4.7 Article

Superhydrophobic hierarchical arrays fabricated by a scalable colloidal lithography approach

期刊

JOURNAL OF COLLOID AND INTERFACE SCIENCE
卷 487, 期 -, 页码 484-492

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcis.2016.10.081

关键词

Superhydrophobic; Dewetting; Colloidal lithography; Colloidal crystals; Hierarchical

资金

  1. US Defense Threat Reduction Agency [HDTRA1-15-1-0022]
  2. NASA [NNX14AB07G]
  3. US National Science Foundation (NSF) [CMMI-1300613]
  4. Div Of Civil, Mechanical, & Manufact Inn
  5. Directorate For Engineering [1300613] Funding Source: National Science Foundation
  6. NASA [686932, NNX14AB07G] Funding Source: Federal RePORTER

向作者/读者索取更多资源

Here we report an unconventional colloidal lithography approach for fabricating a variety of periodic polymer nanostructures with tunable geometries and hydrophobic properties. Wafer-sized, double layer, non-close-packed silica colloidal crystal embedded in a polymer matrix is first assembled by a scalable spin-coating technology. The unusual non-close-packed crystal structure combined with a thin polymer film separating the top and the bottom colloidal layers render great versatility in templating periodic nanostructures, including arrays of nanovoids, nanorings, and hierarchical nanovoids. These different geometries result in varied fractions of entrapped air in between the templated nanostructures, which in turn lead to different apparent water contact angles. Superhydrophobic surfaces with >150 degrees water contact angles and <5 degrees contact angle hysteresis are achieved on fluorosilane-modified polymer hierarchical nanovoid arrays with large fractions of entrapped air. The experimental contact angle measurements are complemented with theoretical predictions using the Cassie's model to gain insights into the fundamental microstructure-dewetting property relationships. The experimental and theoretical contact angles follow the same trends as determined by the unique hierarchical structures of the templated periodic arrays. (C) 2016 Elsevier Inc. All rights reserved.

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