Fluid repellency of a hydrophobic surface has been typically demonstrated in terms of water sliding angle. A drop shape analysis method with a written computer algorithm monitoring the image brightness was proposed to precisely estimate the sliding angle. A hydrophobic surface coated with silanized silicon dioxide or polytetrafluoroethylene was selected as a known sample for the method validation. Average pixel brightness in an 8-bit grayscale unit rapidly increased after a water drop rolled off the surface, thus removing its black pixels. The resulting sliding angle was then determined as the tilt angle of the sample stage related to the sliding time at the brightness leap. The optimized angular speed of the rotor at 0.1 degrees per frame was chosen to avoid an overestimation of the sliding angle due to the deceleration. The proposed method yielded accurate sliding angles with an error of less than 0.2 degrees. It was then applied to study the fluid resistance of commercial face masks including disposable surgical masks and reusable fabric masks. It was found that the outermost layer of the single-use surgical masks can moderately repel a water drop with a sliding angle of 49.4 degrees. Meanwhile, the pre-coated fabric masks retained high protection efficiency at a sliding angle of less than 45 degrees after about 20 wash cycles. In addition, a raw muslin fabric coated with a commercial water-repellent spray could be a promising and affordable alternative to the surgical mask during the pandemic with high water repellency even after a few washes. The results suggested that, besides the hydrophobicity indicated by the typical contact angle, the precise sliding angle estimated by the proposed alternative method could additionally provide crucial information that might lead to a detailed discussion of the fluid repellency of rough materials.
© 2022 The Authors. Published by American Chemical Society.