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Zinc Oxide Nanoparticle as a Novel Class of Antifungal Agents: Current Advances and Future Perspectives
Abstract
Certain types of nanoparticles, especially zinc oxide nanoparticles (ZnONPs), are widely reported to be capable of the inhibition of harmful bacteria, yeasts, and filamentous fungi. The unique physicochemical and biological properties of ZnONPs also make them attractive to the food industry for use as a promising antifungal agent. This Review thoroughly introduces the preparation methods and antifungal properties of ZnONPs and analyzes their possible antifungal mechanisms. The applicability of ZnONPs in food packaging and nutritional supplements and as an antimicrobial additive is also documented. Moreover, evaluations for biological safety of ZnONPs are objectively reviewed in this paper. The discussions addressed in this Review not only have theoretical significance but also are conducive to the development of food safety, nutrition, and human health. The summarized knowledge and future perspectives outlined here are expected to promote and guide new research toward developing and optimizing the application of ZnONPs as a novel class of antifungal agents to help improve food quality as well as food safety in the near future.
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Metadata
- Authors
- Sun, Q., Li, J., & Le, T.
- Venue
- Journal of Agricultural and Food Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-10-09
- Volume
- 66
- Issue
- 43
- Pages
- 11209-11220
- ISSN
- 0021-8561 (ISSN-L)
- 1520-5118
- OpenAlex
- W2897419078
- Cited by
- 265
- References
- 134
- FWCI
- 6.51
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
