# Multifaceted design optimisation for superomniphobic surfaces

## TL;DR

This paper develops computational methods to optimize superomniphobic surfaces by systematically analyzing wetting properties, leading to improved designs for applications like membrane distillation and microfluidics.

## Contribution

It introduces generalizable computational techniques for multi-property surface design, correcting assumptions and enabling efficient optimization with genetic algorithms.

## Key findings

- New models for contact angle hysteresis, critical pressure, and energy barriers.
- Optimized structures for membrane distillation and microfluidics.
- Genetic algorithms achieve up to 10,000x speedup in design optimization.

## Abstract

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 10,000x.