Predicting Wetting Properties for Surfaces with Stochastic Topography

Understanding the influence of topography on wettability is essential for improving the modeling of superhydrophobic surfaces. Moreover, wetting predictions can foresee corrosion, biological contamination, self-cleaning properties, and all phenomena related to wetting. In this context, this research work reports the experimental corroboration of a novel theoretical model for stochastic surfaces that relates the static contact angle for the heterogeneous wetting of surfaces to the root mean square (RMS) slope of the surface structures, allowing wetting prediction through topography. For this study, hydrophobic and superhydrophobic alumina thin films with gradual roughness were constructed. The films were deposited on glass using the dip-coating technique, textured with boiling water, and functionalized to achieve low surface energy using Dynasylan F-8815. Surface wettability was characterized using the sessile drop technique, and the RMS slope of the alumina surfaces was quantified using the atomic force microscopy (AFM) technique. The model, presented here for the first time, fits the experimental data, allowing wetting prediction for hydrophobic and superhydrophobic surfaces considering static contact angles. As expected, topography plays a fundamental role in achieving superhydrophobicity. Therefore, defining a topographic criterion, as performed here, for obtaining superhydrophobic surfaces is highly relevant to reduce the production costs of these surfaces and also enable new production processes and designs.