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Cited by 255

The DOI matches its Crossref record; 190 of the 190 references it cites were found in a registry or an index.

Molecular recognition of organic ammonium ions in solution using synthetic receptors

Open access
Späth, Andreas, König, Burkhard·2010·Beilstein Journal of Organic Chemistry

Abstract

Ammonium ions are ubiquitous in chemistry and molecular biology. Considerable efforts have been undertaken to develop synthetic receptors for their selective molecular recognition. The type of host compounds for organic ammonium ion binding span a wide range from crown ethers to calixarenes to metal complexes. Typical intermolecular interactions are hydrogen bonds, electrostatic and cation–π interactions, hydrophobic interactions or reversible covalent bond formation. In this review we discuss the different classes of synthetic receptors for organic ammonium ion recognition and illustrate the scope and limitations of each class with selected examples from the recent literature. The molecular recognition of ammonium ions in amino acids is included and the enantioselective binding of chiral ammonium ions by synthetic receptors is also covered. In our conclusion we compare the strengths and weaknesses of the different types of ammonium ion receptors which may help to select the best approach for specific applications.

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Metadata

Authors
Späth, A., & König, B.
Venue
Beilstein Journal of Organic Chemistry
Published
2010-04-06
Volume
6
Pages
32-32
ISSN
1860-5397 (ISSN-L)
OpenAlex
W2112675722
Cited by
255
References
190
FWCI
6.47
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Molecular recognition of organic ammonium ions in solution using synthetic receptors. https://app.cochat.ai/papers/10.3762/bjoc.6.32
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Späth, A., & König, B. (2010). Molecular recognition of organic ammonium ions in solution using synthetic receptors. Beilstein Journal of Organic Chemistry. https://doi.org/10.3762/bjoc.6.32

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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.