Machine Learning Prediction of TiO2-Coating Wettability Tuned via UV Exposure

Mohamad Jafari Gukeh; Shashwata Moitra; Ali Noaman Ibrahim; Sybil Derrible; Constantine M Megaridis

doi:10.1021/acsami.1c13262

Machine Learning Prediction of TiO₂-Coating Wettability Tuned via UV Exposure

ACS Appl Mater Interfaces. 2021 Sep 29;13(38):46171-46179. doi: 10.1021/acsami.1c13262. Epub 2021 Sep 15.

Authors

Mohamad Jafari Gukeh¹, Shashwata Moitra¹, Ali Noaman Ibrahim^{1

2}, Sybil Derrible³, Constantine M Megaridis¹

Affiliations

¹ Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States.
² Mechanical Engineering College, University of Babylon, Hilla 51001, Iraq.
³ Department of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, Chicago, Illinois 60607, United States.

PMID: 34523902
DOI: 10.1021/acsami.1c13262

Abstract

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.

Keywords: TiO2 coating; contact angle; machine learning; photocatalytic; superhydrophilic; superhydrophobic; wettability.