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Responsive hydrogels – structurally and dimensionally optimized smart frameworks for applications in catalysis, micro-system technology and material science
Abstract
Although the technological and scientific importance of functional polymers has been well established over the last few decades, the most recent focus that has attracted much attention has been on stimuli-responsive polymers. This group of materials is of particular interest due to its ability to respond to internal and/or external chemico-physical stimuli, which is often manifested as large macroscopic responses. Aside from scientific challenges of designing stimuli-responsive polymers, the main technological interest lies in their numerous applications ranging from catalysis through microsystem technology and chemomechanical actuators to sensors that have been extensively explored. Since the phase transition phenomenon of hydrogels is theoretically well understood advanced materials based on the predictions can be prepared. Since the volume phase transition of hydrogels is a diffusion-limited process the size of the synthesized hydrogels is an important factor. Consistent downscaling of the gel size will result in fast smart gels with sufficient response times. In order to apply smart gels in microsystems and sensors, new preparation techniques for hydrogels have to be developed. For the up-coming nanotechnology, nano-sized gels as actuating materials would be of great interest.
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Metadata
- Authors
- Döring, A., Birnbaum, W., & Kuckling, D.
- Venue
- Chemical Society Reviews
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2013
- Volume
- 42
- Issue
- 17
- Pages
- 7391
- ISSN
- 0306-0012 (ISSN-L)
- 1460-4744
- OpenAlex
- W2094703435
- Cited by
- 391
- References
- 123
- FWCI
- 2.54
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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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.
