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Long-lived superhydrophobic surfaces
Abstract
Prolonging the lifetime of superhydrophobic surfaces is required so that the materials can be used practically. Thus, great efforts have been made in designing surfaces that maintain micro- and nanoscaled hierarchical structures and low surface-energy property, which are necessary for superhydrophobicity, during use. It was demonstrated that improving surface mechanical strength to increase wear resistance helps maintain hierarchical roughness, retarding the loss of superhydrophobicity. Additionally, designing self-healing materials that can recover their structure and/or properties when damaged has been suggested and demonstrated to sustain the superhydrophobicity of surfaces. This review focuses on recent advances in developing mechanically durable, corrosion-resistant, self-healing, and easily repairable superhydrophobic surfaces, which will enable prolonged lifetime of superhydrophobicity for practical applications in the future.
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Metadata
- Authors
- Xue, C.-H., & Ma, J.-Z.
- Venue
- Journal of Materials Chemistry A
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2013
- Volume
- 1
- Issue
- 13
- Pages
- 4146
- ISSN
- 2050-7488 (ISSN-L)
- 2050-7496
- OpenAlex
- W2139463695
- Cited by
- 316
- References
- 112
- FWCI
- 8.20
- 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.