Stable Superhydrophobic Aluminum Surfaces Based on Laser-Fabricated Hierarchical Textures

Laser-microtextured surfaces have gained an increasing interest due to their enormous spectrum of applications and industrial scalability. Direct laser interference patterning (DLIP) and the well-established direct laser writing (DLW) methods are suitable as a powerful combination for the fabrication of single (DLW or DLIP) and multi-scale (DLW+DLIP) textures. In this work, four-beam DLIP and DLW were used independently and combined to produce functional textures on aluminum. The influence of the laser processing parameters, such as the applied laser fluence and the number of pulses, on the resulting topography was analyzed by confocal microscopy and scanning electron microscopy. The static long-term and dynamic wettability characteristics of the laser-textured surfaces were determined through water contact angle and hysteresis measurements, revealing superhydrophobic properties with static contact angles up to 163° and hysteresis as low as 9°. The classical Cassie–Baxter and Wenzel models were applied, permitting a deeper understanding of the observed wetting behaviors. Finally, mechanical stability tests revealed that the DLW elements in the multi-scale structure protects the smaller DLIP features under tribological conditions.

Artificial Intelligence Aided Design of Microtextured Surfaces: Application to Controlling Wettability

Artificial intelligence (AI) has emerged as a powerful set of tools for engineering innovative materials. However, the AI-aided design of materials textures has not yet been researched in depth. In order to explore the potentials of AI for discovering innovative biointerfaces and engineering materials surfaces, especially for biomedical applications, this study focuses on the control of wettability through design-controlled hierarchical surfaces, whose design is supported and its performance predicted thanks to adequately structured and trained artificial neural networks (ANN). The authors explain the creation of a comprehensive library of microtextured surfaces with well-known wettability properties. Such a library is processed and employed for the generation and training of artificial neural networks, which can predict the actual wetting performance of new design biointerfaces. The present research demonstrates that AI can importantly support the engineering of innovative hierarchical or multiscale surfaces when complex-to-model properties and phenomena, such as wettability and wetting, are involved.

Laser Microtextured Surfaces for Friction Reduction: Does the Pattern Matter?

Frictional performances of different textures, including axisymmetric and directional patterns, have been tested in the mixed and the hydrodynamic lubrication regimes. Experimental results, corroborated by numerical simulations, show that the leading parameter is the geometrical pattern void ratio since a large number of dimples offers, at low speed, a trap for debris whereas, at high speed, due to the flow expansion in each micro-hole, fosters a fluid pressure drop, the consequent insurgence of micro-cavitation and, ultimately, the reductions of the shear stresses. Furthermore, in this paper, it is shown that, by means of directional textures, equivalent hydrodynamic wedges can be built up, thus establishing different friction performances depending on the flow direction.

Mitigating cavitation erosion using biomimetic gas-entrapping microtextured surfaces (GEMS)

Cavitation refers to the formation and collapse of vapor bubbles near solid boundaries in high-speed flows, such as ship propellers and pumps. During this process, cavitation bubbles focus fluid energy on the solid surface by forming high-speed jets, leading to damage and downtime of machinery. In response, numerous surface treatments to counteract this effect have been explored, including perfluorinated coatings and surface hardening, but they all succumb to cavitation erosion eventually. Here, we report on biomimetic gas-entrapping microtextured surfaces (GEMS) that robustly entrap air when immersed in water regardless of the wetting nature of the substrate. Crucially, the entrapment of air inside the cavities repels cavitation bubbles away from the surface, thereby preventing cavitation damage. We provide mechanistic insights by treating the system as a potential flow problem of a multi-bubble system. Our findings present a possible avenue for mitigating cavitation erosion through the application of inexpensive and environmentally friendly materials.