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

揭示超疏水和液体注入表面的自清洁机制

• 批准号: EP/X028410/1
• 负责人: Abhinav Naga
• 金额: $ 44.11万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX028410%2F1
• 关键词: Unravelling; Mechanisms; Self; Cleaning; Superhydrophobic

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.

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有害。例如，生物膜和生物有机体对医疗设备的污染是造成约45%的医院感染的原因，而太阳能电池板上灰尘的积累会使其效率降低高达35%（20 g/m2的灰尘）。虽然保持表面尽可能清洁很重要，但这通常需要大量的时间，能源，水和化学品。鉴于对可持续产品和工艺的迫切需求，设计可以用最少资源清洁的表面正成为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、猪笼草和鸭子羽毛等自然表面已经进化出了令人印象深刻的能力，可以去除固体和液体污染物。液滴（例如雨水或露水）很容易从这些所谓的自清洁表面上滚落。当水滴滚落时，它们也会捕获和去除固体污染物。天然的自清洁表面激发了研究人员创造人造的同类产品，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，到实现与汽车，航空航天和摄影行业相关的防冰和防雾特性。自清洁研究现在正处于十字路口。迄今为止，自清洁表面上的液滴去除污染物的机制仍不清楚。详细的机械见解将是非常有价值的指导这些功能表面的设计，从而超越昂贵的试错法，目前占主导地位的领域。因此，我这次奖学金的目标是获得润湿和多相流体动力学在两个最有前途的类型的自清洁表面，即超疏水表面和液体注入表面上发挥的基本理解。这两种表面都由粗糙的固体基底组成，主要区别是在液体注入表面上，基底吸收有润滑剂。最终，该项目将使我们能够定量预测污染物去除机制如何取决于液滴，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玻尔兹曼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单独使用单一方法无法获得的互补见解至关重要。我在计算和实验方法方面的综合专业知识使我处于实现这一目标的独特有利地位。为了引发技术突破，我将进一步组织一次沙坑会议，邀请参与清洁过程建模和开发可持续清洁过程的学术和工业研究人员参与。