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

随着时间的推移，所有表面都会积累灰尘、污垢和其他污染物。受污染的表面对健康和技术性能都是有害的。例如，生物膜和生物有机体对医疗设备的污染造成了约45%的医院感染，太阳能电池板上积聚的粉尘使其效率降低了高达35%，粉尘浓度为20g/m2。尽管尽可能保持表面清洁很重要，但这通常需要大量的时间、能量、水和化学物质。鉴于对可持续产品和工艺的迫切需求，设计可以用最少的资源清洁的表面正成为一个越来越重要的技术目标。大自然为这一挑战提供了一些潜在的变革性解决方案。荷叶、猪笼草和鸭毛等自然表面已经进化出一种令人印象深刻的能力，可以去除固体和液体污染物。液滴(如雨水或露水)很容易从这些所谓的自洁表面上滚下来。当水滴滚落时，它们也会捕获和清除固体污染物。天然的自清洁表面激发了研究人员创造人造类似物，并将其用于广泛的应用，从防止医疗设备上的生物膜形成和太阳能电池板上的灰尘堆积，到实现汽车、航空航天和摄影工业的防冰和防雾性能。自动清洁的研究现在正处于十字路口。到目前为止，通过自清洁表面的液滴去除污染物的机理尚不清楚。详细的机械洞察将对指导这些功能表面的设计非常有价值，从而超越目前主导该领域的昂贵的反复试验方法。因此，我的目标是对两种最有前景的自清洁表面，即超疏水表面和注入液体的表面上的润湿和多相流体动力学有一个基本的了解。这两个表面都由粗糙的固体基板组成，主要的区别是在注入液体的表面上，基板上吸有润滑剂。最终，这个项目将使我们能够定量地预测污染物去除机制如何依赖于液滴、污染物和表面的性质，从而产生指导自清洁表面合理设计所需的关键知识。为了实现这一点，我将开发和利用最先进的计算格子Boltzmann方法和定制的实验装置。模拟和实验之间的协同作用对于提供仅用单一方法无法获得的补充见解至关重要。我在计算方法和实验方法方面的综合专业知识使我在实现这一目标方面处于独特的有利地位。为了推动技术突破，我将进一步组织一次沙坑会议，邀请学术和工业研究人员参与清洁过程的建模和开发可持续的清洁过程。

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.