journal article·2021-03

Ultrafast fiber laser-induced fabrication of superhydrophobic and self-cleaning metal surfaces

Sharjeel A. Khan, Ganjaboy S. Boltaev, Mazhar Iqbal, V. Kim, Rashid A. Ganeev, Ali S. Alnaser

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Abstract

Abstract Wetting properties of metal surfaces play important role in different practical and fundamental applications. In this work we investigate the variation of wetting properties of three different metals (aluminum, copper and galvanized steel) when ablated with ultrafast fiber lasers of femtosecond and picosecond pulse durations. The role of pulse duration and scanning speed used in the laser-surface structuring is analyzed. Scanning electron microscopy, atomic force microscopy, and attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) are employed to characterize the roughness and chemistry of the laser-structured surfaces. The degree of hydrophobicity and self-cleaning characteristics of three laser-structured metals are compared when aged in different post-ablation environments. Freshly laser-treated samples of aluminum, copper, and galvanized steel demonstrated superhydrophilic wetting response when their contact angles are measured immediately after laser ablation. The superhydrophilic characteristics of these freshly laser-structured surfaces are found to evolve to hydrophobic state after 30 days of ageing in ambient atmospheric conditions, while they take only 6 h to transform to superhydrophobic state when aged in low-pressure environment. Ultrafast laser-surface nanostructuring, coupled with vacuum ageing, proves to be an effective and rapid approach in achieving extreme superhydrophobic states in different metals, which makes it suitable for a wide range of self-cleaning applications.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Laser Material Processing Techniques · Laser-induced spectroscopy and plasma · Surface Modification and Superhydrophobicity

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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