Existing methods for fabricating oil-repellent paper rely on highly fluorinated and therefore toxic chemicals. Non-fluorinated omniphobic paper with low contact angle hysteresis (CAH) has not been demonstrated. We report a facile method to prepare omniphobic paper through the vapor-phase deposition of chlorosilane molecules to create “liquid-like” polymer brushes on commercially available release liners. Compared to polymer brushes grafted from solution, this solvent-free method avoided physical deformation of the paper, such as curling or wrinkling. The obtained paper displayed low CAH (<6°) and roll-off angles for liquids exhibiting a broad range of surface tension, from 72.8 to 22.4 mN m–1. A hexadecane droplet (15 μL, 27.5 mN m–1) slid off the paper at a tilt angle less than 4°. The effects of surface roughness, composition, and the presence of particle additives on the wetting properties were investigated. The utility of the omniphobic paper was demonstrated in microfluidic, oil funnel, microtiter plate, and food packaging container applications.

Ice accumulation on aircraft is known to negatively impact the aerodynamic and mechanical operation, sometimes resulting in catastrophic failure. Recently, microwave resonators have gained interest as durable and reliable frost and ice detectors. Here, a microwave resonator sensor with built-in heating capability patterned into the ground plane was designed, fabricated, and tested to investigate real-time ice and frost growth. Sensing was performed on surfaces with anti-icing coatings to quantitatively analyze the effectiveness of these materials. The sensor was also tested to determine its ability to evaluate different deicing methods. The sensor itself was a split-ring resonator (SRR) operating at 5.82 GHz, which could effectively distinguish between water and ice by detecting changes in the dielectric properties on or around its surface. This application was particularly suited for an SRR due to the extreme difference between the relative permittivity of water (ε = 90) and ice (ε = 3.2) at 5 GHz and 0 °C. The results from this sensor can be used to determine the holdover time of various coatings to resist ice formation. This study validates the use of SRRs as ice detection sensors for applications where ice and frost are of great interest, such as on aircraft, roads, or walkways.

The liquid repellency enabled by air bubbles trapped within surface roughness features has drawn the attention of many researchers over the past century. The effects of surface roughness on superhydrophobicity have been extensively studied, mainly using regularly textured, idealized geometries. In comparison, fewer works have investigated the wettability of randomly textured surfaces, although they are much more similar to scalable and bioinspired surfaces. In this work, we investigated whether prior theories developed for understanding the wettability of regularly structured surfaces may be extended to randomly rough surfaces. Sandpapers of varying grit size, when hydrophobized, served as model randomly rough surfaces. Two analyses were conducted. In the first, termed the nonstatistical approach, direct imaging of the surfaces was used to extract an effective texture size and spacing, based on particle analysis and Delaunay triangulation. In the second, termed the statistical approach, two metrology parameters, sample autocorrelation length and mean periodicity, served as the effective texture size and spacing. Overall, the statistical method predicted water contact angles better than the nonstatistical approach, especially for surfaces in the fully wetted Wenzel state or fully nonwetted Cassie state. For surfaces exhibiting a mixed Cassie state of wetting, neither approach was able to predict the apparent contact angles precisely, likely due to the propagation of wetting in three dimensions, as two-dimensional analysis was used to derive the theories of wetting investigated. Estimates on the pressure stability of the nonwetted states were underpredicted when using the statistical parameters. In summation, when randomly rough surfaces exhibit a distribution of texture sizes and spacings, current theories of wettability cannot be directly implemented by a simple mapping using statistical metrology parameters.