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Cited by 3,823

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Green synthesis of metal nanoparticles using plants

Iravani, Siavash·2011·Green Chemistry

Abstract

In recent years, the development of efficient green chemistry methods for synthesis of metal nanoparticles has become a major focus of researchers. They have investigated in order to find an eco-friendly technique for production of well-characterized nanoparticles. One of the most considered methods is production of metal nanoparticles using organisms. Among these organisms plants seem to be the best candidates and they are suitable for large-scale biosynthesis of nanoparticles. Nanoparticles produced by plants are more stable and the rate of synthesis is faster than in the case of microorganisms. Moreover, the nanoparticles are more various in shape and size in comparison with those produced by other organisms. The advantages of using plant and plant-derived materials for biosynthesis of metal nanoparticles have interested researchers to investigate mechanisms of metal ions uptake and bioreduction by plants, and to understand the possible mechanism of metal nanoparticle formation in plants. In this review, most of the plants used in metal nanoparticle synthesis are shown.

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Metadata

Authors
Iravani, S.
Venue
Green Chemistry
Published
2011
Volume
13
Issue
10
Pages
2638
ISSN
  • 1463-9262 (ISSN-L)
  • 1463-9270
OpenAlex
W1995468808
Cited by
3,823
References
1
FWCI
29.59
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Green synthesis of metal nanoparticles using plants. https://app.cochat.ai/papers/10.1039/c1gc15386b
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Iravani, S. (2011). Green synthesis of metal nanoparticles using plants. Green Chemistry. https://doi.org/10.1039/c1gc15386b

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.