Abstract

AbstractIn materials science, “green” synthesis has gained extensive attention as a reliable, sustainable, and eco-friendly protocol for synthesizing a wide range of materials/nanomaterials including metal/metal oxides nanomaterials, hybrid materials, and bioinspired materials. As such, green synthesis is regarded as an important tool to reduce the destructive effects associated with the traditional methods of synthesis for nanoparticles commonly utilized in laboratory and industry. In this review, we summarized the fundamental processes and mechanisms of “green” synthesis approaches, especially for metal and metal oxide [e.g., gold (Au), silver (Ag), copper oxide (CuO), and zinc oxide (ZnO)] nanoparticles using natural extracts. Importantly, we explored the role of biological components, essential phytochemicals (e.g., flavonoids, alkaloids, terpenoids, amides, and aldehydes) as reducing agents and solvent systems. The stability/toxicity of nanoparticles and the associated surface engineering techniques for achieving biocompatibility are also discussed. Finally, we covered applications of such synthesized products to environmental remediation in terms of antimicrobial activity, catalytic activity, removal of pollutants dyes, and heavy metal ion sensing.

Keywords

Environmental remediationNanotechnologyNanoparticleOxideChemistryEnvironmental chemistryMaterials scienceEcologyBiologyContaminationOrganic chemistry

MeSH Terms

Environmental Restoration and RemediationGreen Chemistry TechnologyMetal NanoparticlesOxidesSolvents

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Publication Info

Year
2018
Type
review
Volume
16
Issue
1
Pages
84-84
Citations
2358
Access
Closed

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Citation Metrics

2358
OpenAlex
38
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Cite This

Jagpreet Singh, Tanushree Dutta, Ki‐Hyun Kim et al. (2018). ‘Green’ synthesis of metals and their oxide nanoparticles: applications for environmental remediation. Journal of Nanobiotechnology , 16 (1) , 84-84. https://doi.org/10.1186/s12951-018-0408-4

Identifiers

DOI
10.1186/s12951-018-0408-4
PMID
30373622
PMCID
PMC6206834

Data Quality

Data completeness: 90%