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Cited by 185

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

Nanosheets as highly active solid acid catalysts for green chemical syntheses

Takagaki, Atsushi, Tagusagawa, Caio, Hayashi, Shigenobu, Hara, Michikazu, Domen, Kazunari·2010·Energy Environ. Sci.

Abstract

Solid acid catalysts offer the opportunity to reduce environmental impact owing to such advantages as ease of product separation and recyclability of the catalyst, which contribute considerably to green chemistry. Nanosheets, crystalline two-dimensional metal oxide sheets prepared from cation-exchangeable layered metal oxides through exfoliation and aggregation, are a novel class of potential solid acid catalysts for replacing liquid acids, such as sulfuric acid. This article reviews the acid strength and acid catalysis of several types of nanosheets, which are strongly dependent on novel strong Brønsted acid sites attributed to bridged OH groups formed only on nanosheets. An efficient acid catalysis of layered protonated niobium molybdate in Friedel–Crafts alkylation, esterification and hydrolysis, owing to its unique intercalation availability, is also discussed.

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Metadata

Authors
Takagaki, A., Tagusagawa, C., Hayashi, S., Hara, M., & Domen, K.
Venue
Energy Environ. Sci.
Published
2010
Volume
3
Issue
1
Pages
82-93
ISSN
  • 1754-5692 (ISSN-L)
  • 1754-5706
OpenAlex
W2085470510
Cited by
185
References
4
FWCI
5.26
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nanosheets as highly active solid acid catalysts for green chemical syntheses. https://app.cochat.ai/papers/10.1039/b918563a
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Takagaki, A., Tagusagawa, C., Hayashi, S., Hara, M., & Domen, K. (2010). Nanosheets as highly active solid acid catalysts for green chemical syntheses. Energy Environ. Sci. https://doi.org/10.1039/b918563a

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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.