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Cited by 257

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

Hydrophobic association hydrogels with excellent mechanical and self-healing properties

Jiang, Haicheng, Duan, Lijie, Ren, Xiuyan, Gao, Guanghui·2019·European Polymer Journal

Abstract

Abstract Hydrophobic association hydrogels have attracted great attention of many researchers and been widely utilized in tissue engineering stents, drug delivery carriers, wound dressings, and biosensors as intelligent materials due to their excellent mechanical and self-healing properties. Here, we mainly introduced different mechanisms on hydrophobic association hydrogels with high mechanical and excellent self-healing properties. The mechanisms included not only hydrophobic association, but also synergistic effect with latex particles, nanoparticles, electrostatic effect, metal complex, hybrid crosslinking and double network. Based on these achievements, moreover, we forecast the hydrophobic association interaction would exhibit more meaningful influence on assisting the performance of hydrogels for wide applications of biomedical, industrial, agricultural fields.

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Crossref · 10.1016/j.eurpolymj.2018.10.031

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Metadata

Authors
Jiang, H., Duan, L., Ren, X., & Gao, G.
Venue
European Polymer Journal
Publisher
Elsevier BV
Published
2019-03
Volume
112
Pages
660-669
ISSN
0014-3057 (ISSN-L)
OpenAlex
W2898544714
Cited by
257
References
78
FWCI
4.59
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Hydrophobic association hydrogels with excellent mechanical and self-healing properties. https://app.cochat.ai/papers/10.1016/j.eurpolymj.2018.10.031
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Jiang, H., Duan, L., Ren, X., & Gao, G. (2019). Hydrophobic association hydrogels with excellent mechanical and self-healing properties. European Polymer Journal. https://doi.org/10.1016/j.eurpolymj.2018.10.031

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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.