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Cited by 199

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Reversible Molecular Adsorption Based on Multiple-Point Interaction by Shrinkable Gels

Oya, Taro, Enoki, Takashi, Grosberg, Alexander Yu., Masamune, Satoru, Sakiyama, Takaharu, Takeoka, Yukikazu, Tanaka, Kazunori, Wang, Guoqiang, Yilmaz, Yasar, Feld, Michael S., Dasari, Ramachandra, Tanaka, Toyoichi·1999·Science

Abstract

A general approach is presented for creating polymer gels that can recognize and capture a target molecule by multiple-point interaction and that can reversibly change their affinity to the target by more than one order of magnitude. The polymers consist of majority monomers that make the gel reversibly swell and shrink and minority monomers that constitute multiple-point adsorption centers for the target molecule. Multiple-point interaction is experimentally proven by power laws found between the affinity and the concentration of the adsorbing monomers within the gels.

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Metadata

Authors
Oya, T., Enoki, T., Grosberg, A. Y., Masamune, S., Sakiyama, T., Takeoka, Y., Tanaka, K., Wang, G., Yilmaz, Y., Feld, M. S., Dasari, R., & Tanaka, T.
Venue
Science
Published
1999-11-19
Volume
286
Issue
5444
Pages
1543-1545
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2122086796
Cited by
199
References
0
FWCI
4.40
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Reversible Molecular Adsorption Based on Multiple-Point Interaction by Shrinkable Gels. https://app.cochat.ai/papers/10.1126/science.286.5444.1543
The paper
Oya, T., Enoki, T., Grosberg, A. Y., Masamune, S., Sakiyama, T., Takeoka, Y., Tanaka, K., Wang, G., Yilmaz, Y., Feld, M. S., Dasari, R., & Tanaka, T. (1999). Reversible Molecular Adsorption Based on Multiple-Point Interaction by Shrinkable Gels. Science. https://doi.org/10.1126/science.286.5444.1543

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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.