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Antibacterial Properties of Graphene-Based Nanomaterials
Open accessAbstract
Bacteria mediated infections may cause various acute or chronic illnesses and antibiotic resistance in pathogenic bacteria has become a serious health problem around the world due to their excessive use or misuse. Replacement of existing antibacterial agents with a novel and efficient alternative is the immediate demand to alleviate this problem. Graphene-based materials have been exquisitely studied because of their remarkable bactericidal activity on a wide range of bacteria. Graphene-based materials provide advantages of easy preparation, renewable, unique catalytic properties, and exceptional physical properties such as a large specific surface area and mechanical strength. However, several queries related to the mechanism of action, significance of size and composition toward bacterial activity, toxicity criteria, and other issues are needed to be addressed. This review summarizes the recent efforts that have been made so far toward the development of graphene-based antibacterial materials to face current challenges to combat against the bacterial targets. This review describes the inherent antibacterial activity of graphene-family and recent advances that have been made on graphene-based antibacterial materials covering the functionalization with silver nanoparticles, other metal ions/oxides nanoparticles, polymers, antibiotics, and enzymes along with their multicomponent functionalization. Furthermore, the review describes the biosafety of the graphene-based antibacterial materials. It is hoped that this review will provide valuable current insight and excite new ideas for the further development of safe and efficient graphene-based antibacterial materials.
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Cited by
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Metadata
- Authors
- Kumar, P., Huo, P., Zhang, R., & Liu, B.
- Venue
- Nanomaterials
- Publisher
- MDPI AG
- Published
- 2019-05-13
- Volume
- 9
- Issue
- 5
- Pages
- 737
- ISSN
- 2079-4991 (ISSN-L)
- OpenAlex
- W2944890626
- Cited by
- 512
- References
- 178
- FWCI
- 17.99
- Type
- journal article
- Language
- en
- Open access
- gold
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
