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Surface-Initiated Polymerization as an Enabling Tool for Multifunctional (Nano-)Engineered Hybrid Materials
Abstract
Surface-initiated atom transfer radical polymerization (SI-ATRP) has become an indispensable tool for engineering the structure and properties of polymer/inorganic and polymer/organic interfaces. This article describes the progress and challenges that are associated with the application of SI-ATRP to precisely control the molecular characteristics of polymer chains tethered to nanoparticle surfaces and explores the properties and potential applications of the resulting particle brush materials. Even for the conceptually most “simple” particle brush systems—that is, spherical particles uniformly grafted with amorphous nonpolar polymers—the complex superposition of interactions as well as time- and length-scales related to particle core and tethered chains provides a rich and largely unexplored parameter space for the design of novel functional materials. The application of the particle brush approach to the development of materials for applications ranging from photonic inks and paints to advanced high “ k ” dielectrics for energy storage and advanced nanocomposite materials with improved optical, mechanical, or transport characteristics is discussed.
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Metadata
- Authors
- Hui, C. M., Pietrasik, J., Schmitt, M., Mahoney, C., Choi, J., Bockstaller, M. R., & Matyjaszewski, K.
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-08-28
- Volume
- 26
- Issue
- 1
- Pages
- 745-762
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W2327824874
- Cited by
- 367
- References
- 143
- FWCI
- 19.28
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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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.
