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Wetting imperfections are omnipresent on surfaces. They cause contact angle hysteresis and determine the wetting dynamics. Still, existing techniques (e.g., contact angle goniometry) are not sufficient to localize inhomogeneities and image wetting variations. We overcome these limitations through scanning drop friction force microscopy (sDoFFI). In sDoFFI, a 15 μL drop of Milli-Q water is raster-scanned over a surface. The friction force (lateral adhesion force) acting on the moving contact line is plotted against the drop position. Using sDoFFI, we obtained 2D wetting maps of the samples having sizes in the order of several square centimeters. We mapped areas with distinct wetting properties such as those present on a natural surface (e.g., a rose petal), a technically relevant superhydrophobic surface (e.g., Glaco paint), and an in-house prepared model of inhomogeneous surfaces featuring defined areas with low and high contact angle hysteresis. sDoFFI detects features that are smaller than 0.5 mm in size. Furthermore, we quantified the sliding behavior of drops across the boundary separating areas with different contact angles on the model sample. The sliding of a drop across this transition line follows a characteristic stick−slip motion. We use the variation in force signals, advancing and receding contact line velocities, and advancing and receding contact angles to identify zones of stick and slip. When scanning the drop from low to high contact angle hysteresis, the drop undergoes a stick−slip−stick−slip motion at the interline. Sliding from high to low contact angle hysteresis is characterized by the slip−stick−slip motion. The sDoFFI is a new tool for 2D characterization of wetting properties, which is applicable to laboratory-based samples but also characterizes biological and commercial surfaces.

Methods for fabricating super-liquid-repellent surfaces have typically relied on perfluoroalkyl substances. However, growing concerns about the environmental and health effects of perfluorinated compounds have caused increased interest in fluorine-free alternatives. Polydimethylsiloxane (PDMS) is most promising. In contrast to fluorinated surfaces, PDMS-coated surfaces showed only superhydrophobicity. This raises the question whether the poor liquid repellency is caused by PDMS interacting with the probe liquid or whether it results from inappropriate surface morphology. Here, we demonstrate that a welldesigned two-tier structure consisting of silicon dioxide nanoparticles combined with surface-tethered PDMS chains allows superliquid-repellency toward a range of low surface tension liquids. Drops of water−ethanol solutions with surface tensions as low as 31.0 mN m −1 easily roll and bounce off optimized surface structures. Friction force measurements demonstrate excellent surface homogeneity and easy mobility of drops. Our work shows that fluorine-free super-liquid-repellent surfaces can be achieved using scalable fabrication methods and environmentally friendly surface functionalization.

The friction force opposing the onset of motion of a drop on a solid surface is typically considered to be a material property for a fixed drop volume on a given surface. However, here we show that even for a fixed drop volume, the static friction force can be tuned by over 30% by preshaping the drop. The static friction usually exceeds the kinetic friction that the drop experiences when moving in a steady state.Both forces converge when the drop is prestretched in the direction of motion or when the drop shows low contact angle hysteresis. In contrast to static friction, kinetic friction is independent of preshaping the drop, that is, the drop history.Kinetic friction forces reflect the material properties.

State-of-the-art contact angle measurements usually involve image analysis of sessile drops. The drops are symmetric and images can be taken at high resolution. The analysis of videos of drops sliding down a tilted plate is hampered due to the low resolution of the cutout area where the drop is visible. The challenge is to analyze all video images automatically, while the drops are not symmetric anymore and contact angles change while sliding down the tilted plate. To increase the accuracy of contact angles, we present a 4-segment super-resolution optimized-fitting (4S-SROF) method. We developed a deep learning-based super-resolution model with an upscale ratio of 3; i.e., the trained model is able to enlarge drop images 9 times accurately (PSNR = 36.39). In addition, a systematic experiment using synthetic images was conducted to determine the best parameters for polynomial fitting of contact angles. Our method improved the accuracy by 21% for contact angles lower than 90°and by 33% for contact angles higher than 90°.

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