4.5 Article

Recent developments in green magnetic nanoparticles for extraction and preconcentration of pollutants from environmental samples

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DOI: 10.1016/j.teac.2023.e00211

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Green Magnetic nanoparticles; Preconcentration; Pollutants; Green synthesis

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Magnetic nanoparticles (MNPs) play a significant role in various sectors, including analytical chemistry, due to their unique properties. They have outstanding performance in extracting and enriching target analytes, making them suitable for a wide range of applications. However, efforts are needed to reduce the toxicity of MNPs and explore green production methods. This review focuses on studies that discuss strategies for synthesizing green MNPs and presents their recent applications in environmental contaminant analysis. Potential challenges and future perspectives are also addressed.
Magnetic nanoparticles (MNPs) have recently emerged as significant materials in the development of a variety of sectors, including analytical chemistry, by virtue of their unique properties making them appropriate for a wide range of applications. They have exceptional performance in extracting and enriching a wide range of target analytes such as trace pollutants due to their superparamagnetic properties, ease of separation, and surface modification as well as selective adsorption capacity. However, the toxicity of such materials has urged efforts to search for green production ways so that assuring reduced toxicity levels and permitting unlimited applications. Current research and analysis on biosynthesized green MNPs for the identification and quantification of environmental contaminants are widespread. Consequently, this review article focuses on several studies which outline novel strategies for synthesizing MNPs from green sources, as well as the future direction of research in this field. The recent applications of green MNPs (from 2016 to June 2023) in the separation and preconcentration of various pollutants including both organic and inorganic ones in different environmental matrices are demonstrated. Potential challenges and future perspectives are also highlighted. This review can serve as a roadmap and inspire further research in this area.

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