mussel‐Inspired Materials: Self‐Healing through Coordination Chemistry. https://app.cochat.ai/papers/10.1002/chem.201503380
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Krogsgaard, M., Nue, V., & Birkedal, H. (2016). Mussel‐Inspired Materials: Self‐Healing through Coordination Chemistry. Chemistry – A European Journal. https://doi.org/10.1002/chem.201503380

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.