Wettability and Bactericidal Properties of Bioinspired ZnO Nanopillar Surfaces.

Nanomaterials of zinc oxide (ZnO) exhibit antibacterial activities under ambient illumination that result in cell membrane permeability and disorganization, representing an important opportunity for health-related applications. However, the development of antibiofouling surfaces incorporating ZnO nanomaterials has remained limited. In this work, we fabricate superhydrophobic surfaces based on ZnO nanopillars. Water droplets on these superhydrophobic surfaces exhibit small contact angle hysteresis (within 2-3°) and a minimal tilting angle of 1°. Further, falling droplets bounce off when impacting the superhydrophobic ZnO surfaces with a range of Weber numbers (8-46), demonstrating that the surface facilitates a robust Cassie-Baxter wetting state. In addition, the antibiofouling efficacy of the surfaces has been established against model pathogenic Gram-positive bacteria Staphylococcus aureus (S. aureus) and Gram-negative bacteria Escherichia coli (E. coli). No viable colonies of E. coli were recoverable on the superhydrophobic surfaces of ZnO nanopillars incubated with cultured bacterial solutions for 18 h. Further, our tests demonstrate a substantial reduction in the quantity of S. aureus that attached to the superhydrophobic ZnO nanopillars. Thus, the superhydrophobic ZnO surfaces offer a viable design of antibiofouling materials that do not require additional UV illumination or antimicrobial agents.

Deep Learning to Analyze Sliding Drops.

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°.

Adaptation of a Styrene-Acrylic Acid Copolymer Surface to Water.

Solid surfaces, in particular polymer surfaces, are able to adapt upon contact with a liquid. Adaptation results in an increase in contact angle hysteresis and influences the mobility of sliding drops on surfaces. To study adaptation and its kinetics, we synthesized a random copolymer composed of styrene and 11-25 mol% acrylic acid (PS/PAA). We measured the dynamic advancing (θA) and receding (θR) contact angles of water drops sliding down a tilted plate coated with this polymer. We measured θA ≈ 87° for velocities of the contact line <20 µm/s. At higher velocities, θA gradually increased to ∼98°. This value is similar to θA of a pure polystyrene (PS) film, which we studied for comparison. We associate the gradual increase in θA to the adaptation process to water: The presence of water leads to swelling and/or an enrichment of acid groups at the water/polymer interface. By applying the latest adaptation theory (Butt et al. Langmuir 2018, 34, 11292), we estimated the time constant of this adaptation process to be ≪1 s. For sliding water drops, θR is ∼10° lower compared to the reference PS surface for all tested velocities. Thus, at the receding side of a sliding drop, the surface is already enriched by acid groups. For a water drop with a width of 5 mm, the increase in contact angle hysteresis corresponds to an increase in capillary force in the range of 45-60 µN, depending on sliding velocity.

When and how self-cleaning of superhydrophobic surfaces works.

Despite the enormous interest in superhydrophobicity for self-cleaning, a clear picture of contaminant removal is missing, in particular, on a single-particle level. Here, we monitor the removal of individual contaminant particles on the micrometer scale by confocal microscopy. We correlate this space- and time-resolved information with measurements of the friction force. The balance of capillary and adhesion force between the drop and the contamination on the substrate determines the friction force of drops during self-cleaning. These friction forces are in the range of micro-Newtons. We show that hydrophilic and hydrophobic particles hardly influence superhydrophobicity provided that the particle size exceeds the pore size or the thickness of the contamination falls below the height of the protrusions. These detailed insights into self-cleaning allow the rational design of superhydrophobic surfaces that resist contamination as demonstrated by outdoor environmental (>200 days) and industrial standardized contamination experiments.