Multifaceted design optimization for superomniphobic surfaces
Panter, J.R.; Gizaw, Y; Kusumaatmaja, H.
Professor Halim Kusumaatmaja firstname.lastname@example.org
Superomniphobic textures are at the frontier of surface design for vast arrays of applications. Despite recent substantial advances in fabrication methods for reentrant and doubly reentrant microstructures, design optimization remains a major challenge. We overcome this in two stages. First, we develop readily generalizable computational methods to systematically survey three key wetting properties: contact angle hysteresis, critical pressure, and minimum energy wetting barrier. For each, we uncover multiple competing mechanisms, leading to the development of quantitative models and correction of inaccurate assumptions in prevailing models. Second, we combine these analyses simultaneously, demonstrating the power of this strategy by optimizing structures that are designed to overcome challenges in two emerging applications: membrane distillation and digital microfluidics. As the wetting properties are antagonistically coupled, this multifaceted approach is essential for optimal design. When large surveys are impractical, we show that genetic algorithms enable efficient optimization, offering speedups of up to 10,000 times.
Panter, J., Gizaw, Y., & Kusumaatmaja, H. (2019). Multifaceted design optimization for superomniphobic surfaces. Science Advances, 5(6), Article eaav7328. https://doi.org/10.1126/sciadv.aav7328
|Journal Article Type||Article|
|Acceptance Date||Apr 7, 2019|
|Online Publication Date||Jun 21, 2019|
|Publication Date||Jun 5, 2019|
|Deposit Date||Apr 10, 2019|
|Publicly Available Date||Jun 25, 2019|
|Publisher||American Association for the Advancement of Science|
|Peer Reviewed||Peer Reviewed|
Published Journal Article
Publisher Licence URL
Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).<br /> This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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