Predicting Sliding Angles on Random Pit-Distributed Textures Using Probabilistic Neural Networks

Abstract

The three-phase contact line best reflects the sliding ability of droplets on solid surfaces. Most studies on the sliding angle (SA) of superhydrophobic surfaces are limited to regularly arranged microtextured surfaces, lacking definite models and effective methods for a complex surface of a random texture. In this study, random pits with an area ratio of 19% were generated on 1 mm × 1 mm subregions, and the subregions formed arrays on a sample surface of 10 mm × 10 mm to obtain a randomly distributed microtexture surface with no pit overlaps. Although the contact angle (CA) of randomly pitted texture was the same, the SA was different. The SA of surfaces was affected by the pit location. The location of random pits increased the complexity of the three-phase contact line movement. The continuity of the three-phase contact angle (T) can reveal the rolling mechanism of the random pit texture and predict the SA, but the relationship between the T and SA is a relatively poor linear relation (R2 = 74%), and the SA of the random pit texture can only be roughly estimated. The quantized pit coordinates and SA were used as the input and output labels for the PNN model, respectively, and the accuracy of the model convergence was 90.2%.