journal article·2020-12

Fabrication of micro–nano-roughened surface with superhydrophobic character on an aluminium alloy surface by a facile chemical etching process

Mohammad Reza Attar, Ehsan Khajavian, Saman Hosseinpour, Ali Davoodi

Bulletin of Materials Science· Volume 43· Issue 1
Publisher page

Abstract

In the present work, we have fabricated a superhydrophobic surface on aluminium alloy 2024 through a simple immersion chemical etching method in hydrochloric acid followed by a functionalization step in stearic acid solution. The impact of etching time on water contact angle was investigated and a contact angle of $$\sim $$ 167 $$^{{\circ }}$$ was reached on the superhydrophobic surface, which was etched for 4 min. Morphology of the surface was evaluated by scanning electron microscopy and the surface chemical analysis was performed by energy-dispersive X-ray spectroscopy and Raman spectroscopy. We show that the fabricated superhydrophobic samples can besides water, also repel other liquids. We also demonstrate the self-cleaning properties of the fabricated samples using graphite particles as contaminants. Ultimately, we assessed the corrosion resistance properties of the fabricated surfaces by the potentiodynamic polarization method. The superhydrophobic surface exhibited increased corrosion potential and polarization resistance along with reduced corrosion current density, all of which are indicative of a significant improvement in corrosion performance of the superhydrophobic surface in comparison with typical aluminium 2024. The cheap and facile superhydrophobic surface fabrication method presented in this study can be applied to large scale samples with no need for electricity or expensive raw materials.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Surface Modification and Superhydrophobicity · Advanced Sensor and Energy Harvesting Materials · Electrohydrodynamics and Fluid Dynamics

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

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