Hydrophobicity and superhydrophobicity with self-cleaning properties are well-known characteristics of several natural surfaces, such as the leaves of the sacred lotus plant (Nelumbo nucifera). To achieve a superhydrophobic state, micro- and nanometer scale topography should be realized on a low surface energy material, or a low surface energy coating should be deposited on top of the micro-nano topography if the material is inherently hydrophilic. Tailoring the surface chemistry and topography to control the wetting properties between extreme wetting states enables a palette of functionalities, such as self-cleaning, antifogging, anti-biofouling etc. A variety of surface topographies have been realized in polymers, ceramics, and metals. Metallic surfaces are particularly important in several engineering applications (e.g., naval, aircrafts, buildings, automobile) and their transformation to superhydrophobic can provide additional functionalities, such as corrosion protection, drag reduction, and anti-icing properties. This review paper focuses on the recent advances on superhydrophobic metals and alloys which can be applicable in real life applications and aims to provide an overview of the most promising methods to achieve sustainable superhydrophobicity.

An effective pathway to enhance the heat transfer process is to induce the formation of highly mobile condensate droplets, employing micro‐nanoengineered superhydrophobic surfaces. However, the design of the topography of these surfaces for sustained high performance constitutes a significant scientific and technological challenge. Herein, the critical role of the correlation length of topography is demonstrated as an important factor when designing superhydrophobic surfaces for heat transfer applications. Specifically, it is shown that a) a high correlation length value corresponds to increased space between surface structures and higher lateral distances between nucleating droplets, which results in lower droplet departure diameter and significantly delayed flooding of the surface and b) correlation length has to surpass a critical value for dropwise condensation (DWC) to be sustained in hierarchical structured surfaces, when the droplets are growing in a partial Cassie state. Following this rationale, droplets are categorized in three different energy and wetting states (Wenzel droplets, Cassie droplets of low kinetic energy and high energy jumping droplets), depending on the correlation length of the topography. Heat transfer experiments demonstrate an increase of 126% in the heat transfer coefficient (HTC) of surfaces exhibiting the maximum correlation length when compared to the flat hydrophobic surface.

Controlling the wettability properties of metallic materials and surfaces can enhance their applicability and improve their performance and durability in several fields, such as corrosion protection, heat transfer applications, self-cleaning, and friction reduction. Here, we present and compare some versatile fabrication methods that can provide aluminum surfaces with durable superhydrophobic performance which are suitable for heat transfer applications. To probe their stability in heat transfer applications, two evaluation protocols are designed, one which suggests immersion in hot water for several hours, and a second testing against the harsh conditions of hot steam impact. The superhydrophobic aluminum surfaces are fabricated by first creating micro or micro-nano roughness on an initially flat surface, followed by the minimization of its surface energy through two hydrophobization methods, one wet and one dry, thus creating a series of different coating materials. Surfaces are then evaluated by immersing them in hot water and exposing them to steam impact. It is demonstrated that despite the fact that all hydrophobization methods tested resulted in surfaces exhibiting superhydrophobic properties, only the ultra-thin Teflon-like coating, obtained after plasma deposition using C₄F₈ plasma, exhibited robust superhydrophobicity with hysteresis lower than 8° when immersed in water at 90 °C for 10 h. This surface also showed minimal wettability changes and was the only one to retain its hysteresis below 6° after 4 h of exposure to hot steam.

In this work, we use the basic ingredients of chaotic dynamics (stretching and folding of phase space points) for the characterization of the complexity of microscopy images of rough surfaces. The key idea is to use an image as the initial condition of a chaotic discrete dynamical system, such as the Arnold cat map, and track its transformations during the first iterations of the map. Since the basic effects of the Arnold map are the stretching and folding of image texture, the application of the map leads to an enhancement of the high frequency content of images along with an increase of discontinuities in pixel intensities. We exploit these effects to quantify the complexity of $S$ type (lying between homogeneity and randomness) of the image texture since the first (enhancement of high frequencies) can be used to quantify the distance of texture from randomness and noise and the second (the proliferation of discontinuities) the distance from order and homogeneity. The method is validated in synthetic images which are generated from computer generated surfaces with controlled correlation length and fractal dimension and it is applied in real images of nanostructured surfaces obtained from a scanning electron microscope with very interesting results.

Superhydrophobic surfaces have attracted a lot of interest due to their usability in various fields such as antibacterial and self-cleaning surfaces, heat transfer applications, and hydrodynamic drag reduction. Despite their potential, fabricating such surfaces typically requires complex processes and costly equipment. Here, we introduce an ultrafast, simple, and environmentally friendly method for fabricating nanostructured superhydrophobic surfaces via thermal nanoimprinting using randomly nanostructured aluminum surfaces as masters. Reusable Al masters bearing ∼250 nm boehmite nanostructures are produced by immersing aluminum in boiling water. Then, a modified thermal imprinting step, termed "pattern elongated" nanoimprinting, is used to pattern flexible and inherently hydrophobic polyethylene foils. Thus, the replicated pattern is elongated during demolding, yielding highly dense, filamentary structures. As a result, superhydrophobic polyethylene foils, exhibiting high contact angles, low hysteresis, and reduced interfacial friction, as indicated by friction measurements, are fabricated. Furthermore, computational fluid dynamics simulations are performed for three cases in which such surfaces can be used (i.e., in a microchannel, in a rectangular channel, and over an open surface), showcasing significant reductions in pressure drop compared with untreated surfaces, highlighting their high potential for drag reduction in pressure-driven microfluidics and low-friction maritime applications.

A salient feature of modern material surfaces used in cutting-edge technologies is their structural and spatial complexity, which endows them with novel properties and multifunctionality. The quantitative characterization of material complexity is a challenge that must be addressed to optimize their production and performance. While numerous metrics exist to quantify the complexity of spatial structures in various scientific domains, methods specifically tailored for characterizing the spatial complexity of material surface morphologies at the micro- and nanoscale are relatively scarce. In this paper, we utilize the concept of multiscale entropy to quantify the complexity of surface morphologies of rough surfaces across different scales and investigate the effects of amplitude fluctuations (i.e., surface height distribution) in both stepwise and smooth self-affine rough surfaces. The crucial role of the binning scheme in regulating amplitude effects on entropy and complexity measurements is highlighted and explained. Furthermore, by selecting an appropriate binning strategy, we analyze the impact of 2D imaging on the complexity of a rough surface and demonstrate that imaging can artificially introduce peaks in the relationship between complexity and surface amplitude. The results demonstrate that entropy-based spatial complexity effectively captures the scale-dependent heterogeneity of stepwise rough surfaces, providing valuable insights into their structural properties.