Surfaces with extreme wettability (too low, superhydrophobic; too high, superhydrophilic) have attracted considerable attention over the past two decades. Titanium dioxide (TiO₂) has been one of the most popular components for generating superhydrophobic/hydrophilic coatings. Combining TiO₂ 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 TiO₂ 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 TiO₂/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.

中文翻译：

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通过紫外线照射调整的二氧化钛涂层润湿性的机器学习预测

在过去的二十年中，具有极端润湿性（太低，超疏水；太高，超亲水）的表面引起了相当多的关注。二氧化钛 (TiO ₂ ) 一直是用于生成超疏水/亲水涂层的最流行的组分之一。将 TiO ₂与乙醇和商用氟丙烯酸共聚物分散体（称为 PMC）相结合，可以生产出水接触角接近 170° 的涂层。这种特定 TiO _{2 的}另一个感兴趣的特性配方是其光催化行为，这会导致水的接触角随着紫外线照射时间的增加而逐渐减小。虽然该配方已用于许多研究，但没有定量指导来确定或调整接触角（以及润湿性）与涂层组成和紫外线照射时间。在本文中，机器学习模型用于预测任何指定的 TiO ₂所需的紫外线照射时间/PMC 涂料组合物以获得一定的润湿性（紫外线降低的接触角）。为此，将八种不同的涂层组合物涂在载玻片上并暴露于紫外线下不同的时间间隔。将收集的接触角数据提供给不同的回归模型，以指定最佳方法来预测预定润湿性所需的紫外线照射时间。使用了两种类型的机器学习模型：（1）参数和（2）非参数。结果表明涂料配方与其在定时紫外线照射后获得的接触角之间存在非线性行为。非参数方法显示出高精度和稳定性，一般回归神经网络 (GRNN) 在测试、训练和未知数据集上的精度分别为 0.971、0.977 和 0.933，表现最佳。