Jafari Gukeh Mohamad, Moitra Shashwata, Ibrahim Ali Noaman, Derrible Sybil, Megaridis Constantine M
Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States.
Mechanical Engineering College, University of Babylon, Hilla 51001, Iraq.
ACS Appl Mater Interfaces. 2021 Sep 29;13(38):46171-46179. doi: 10.1021/acsami.1c13262. Epub 2021 Sep 15.
Surfaces with extreme wettability (too low, superhydrophobic; too high, superhydrophilic) have attracted considerable attention over the past two decades. Titanium dioxide (TiO) has been one of the most popular components for generating superhydrophobic/hydrophilic coatings. Combining TiO with ethanol and a commercial fluoroacrylic copolymer dispersion, known as PMC, can produce coatings with water contact angles approaching 170°. Another property of interest for this specific TiO formulation is its photocatalytic behavior, which causes the contact angle of water to be gradually reduced with rising timed exposure to UV light. While this formulation has been employed in many studies, there exists no quantitative guidance to determine or tune the contact angle (and thus wettability) with the composition of the coating and UV exposure time. In this article, machine learning models are employed to predict the required UV exposure time for any specified TiO/PMC coating composition to attain a certain wettability (UV-reduced contact angle). For that purpose, eight different coating compositions were applied to glass slides and exposed to UV light for different time intervals. The collected contact-angle data was supplied to different regression models to designate the best method to predict the required UV exposure time for a prespecified wettability. Two types of machine learning models were used: (1) parametric and (2) nonparametric. The results showed a nonlinear behavior between the coating formulation and its contact angle attained after timed UV exposure. Nonparametric methods showed high accuracy and stability with general regression neural network (GRNN) performing best with an accuracy of 0.971, 0.977, and 0.933 on the test, train, and unseen data set, respectively. The present study not only provides quantitative guidance for producing coatings of specified wettability, but also presents a generalized methodology that could be employed for other functional coatings in technological applications requiring precise fluid/surface interactions.
在过去二十年中，具有极端润湿性（过低，超疏水；过高，超亲水）的表面引起了相当大的关注。二氧化钛（TiO₂）一直是制备超疏水/亲水涂层最常用的成分之一。将TiO₂与乙醇和一种名为PMC的商业氟丙烯酸共聚物分散体相结合，可以制备出水接触角接近170°的涂层。这种特定TiO₂配方的另一个有趣特性是其光催化行为，随着暴露于紫外光时间的增加，水的接触角会逐渐减小。虽然这种配方已在许多研究中使用，但对于如何根据涂层成分和紫外光暴露时间来确定或调整接触角（进而润湿性），尚无定量指导。在本文中，使用机器学习模型来预测任何指定的TiO₂/PMC涂层组合物达到特定润湿性（紫外光降低后的接触角）所需的紫外光暴露时间。为此，将八种不同的涂层组合物涂覆在载玻片上，并在不同的时间间隔内暴露于紫外光下。将收集到的接触角数据提供给不同的回归模型，以确定预测达到预定润湿性所需的紫外光暴露时间的最佳方法。使用了两种类型的机器学习模型：（1）参数模型和（2）非参数模型。结果表明，涂层配方与其在定时紫外光暴露后获得的接触角之间存在非线性关系。非参数方法显示出高精度和稳定性，其中广义回归神经网络（GRNN）表现最佳，在测试集、训练集和未见数据集上的准确率分别为0.971、0.977和0.933。本研究不仅为制备具有特定润湿性的涂层提供了定量指导，还提出了一种通用方法，可用于需要精确流体/表面相互作用的技术应用中的其他功能涂层。
