Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis
This research explores the anticorrosive performance of AZ31 magnesium alloy treated with plasma electrolytic oxidation (PEO), further enhanced by layered double hydroxides (LDHs) containing Co, Ni, or Zn. The PEO layer serves as a baseline protective coating, while adding CoFeLDH, NiFeLDH, and ZnFe...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425017089 |
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| Summary: | This research explores the anticorrosive performance of AZ31 magnesium alloy treated with plasma electrolytic oxidation (PEO), further enhanced by layered double hydroxides (LDHs) containing Co, Ni, or Zn. The PEO layer serves as a baseline protective coating, while adding CoFeLDH, NiFeLDH, and ZnFeLDH films addresses its inherent porosity and enhances corrosion resistance. Potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) tests, conducted in a 3.5 wt% NaCl solution, reveal that all LDH coatings significantly improve corrosion protection over the PEO layer alone. Among them, CoFeLDH demonstrates the highest corrosion resistance, with a corrosion current density (icorr) of 8.4 × 10−9 A/cm2 and a corrosion potential (Ecorr) of −0.251 V, outperforming the PEO coating (icorr of 2.7 × 10−6 A/cm2 and Ecorr of −0.545 V). This superior performance is attributed to the densely packed nanosheet structure of CoFeLDH, which creates an effective physical barrier against corrosive species while promoting ion exchange that may help minimize chloride ion penetration. A machine learning model was employed alongside the experimental findings to identify key electrochemical parameters influencing corrosion behavior and to support data-driven interpretation. The results emphasize the critical importance of cation selection and illustrate the potential of integrating experimental design with machine learning for optimizing LDH-based protective coatings on Mg alloys. |
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| ISSN: | 2238-7854 |