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Slippage of Water Past Superhydrophobic Carbon Nanotube Forests in Microchannels

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Joseph, P., Cottin-Bizonne, C., Benoît, J.-M., Ybert, C., Journet, C., Tabeling, P., Bocquet, L.·2006·Physical Review Letters

Abstract

We present in this Letter an experimental characterization of liquid flow slippage over superhydrophobic surfaces made of carbon nanotube forests, incorporated in microchannels. We make use of a particle image velocimetry technique to achieve the submicrometric resolution on the flow profile necessary for accurate measurement of the surface hydrodynamic properties. We demonstrate boundary slippage on the Cassie superhydrophobic state, associated with slip lengths of a few microns, while a vanishing slip length is found in the Wenzel state when the liquid impregnates the surface. Varying the lateral roughness scale L of our carbon nanotube forest-based superhydrophobic surfaces, we demonstrate that the slip length varies linearly with L in line with theoretical predictions for slippage on patterned surfaces.

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Metadata

Authors
Joseph, P., Cottin-Bizonne, C., Benoît, J.-M., Ybert, C., Journet, C., Tabeling, P., & Bocquet, L.
Venue
Physical Review Letters
Published
2006-10-10
Volume
97
Issue
15
Pages
156104-156104
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
OpenAlex
W2079464417
Cited by
474
References
0
FWCI
17.56
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Slippage of Water Past Superhydrophobic Carbon Nanotube Forests in Microchannels. https://app.cochat.ai/papers/10.1103/physrevlett.97.156104
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Joseph, P., Cottin-Bizonne, C., Benoît, J.-M., Ybert, C., Journet, C., Tabeling, P., & Bocquet, L. (2006). Slippage of Water Past Superhydrophobic Carbon Nanotube Forests in Microchannels. Physical Review Letters. https://doi.org/10.1103/physrevlett.97.156104

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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.