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Ion Transport by Nanochannels in Ion-Containing Aromatic Copolymers
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The search for the next generation of highly ion-conducting polymer electrolyte membranes has been a subject of intense research because of their potential applications in energy storage and transformation devices, such as fuel cells, vanadium flow batteries, membrane-based artificial photosynthesis, water electrolysis, or water treatment processes such as electrodialysis desalination. Nanochannels that contain ionic groups, through which “hydrated” ions can pass, are believed to be of key importance for efficient ion transport in polymer electrolytes membranes. In this Perspective, we present an overview of the approaches to induce ion-conducting nanochannel formation by self-assembly, using polymer architecture such as block or comb-shaped copolymers. The transport properties of ion-containing aromatic copolymers are examined to obtain an insight into the fundamental behavior of these materials, which are targeted toward applications in fuel cells and other electrochemical devices. Challenges in obtaining well-defined nanochannel morphologies, and possible strategies to improve transport properties in aromatic copolymers having structures with the potential to withstand operation in electrochemical/chemical devices, are discussed. Opportunities for the application of ion-containing aromatic copolymer membranes in fuel cells, vanadium flow batteries, membrane-based artificial photosynthesis, electrolysis, and electrodialysis are also reviewed. Research needs for further improvements in ionic conductivity and durability, and their applications are identified.
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Metadata
- Authors
- Li, N., & Guiver, M. D.
- Venue
- Macromolecules
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-02-18
- Volume
- 47
- Issue
- 7
- Pages
- 2175-2198
- ISSN
- 0024-9297 (ISSN-L)
- 1520-5835
- OpenAlex
- W2327877047
- Cited by
- 440
- References
- 137
- FWCI
- 26.88
- Type
- journal article
- Language
- en
- Open access
- green
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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.
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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.
