Unravelling the Mechanisms of Self-Cleaning on Superhydrophobic and Liquid-Infused Surfaces

All surfaces accumulate dust, dirt, and other contaminants over time. Contaminated surfaces are detrimental to health and technological performance. For example, the contamination of medical equipment by biofilms and biological organisms is responsible for around 45% of hospital-contracted infections and the accumulation of dust on solar panels reduces their efficiency by up to 35% for 20 g/m2 of dust. While it is important to keep surfaces as clean as possible, this usually requires significant amounts of time, energy, water, and chemicals. Given the urgent need for sustainable products and processes, designing surfaces that can be cleaned with minimal resources is becoming an increasingly important technological goal.

Nature provides some potentially transformative solutions to this challenge. Natural surfaces such as lotus leaves, pitcher plants, and duck feathers have evolved an impressive ability to shed solid and liquid contaminants. Liquid drops (e.g. from rain or dew) easily roll off these so-called self-cleaning surfaces. As drops roll off, they also capture and remove solid contaminants. Natural self-cleaning surfaces have inspired researchers to create manmade equivalents and exploit them for a wide range of applications, from preventing biofilm formation on medical devices and dust build-up on solar panels to realising anti-icing and anti-fogging properties relevant for the automotive, aerospace, and photographic industries.

Research in self-cleaning is now at a crossroads. To date, the mechanism of contaminant removal by drops from self-cleaning surfaces remains unclear. Detailed mechanistic insights would be highly valuable to guide the design of these functional surfaces, thus surpassing costly trial-and-error approaches that currently dominate the field. Hence, my goal for this fellowship is to acquire a fundamental understanding of the wetting and multiphase fluid dynamics at play on two of the most promising types of self-cleaning surfaces, namely superhydrophobic surfaces and liquid-infused surfaces. Both these surfaces consist of a rough solid substrate, with the main difference being that on liquid-infused surfaces, the substrate is imbibed with a lubricant.

Ultimately, this project will enable us to predict quantitatively how the mechanism of contaminant removal depends on the properties of the drop, contaminant, and surface, thereby generating the key knowledge required to guide the rational design of self-cleaning surfaces. To deliver this, I will develop and harness a state-of-the-art computational lattice Boltzmann method and a bespoke experimental setup. The synergy between simulations and experiments is crucial to provide complementary insights that cannot be obtained using a single method alone. My combined expertise in both computational and experimental methods puts me in a uniquely strong position to realise this goal. To trigger technological breakthroughs, I will further organise a sandpit meeting to engage academic and industrial researchers involved in modelling cleaning processes and in developing sustainable cleaning processes.