Multifaceted design optimization for superomniphobic surfaces

J. R. Panter, Y. Gizaw, H. Kusumaatmaja

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbereaav7328
JournalScience Advances
Volume5
Issue number6
DOIs
Publication statusPublished - 2019

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