journal article·2025-08Open access

A multi-level biomimetic LDH coatings with super hydrophobicity, corrosion resistance, anti-icing and anti-fouling properties on magnesium alloy

Lin Dai, Hongzhi Cui, Xiaohua Chen, Ruiqi Xu, Yuhao Zhang, Leigang Li

Journal of Magnesium and Alloys· Volume 13· Issue 8

Abstract

In this study, femtosecond pulsed laser processing was applied to the magnesium alloy, followed by in situ growth of Mg-Al layered double hydroxides (LDHs), and finally modification with low surface energy materials to prepare a biomimetic of centipede-like superhydrophobic composite coating. The resulting biomimetic coating features a dual-scale structure, comprising a micron-scale laser-etched array and nano-scale LDH sheets, which together create a complex hierarchical architecture. The multistage bionic superhydrophobic coating exhibits exceptional corrosion resistance, with a reduction in corrosion current density by approximately five orders of magnitude compared to the bare magnesium alloy substrate. This remarkable corrosion resistance is attributed to the synergistic effects of the superhydrophobicity with a contact angle (CA) of 154.60°, the densification of the surface LDH nanosheets, and the NO 3 - exchange capacity. Additionally, compared to untreated AZ91D alloy, the biomimetic coating prolongs ice formation time by 250% at -40 °C and withstands multiple cycles of sandpaper abrasion and repeated tape peeling tests. Furthermore, it demonstrates excellent self-cleaning and anti-fouling properties, as confirmed by dye immersion and dust contamination tests. The construction of the multi-level bionic structured coating not only holds significant practical potential for metal protection but also provides valuable insights into the application of formed LDH materials in functional bionic coating engineering.
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Research context

OpenAlex topics: Layered Double Hydroxides Synthesis and Applications · Corrosion Behavior and Inhibition · Magnesium Oxide Properties and Applications

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

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