Characterization of critical roughness indicators by digital image processing to predict contact angles on hydrophobic surfaces

This study innovatively applies digital imaging techniques to quantify the irregular nanoscale roughness of the surfaces of plasma-etched, hydrophobic polymer films. The main objective is to verify meaningful roughness indicators that predict the surface wettability by quantifying the surface nanostructures in a simple way. Five texture parameters were extracted from SEM images by box-counting and a gray-level co-occurrence matrix (GLCM) algorithm, which together provide 2D spatial information of roughness features. Also, five parameters related to height and aspect ratio profiles of roughness were obtained using AFM analysis. The 2D texture parameters and AFM roughness profiles were statistically correlated, and either set could be used to predict surface wettability with high explanatory power. The prediction was most powerful when variables of fractal dimension, arithmetic mean height Ra of roughness features in the assessed area, and skewness Rsk of height in the assessed area were used in the regression model. This result indicates that both the height profile and 2D spatial distribution of roughness features strongly affect surface wettability. The correlation of gray levels with the neighboring pixels was statistically correlated with Ra and Rsk, and a simple predictive model was developed using fractal dimension and correlation. This research is significant in that it explored a novel analytical approach in assessing the roughness characteristics to predict surface wettability. Ultimately, we anticipate that the developed approach can be applied in designing relevant process parameters to control the wetting properties. •This study explores use of digital imaging techniques to predict surface wettability of plasma-etched, hydrophobic films.•Image texture parameters could be used as alternatives to AFM roughness parameters to quantify irregular nano-roughness.•Image texture parameters could be used to predict surface wettability with high explanatory power.•Both the height profile and 2D spatial distribution of roughness features strongly affect surface wettability.