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Cited by 418

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Nano‐adsorbents for the removal of metallic pollutants from water and wastewater

Sharma, Y.C., Srivastava, V., Singh, V.K., Kaul, S.N., Weng, C.H.·2009·Environmental Technology

Abstract

Of the variety of adsorbents available for the removal of heavy and toxic metals, activated carbon has been the most popular. A number of minerals, clays and waste materials have been regularly used for the removal of metallic pollutants from water and industrial effluents. Recently there has been emphasis on the application of nanoparticles and nanostructured materials as efficient and viable alternatives to activated carbon. Carbon nanotubes also have been proved effective alternatives for the removal of metallic pollutants from aqueous solutions. Because of their importance from an environmental viewpoint, special emphasis has been given to the removal of the metals Cr, Cd, Hg, Zn, As, and Cu. Separation of the used nanoparticles from aqueous solutions and the health aspects of the separated nanoparticles have also been discussed. A significant number of the latest articles have been critically scanned for the present review to give a vivid picture of these exotic materials for water remediation.

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Metadata

Authors
Sharma, Y. C., Srivastava, V., Singh, V. K., Kaul, S. N., & Weng, C. H.
Venue
Environmental Technology
Published
2009-05
Volume
30
Issue
6
Pages
583-609
ISSN
  • 0959-3330 (ISSN-L)
  • 1479-487X
OpenAlex
W2054601385
Cited by
418
References
4
FWCI
9.50
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nano‐adsorbents for the removal of metallic pollutants from water and wastewater. https://app.cochat.ai/papers/10.1080/09593330902838080
The paper
Sharma, Y. C., Srivastava, V., Singh, V. K., Kaul, S. N., & Weng, C. H. (2009). Nano‐adsorbents for the removal of metallic pollutants from water and wastewater. Environmental Technology. https://doi.org/10.1080/09593330902838080

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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.