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Well-Defined Organic Nanotubes from Multicomponent Bottlebrush Copolymers

Huang, Kun, Rzayev, Javid·2009·Journal of the American Chemical Society

Abstract

Bottlebrush copolymers are comblike macromolecules with densely grafted polymeric branches that adopt a cylindrical shape in solutions. We demonstrate a new method for the preparation of organic nanotubes by single molecule templating of core-shell bottlebrush copolymers. Multicomponent bottlebrush copolymers with well-defined structural parameters are synthesized by a combination of different living polymerization methods. Tubular structures can be prepared by cross-linking the shell layer and selectively etching out the core. The shape and size of original bottlebrush macromolecules are preserved during these transformations, which leads to the formation of well-defined organic nanotubes. The length and diameter of nanotubes are dictated by the length of the backbones and branches of the polymeric precursors, respectively. Water-soluble nanotubes with a hydrophobic interior can be prepared from bottlebrush copolymers with triblock copolymer branches. Herein, we outline molecular design strategies to fabricate nanotubes with controlled lengths, open pores, and different solubility characteristics.

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Metadata

Authors
Huang, K., & Rzayev, J.
Venue
Journal of the American Chemical Society
Published
2009-04-27
Volume
131
Issue
19
Pages
6880-6885
ISSN
  • 0002-7863 (ISSN-L)
  • 1520-5126
OpenAlex
W2004853951
Cited by
227
References
0
FWCI
9.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Well-Defined Organic Nanotubes from Multicomponent Bottlebrush Copolymers. https://app.cochat.ai/papers/10.1021/ja901936g
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Huang, K., & Rzayev, J. (2009). Well-Defined Organic Nanotubes from Multicomponent Bottlebrush Copolymers. Journal of the American Chemical Society. https://doi.org/10.1021/ja901936g

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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.