Nanotechnology in pesticide degradation: mechanisms, applications, and environmental implications
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Angel Rose
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Simran Rana
Abstract
Increased use of pesticides has proven to be a serious threat despite their benefits, due to their prolonged persistence in the environment that poses serious toxic effects on non-target species as well as disrupts the ecosystems by contaminating soil and water. Current review covers the role of different metal oxide, chalcogenide, nitride and other novel nanomaterials in the degradation of pesticides. Contribution of nanotechnology has been emphasized by including several degradation mechanisms such as charge transfer, adsorption and role of surface defects or states. Recent advancement in the field has been focused by incorporating the examples of novel and 0D, 1D and 2D functional nanomaterials such as graphene, carbon nanotubes, carbon quantum dots, MoS2 and their composites. Nanotechnology driven enhancement of selectivity and reactivity for pesticide breakdown, by surface modification and bandgap engineering has been included. As, in the current era the detection of trace pesticide residues becomes crucial, this review further covers the progress of nanotechnology-based sensors’ implementations in pesticide residue detection and environmental monitoring. Environmental impact and safety of nanotechnology in pesticides degradation has been highlighted by covering the aspects like potential risks and toxicity of nanomaterials. The article concludes with the important points like regulatory consideration and safety guidelines of using nanomaterials for environmental applications such as pesticide degradation.
Acknowledgments
We would like to thank Multidisciplinary Research & Innovation Center, NFSU Goa.
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Informed consent: Not applicable.
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Research ethics: This study does not involve any human participants, animals, or biological samples requiring ethical approval.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Research funding: None declared.
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Conflict of interest: The authors declare that there is no conflict of interest regarding the publication of this article.
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Data availability: All data supporting the findings of this study are available within the article.
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Compliance with Legal and Ethical Standards: The authors affirm that the manuscript complies with all ethical and legal standards of the journal. Proper attribution and citation have been provided for all referenced work.
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