10.15128/R1M039K492R
Panter, Jack
University of Durham, UK
Kusumaatmaja, Halim
University of Durham, UK
Multifaceted design optimisation for superomniphobic surfaces [dataset]
Durham University
2019
Durham University
Durham University
Engineering and Physical Sciences Research Council
Procter & Gamble
Panter, Jack
University of Durham, UK
Kusumaatmaja, Halim
University of Durham, UK
Gizaw, Yonas
P&G, USA
Physics
superomniphobic
contact angle hysteresis
critical pressure
wetting barrier
simultaneous optimisation
2019-04-23
Dataset
57122
application/zip
Creative Commons Attribution 4.0 International (CC BY)
ark:/32150/r1m039k492r
10.1126/sciadv.aav7328
This dataset contains the simulated data for the contact angle hysteresis (CAH), critical pressures, critical heights, transition state energies, and genetic algorithm populations presented in the results and discussions of the main text and supplementary material of: Panter, J. R., Gizaw, Y., and Kusumaatmaja, H., Multifaceted design optimisation for superomniphobic surfaces, Science Advances, 2019.
Abstract for Panter, J. R., Gizaw, Y., and Kusumaatmaja, H., Multifaceted design optimisation for superomniphobic surfaces, Science Advances, 2019: Superomniphobic textures are at the frontier of surface design for vast arrays of applications. Despite recent significant advances in fabrication methods for reentrant and doubly reentrant microstructures, design optimisation remains a major challenge. We overcome this in two stages. Firstly, we develop readily-generalisable 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 new quantitative models, and correction of inaccurate assumptions in prevailing models. Secondly, we combine these analyses simultaneously, demonstrating the power of this strategy by optimizing structures that are well-suited to overcome challenges faced by 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 optimisation, offering speedups of up to 10,000×.