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Cited by 1,686

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Magnetically Recoverable Nanocatalysts

Polshettiwar, Vivek, Luque, Rafael, Fihri, Aziz, Zhu, Haibo, Bouhrara, Mohamed, Basset, Jean-Marie·2011·Chemical Reviews

Abstract

A broad overview on magnetically recoverable nanocatalysts is presented and the use of magnetic nanomaterials as catalysts is discussed. Magnetic materials are used as organocatalysts and their applications range to challenging reactions, such as hydroformylation and olefin metathesis. Magnetic nanomaterials are also being used in environmental applications, such as for photo- and biocatalysis and for the adsorption and removal of pollutants from air and water. These materials show great promise as enantioselective catalysts, which are used extensively for the synthesis of medicines, drugs, and other bioactive molecules. By functionalizing these materials using chiral ligands, a series of chiral nanocatalysts can be designed, offering great potential to reuse these otherwise expensive catalyst systems. Characterization of magnetic catalysts is often a challenging task, and NMR characterization of these catalysts is difficult because the magnetic nature of the materials interferes with the magnetic field of the spectrometer.

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Metadata

Authors
Polshettiwar, V., Luque, R., Fihri, A., Zhu, H., Bouhrara, M., & Basset, J.-M.
Venue
Chemical Reviews
Published
2011-03-14
Volume
111
Issue
5
Pages
3036-3075
ISSN
  • 0009-2665 (ISSN-L)
  • 1520-6890
OpenAlex
W2043210417
Cited by
1,686
References
1
FWCI
3.13
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Magnetically Recoverable Nanocatalysts. https://app.cochat.ai/papers/10.1021/cr100230z
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Polshettiwar, V., Luque, R., Fihri, A., Zhu, H., Bouhrara, M., & Basset, J.-M. (2011). Magnetically Recoverable Nanocatalysts. Chemical Reviews. https://doi.org/10.1021/cr100230z

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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.