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Liquid-Infused Surfaces: A Review of Theory, Design, and Applications
Abstract
plant, a frontier of devices has emerged with unmatched capabilities. Liquid-infused surfaces (LISs), particularly known for their liquid-repelling behavior under low tilting angles (<5°), have demonstrated a plethora of applications in medical, marine, energy, industrial, and environmental materials. This review presents recent developments of LIS technology and its prospective to define the future direction of this technology in solving tomorrow's real-life challenges. First, an introduction to the different models explaining the physical phenomena of these surfaces, their wettability, and viscous-dependent frictional forces is discussed. Then, an outline of different emerging strategies required to fabricate a stable liquid-infused interface is presented, including different substrates, lubricants, surface chemistries, and design parameters which can be tuned depending on the application. Furthermore, applications of LIS coatings in the areas of anticorrosion, antifouling, anti-icing, self-healing, droplet manipulation, and biomedical devices will be presented followed by the limitations and future direction of this technology.
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Heydarian, S., Jafari, R., & Momen, G. (2021) · Progress in Organic Coatings · doi:10.1016/j.porgcoat.2020.106096 · Cited by 117
Metadata
- Authors
- Villegas, M., Zhang, Y., Abu Jarad, N., Soleymani, L., & Didar, T. F.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-08-02
- Volume
- 13
- Issue
- 8
- Pages
- 8517-8536
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W2964524264
- Cited by
- 459
- References
- 106
- FWCI
- 15.25
- 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.
