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Cited by 532

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Dynamics of Entangled Solutions of Associating Polymers

Rubinstein, Michael, Semenov, Alexander N.·2001·Macromolecules

Abstract

The process of making and breaking reversible bonds between associating groups (stickers) controls the dynamics of associating polymers. We develop a theory of “sticky reptation” to model the dynamics of entangled solutions of associating polymers with many stickers per chain. At a high degree of association, there are very few unassociated stickers. It is therefore very difficult for a sticker to find a new partner to associate with after breaking the bond with an old one. Typically a sticker returns to its old partner following an unsuccessful search for a new one, prolonging the effective lifetime of reversible bonds. In the sticky reptation model, the search for a new partner is restricted to a part of the tube confining the entangled chain. Another important effect is the increase of the fraction of the interchain associations at the expense of the intrachain ones with increasing polymer concentration. The sticky reptation model predicts a very strong concentration dependence of viscosity in good agreement with experiments.

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Metadata

Authors
Rubinstein, M., & Semenov, A. N.
Venue
Macromolecules
Published
2001-01-12
Volume
34
Issue
4
Pages
1058-1068
ISSN
  • 0024-9297 (ISSN-L)
  • 1520-5835
OpenAlex
W2014235439
Cited by
532
References
12
FWCI
9.94
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Dynamics of Entangled Solutions of Associating Polymers. https://app.cochat.ai/papers/10.1021/ma0013049
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Rubinstein, M., & Semenov, A. N. (2001). Dynamics of Entangled Solutions of Associating Polymers. Macromolecules. https://doi.org/10.1021/ma0013049

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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.