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Cited by 300

The DOI matches its Crossref record; 160 of the 160 references it cites were found in a registry or an index.

Hydrogel Preparation Methods and Biomaterials for Wound Dressing

Open access
Su, Jingjing, Li, Jiankang, Liang, Jiaheng, Zhang, Kun, Li, Jingan·2021·Life

Abstract

Wounds have become one of the causes of death worldwide. The metabolic disorder of the wound microenvironment can lead to a series of serious symptoms, especially chronic wounds that bring great pain to patients, and there is currently no effective and widely used wound dressing. Therefore, it is important to develop new multifunctional wound dressings. Hydrogel is an ideal dressing candidate because of its 3D structure, good permeability, excellent biocompatibility, and ability to provide a moist environment for wound repair, which overcomes the shortcomings of traditional dressings. This article first briefly introduces the skin wound healing process, then the preparation methods of hydrogel dressings and the characteristics of hydrogel wound dressings made of natural biomaterials and synthetic materials are introduced. Finally, the development prospects and challenges of hydrogel wound dressings are discussed.

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Metadata

Authors
Su, J., Li, J., Liang, J., Zhang, K., & Li, J.
Venue
Life
Publisher
MDPI AG
Published
2021-09-27
Volume
11
Issue
10
Pages
1016
ISSN
2075-1729 (ISSN-L)
OpenAlex
W3202096439
Cited by
300
References
160
FWCI
18.62
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Hydrogel Preparation Methods and Biomaterials for Wound Dressing. https://app.cochat.ai/papers/10.3390/life11101016
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Su, J., Li, J., Liang, J., Zhang, K., & Li, J. (2021). Hydrogel Preparation Methods and Biomaterials for Wound Dressing. Life. https://doi.org/10.3390/life11101016

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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.