Functional surfaces are of paramount engineering importance for various applications. The purpose of this review is to present counter-intuitive methods of fabrication based upon damage or instabilities for creating value-added surface functions.

Understanding the mechanisms of snow adhesion to surfaces and its subsequent shedding provides means to search for active and passive methods to mitigate the issues caused by snow accumulation on surfaces. Here, a novel setup is presented to measure the adhesion strength of snow to various surfaces without altering its properties (i.e., liquid water content (LWC) and/or density) during the measurements and to study snow shedding mechanisms. In this setup, a sensor is utilized to ensure constant temperature and liquid water content of snow on test substrates, unlike inclined or centrifugal snow adhesion testing. A snow gun consisting of an internal mixing chamber and ball valves for adjusting air and water flow is designed to form snow with controlled LWC inside a walk-in freezing room with controlled temperatures. We report that snow adheres to surfaces strongly when the LWC is around 20%. We also show that on smooth (i.e., RMS roughness of less than 7.17 μm) and very rough (i.e., RMS roughness of greater than 308.33 μm) surfaces, snow experiences minimal contact with the surface, resulting in low adhesion strength of snow. At the intermediate surface roughness (i.e., RMS of 50 μm with a surface temperature of 0 °C, the contact area between the snow and the surface increases, leading to increased adhesion strength of snow to the substrate. It is also found that an increase in the polar surface energy significantly increases the adhesion strength of wet snow while adhesion strength decreases with an increase in dispersive surface energy. Finally, we show that during shedding, snow experiences complete sliding, compression, or a combination of the two behaviors depending on surface temperature and LWC of the snow. The results of this study suggest pathways for designing surfaces that might reduce snow adhesion strength and facilitate its shedding.

Conformal coating of cylindrically‐patterned carbon nano tube micropillars with a dielectric poly‐tetravinyltetrameth ylcyclotetrasiloxane) (PV₄D₄) film using initiated chemical vapor deposition (iCVD), followed by lithiation for 3 days in a 1 M solution of LiClO₄ in propylene carbonate (PC) and annealing at 110 °C for 1 hour, results in partial capillarity‐driven collapse of cylinders and the formation of porous CNT “microcupcakes” that offer potential application as electrodes in 3D Li⁺ batteries. More details can be found in article number 1801247 by Srinivasa Kartik Nemani, Hossein Sojoudi, and co‐workers. Courtesy of Hossein Sojoudi, Sanha Kim, and Gareth H. McKinley, Karen K. Gleason, and A. John Hart groups at MIT.

Superhydrophobic surfaces have aroused great interest for being promising candidates in applications such as self-cleaning, anti-icing, and corrosion resistance. However, most of the superhydrophobic surfaces lose their anti-wettability in low surface temperature and high humidity. The loss of superhydrophobicity by condensed liquid is a very common practical incident, yet to be understood properly. Here we report the wettability of the superhydrophobic nanoporous surfaces in condensation and freezing environments. Various structured surfaces fabricated with carbon nanotubes (CNT) and coated by an ultrathin, conformal, and low surface energy layer of poly (1H,1H,2H,2H-perfluorodecylacrylate) (pPFDA) are exploited in humid conditions. Droplet impact dynamics, condensate characteristics, and freezing time delays are investigated on the CNT micropillars with various geometries along with the CNT forest and two commercially available anti-wetting coatings, NeverWet and WX2100. Nanoporous microstructured CNT pillars with the favorable topological configuration demonstrated complete droplet bouncing, significant freezing delays, and considerable durability during several icing/de-icing cycles. This study provides an understanding on the preferable geometry of the highly porous CNT micropillars for retaining hydrophobicity and preventing ice formation, which is of practical importance for the rational development of anti-wetting surfaces and their applications in low temperatures and humid conditions.

Wet snow accumulation on bridge cables and its shedding due to external phenomena such as rise in temperature, wind, and gravity is a serious threat to the safety of cars and pedestrians crossing the bridge. Commonly the accumulated snow on bridge cables is removed by external means such as mechanical removal or heat treatment which are expensive, time-consuming, and high-risk processes and are conducted based on little or no information available regarding the actual size and shape of the accumulated snow. In addition, cleaning of cables using the mechanical methods can potentially lead to erosion of cable materials when applied over years, resulting in enhanced surface roughness and potentially increased wet snow/ice accumulation during future precipitation events, and sometimes might require replacement of cable stays, which is an extremely costly and complicated task. Optimizing the number of mechanical cleaning procedures such as chain release through predicting the shape and thickness of the accumulated snow on the cable stays reduces the cost, time, and risk associated with the process. In this study, wet snow accumulation on torsionally rigid inclined cylinders of high-density polyethylene (HDPE) has been studied experimentally and numerically. A 2-D numerical model has been developed utilizing weather data to predict the thickness and the shape of the accumulated wet snow on inclined cylindrical surfaces. Outdoor experiments were also conducted to measure the density and thickness of accumulated snow, while monitoring the weather data real time. Overall, snow density was found to be linearly increasing with an increase in wind velocity, during snow precipitation. The maximum thickness and shape of the accumulated snow on cables obtained from the numerical model were found to be in good agreement with the outdoor experimental data. This work aims to provide a mean for prediction of snow accumulation on surfaces for optimizing the efficiency of the costly and high-risk snow removal procedures.