ArticleMaterials (Basel, Switzerland)2025
Fabrication of Mechanically Robust Hydrophobic Surfaces Using Femtosecond Laser Shock Peening.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The harsh service environment has increased the demand for hydrophobic surfaces with excellent mechanical properties; however, how to manufacture such surfaces remains a significant challenge. In this study, a method for fabricating hydrophobic surfaces with excellent mechanical properties using femtosecond laser shock peening (fs-LSP) is proposed, without the need for any additional processing steps. Taking CH1900A martensitic steel as an example, a systematic analysis of the microstructure was conducted after fs-LSP, revealing the mechanisms by which fs-LSP affects surface morphology, grain structure, dislocation density, and grain boundary characteristics. The high-density dislocations and grain refinement induced by fs-LSP significantly enhanced the surface hardness and introduced residual compressive stresses. Additionally, the laser-induced periodic micro/nanostructures on the surface ensured excellent hydrophobic properties. The effect of single pulse energy and the number of impacts on fs-LSP has also been discussed in detail. As the pulse energy and number of impacts were increased, the surface microstructure of the material was progressively optimized, evidenced by grain refinement, an increase in geometrically necessary dislocation (GND) density, and a higher proportion of high-angle grain boundaries (HAGBs). Such optimization is not monotonous or unlimited; a pulse energy of 75 μJ and six impacts achieved the optimal effect, with the surface hardness reaching up to 8.2 GPa and a contact angle of 135 degrees. The proposed fs-LSP provides a new strategy for manufacturing hydrophobic surfaces with excellent mechanical properties, and the detailed discussion and analysis also provide theoretical guidance for process optimization.
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