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    Surface Wettability Prediction Using Image Analysis and an Artificial Neural Network

    Year: 2022

    Journal: Langmuir, Volume 38, JUN 14, page 7208–7217

    Authors: Cho, Yoonkyung; Kim, Sungmin; Park, Chung Hee

    Organizations: National Research Foundation of Korea (NRF) - Korean government (MSIT) [NRF-2021R1A2B5B01001694, NRF-2021R1A6A3A01088074]

    In this study, a wettability-predicting method that uses an artificial neural network (ANN) by learning from digital images of the actual surface structures was developed. Polyester film surfaces were treated with oxygen plasma to realize various nanostructured surfaces. Surface structural characteristics from SEM images were quantified in a multifaceted way using a box-counting algorithm, a gray-level co-occurrence matrix algorithm, and binary image analysis. An ANN model that can predict wettability from surface structures was developed using the quantified surface structure and the resulting wettability as learning data. Furthermore, a surface with an optimal nanostructure to achieve superhydrophobicity was suggested by considering extracted surface structural parameters that significantly affect the surface wettability.
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