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Applied Physics A

, Volume 108, Issue 3, pp 559–568 | Cite as

Electrochemical machining of super-hydrophobic Al surfaces and effect of processing parameters on wettability

  • Jin-long Song
  • Wen-ji XuEmail author
  • Xin Liu
  • Yao Lu
  • Jing Sun
Article

Abstract

Super-hydrophobic aluminum (Al) surfaces were successfully fabricated via electrochemical machining in neutral NaClO3 electrolyte and subsequent fluoroalkylsilane (FAS) modification. The effects of the processing time, processing current density, and electrolyte concentration on the wettability, morphology, and roughness were studied. The surface morphology, chemical composition, and wettability of the Al surfaces were investigated using scanning electron microscopy (SEM) equipped with energy-dispersive spectroscopy (EDS), white-light interferometry, roughness measurements, X-ray diffraction (XRD), Fourier-transform infrared spectrometry (FTIR), and optical contact angle measurements. The results show that hierarchical rough structures and low surface energy films were present on the Al surfaces after electrochemical machining and FAS modification. The combination of the rough structures and the low surface energy materials plays a crucial role in achieving super-hydrophobicity. Compared with the anodic oxidation and chemical etching method, the method proposed in our work does not require strong acid or alkali, and causes less harm to the environment and operators but with high processing efficiency. The rough structures required by the super-hydrophobic surfaces were obtained at 30-s processing time and the best super-hydrophobicity with 164.6 water contact angle and 2 tilting angle was obtained at 360 s. The resulting super-hydrophobic Al surfaces have a long-time stability in air and an excellent resistance to corrosive liquids.

Keywords

Contact Angle Wettability Water Contact Water Contact Angle Electrolyte Concentration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (NSFC, Grant No. 90923022).

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Jin-long Song
    • 1
  • Wen-ji Xu
    • 1
    Email author
  • Xin Liu
    • 1
  • Yao Lu
    • 1
  • Jing Sun
    • 1
  1. 1.School of Mechanical EngineeringDalian University of TechnologyDalianP.R. China

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