4.7 Article

Silicon nanoparticles: Comprehensive review on biogenic synthesis and applications in agriculture

期刊

ENVIRONMENTAL RESEARCH
卷 232, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.envres.2023.116292

关键词

Nanotechnology; Silicon nanoparticles; Environmental stress; Biological synthesis; Machine learning algorithm

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Recent advancements in nanotechnology have provided new opportunities for agriculture, particularly with the use of silicon nanoparticles. These nanoparticles have shown potential as nanofertilizers, nanopesticides, and targeted delivery systems. They can improve plant growth and stress tolerance, but further research is needed to understand their interactions with different plants and potential toxic effects.
Recent advancements in nanotechnology have opened new advances in agriculture. Among other nanoparticles, silicon nanoparticles (SiNPs), due to their unique physiological characteristics and structural properties, offer a significant advantage as nanofertilizers, nanopesticides, nanozeolite and targeted delivery systems in agriculture. Silicon nanoparticles are well known to improve plant growth under normal and stressful environments. Nanosilicon has been reported to enhance plant stress tolerance against various environmental stress and is considered a non-toxic and proficient alternative to control plant diseases. However, a few studies depicted the phytotoxic effects of SiNPs on specific plants. Therefore, there is a need for comprehensive research, mainly on the interaction mechanism between NPs and host plants to unravel the hidden facts about silicon nanoparticles in agriculture. The present review illustrates the potential role of silicon nanoparticles in improving plant resistance to combat different environmental (abiotic and biotic) stresses and the underlying mechanisms involved.Furthermore, our review focuses on providing the overview of various methods exploited in the biogenic synthesis of silicon nanoparticles. However, certain limitations exist in synthesizing the well-characterized SiNPs on a laboratory scale. To bridge this gap, in the last section of the review, we discussed the possible use of the machine learning approach in future as an effective, less labour-intensive and time-consuming method for silicon nanoparticle synthesis. The existing research gaps from our perspective and future research directions for uti-lizing SiNPs in sustainable agriculture development have also been highlighted.

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