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Cited by 492

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Supramolecular Analytical Chemistry

Anslyn, Eric V.·2007·The Journal of Organic Chemistry

Abstract

A large fraction of the field of supramolecular chemistry has focused in previous decades upon the study and use of synthetic receptors as a means of mimicking natural receptors. Recently, the demand for synthetic receptors is rapidly increasing within the analytical sciences. These classes of receptors are finding uses in simple indicator chemistry, cellular imaging, and enantiomeric excess analysis, while also being involved in various truly practical assays of bodily fluids. Moreover, one of the most promising areas for the use of synthetic receptors is in the arena of differential sensing. Although many synthetic receptors have been shown to yield exquisite selectivities, in general, this class of receptor suffers from cross-reactivities. Yet, cross-reactivity is an attribute that is crucial to the success of differential sensing schemes. Therefore, both selective and nonselective synthetic receptors are finding uses in analytical applications. Hence, a field of chemistry that herein is entitled "Supramolecular Analytical Chemistry" is emerging, and is predicted to undergo increasingly rapid growth in the near future.

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Crossref · 10.1021/jo0617971

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Metadata

Authors
Anslyn, E. V.
Venue
The Journal of Organic Chemistry
Published
2007-01-12
Volume
72
Issue
3
Pages
687-699
ISSN
  • 0022-3263 (ISSN-L)
  • 1520-6904
OpenAlex
W2173353533
Cited by
492
References
2
FWCI
24.31
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Supramolecular Analytical Chemistry. https://app.cochat.ai/papers/10.1021/jo0617971
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Anslyn, E. V. (2007). Supramolecular Analytical Chemistry. The Journal of Organic Chemistry. https://doi.org/10.1021/jo0617971

Data sources

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.