TY - JOUR
T1 - Multifaceted design optimization for superomniphobic surfaces
AU - Panter, J. R.
AU - Gizaw, Y.
AU - Kusumaatmaja, H.
N1 - Funding Information:
We would like to thank C. Semprebon, I. Liu, and E. Xi for useful discussions and C. M. Jones for updating the energy minimization software. We thank P&G and EPSRC (EP/P007139/1) for funding.
Publisher Copyright:
Copyright © 2019 The Authors,
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068126293&partnerID=8YFLogxK
U2 - 10.1126/sciadv.aav7328
DO - 10.1126/sciadv.aav7328
M3 - Article
C2 - 31501770
AN - SCOPUS:85068126293
VL - 5
JO - Science Advances
JF - Science Advances
SN - 2375-2548
IS - 6
M1 - eaav7328
ER -