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Superhydrophobic Surfaces: Insights from Theory and Experiment
Open accessAbstract
Biomimetic nanosurfaces with distinct wettability and versatility have found special enthusiasm in both fundamental research and industrial applications. With the advent of nanotechnology, it is doable to acclimate surface architecture and surface chemistry to attain superhydrophobicity. The uniqueness of superhydrophobic surfaces arises from various phenomenal advances, and its progress is expected to continue for decades in the future. In this Review Article, we discuss recent progress made in defining physical aspects of numerical modeling, experimental practices adopted, and applications of superhydrophobic surfaces. First, we revisit various classical models of superhydrophobicity and recent theoretical advances achieved related to the wetting phenomena. Subsequently, we emphasize various precursors and advance fabrication strategies adopted to fabricate superhydrophobic surfaces. In the following section, we take up various potential applications and appropriate working principles to explain wettability phenomena. Finally, some general conclusions are drawn along with proposed guidelines for designing robust superhydrophobic coatings.
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Metadata
- Authors
- Parvate, S., Dixit, P., & Chattopadhyay, S.
- Venue
- The Journal of Physical Chemistry B
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-01-13
- Volume
- 124
- Issue
- 8
- Pages
- 1323-1360
- ISSN
- 1520-5207 (ISSN-L)
- 1520-6106
- OpenAlex
- W3000318384
- Cited by
- 787
- References
- 134
- FWCI
- 23.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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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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.