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Abstract Slide electrification is the spontaneous separation of electric charges at the rear of water drops sliding over solid surfaces. This study delves into how surfaces treated with a low-pressure plasma impact water slide electrification. Ar, O2, and N2 plasma treatment reduced the drop charge and contact angles on glass, quartz, and SU-8 coated with 1H,1H,2H,2H-perfluoroctyltrichlorosilane (PFOTS), and polystyrene. Conversely, 64% higher drop charge was achieved using electrode-facing treatment in plasma chamber. Based on the zeta potential, Kelvin potential, and XPS measurements, the plasma effects were attributed to alterations of the topmost layer's chemistry, such as oxidation and etching, and superficially charge deposition. The surface top layer charges were less negative after electrode-facing and more negative after bulk plasma treatment. As a result, the zeta potential was less negative after electrode-facing and more negative after bulk plasma treatment. Although the fluorinated layer was applied after plasma activation, we observed a discernible impact of plasma-glass treatment on drop charging. Plasma surface modification offers a means to adjust drop charges: electrode-facing treatment of the fluorinated layer leads to an enhanced drop charge, while plasma treatment on the substrate prior to fluorination diminishes drop charges, all without affecting contact angles or surface roughness.

Abstract High speed side-view videos of sliding drops enable researchers to investigate drop dynamics and surface properties. However, understanding the physics of sliding requires knowledge of the drop width. A front-view perspective of the drop is necessary. In particular, the drop’s width is a crucial parameter owing to its association with the friction force. Incorporating extra cameras or mirrors to monitor changes in the width of drops from a front-view perspective is cumbersome and limits the viewing area. This limitation impedes a comprehensive analysis of sliding drops, especially when they interact with surface defects. Our study explores the use of various regression and multivariate sequence analysis (MSA) models to estimate the drop width at a solid surface solely from side-view videos. This approach eliminates the need to incorporate additional equipment into the experimental setup. In addition, it ensures an unlimited viewing area of sliding drops. The Long Short Term Memory (LSTM) model with a 20 sliding window size has the best performance with the lowest root mean square error (RMSE) of 67 µm. Within the spectrum of drop widths in our dataset, ranging from 1.6 to 4.4 mm, this RMSE indicates that we can predict the width of sliding drops with an error of 2.4%. Furthermore, the applied LSTM model provides a drop width across the whole sliding length of 5 cm, previously unattainable.