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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Elsevier
2025-07-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425017089 |
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| author | Mosab Kaseem Talitha Tara Thanaa Krishna Kumar Yadav Hagar H. Hassan Arash Fattah-alhosseini |
| author_facet | Mosab Kaseem Talitha Tara Thanaa Krishna Kumar Yadav Hagar H. Hassan Arash Fattah-alhosseini |
| author_sort | Mosab Kaseem |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ee0fdb6d9584415c925dee5a5fc5994f |
| institution | Kabale University |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-ee0fdb6d9584415c925dee5a5fc5994f2025-08-20T03:28:44ZengElsevierJournal of Materials Research and Technology2238-78542025-07-01373586359910.1016/j.jmrt.2025.07.050Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysisMosab Kaseem0Talitha Tara Thanaa1Krishna Kumar Yadav2Hagar H. Hassan3Arash Fattah-alhosseini4Corrosion and Electrochemistry Laboratory, Department of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul, 05006, Republic of Korea; Corresponding author.Corrosion and Electrochemistry Laboratory, Department of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul, 05006, Republic of KoreaDepartment of VLSI Microelectronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 602105, Tamil Nadu, India; Environmental and Atmospheric Sciences Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, Thi-Qar, 64001, IraqDepartment of Sanitary Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt; Corresponding author.Department of Materials Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2238785425017089PEO layerAZ31 Mg alloyLDHCorrosionMachine learning |
| spellingShingle | Mosab Kaseem Talitha Tara Thanaa Krishna Kumar Yadav Hagar H. Hassan Arash Fattah-alhosseini Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis Journal of Materials Research and Technology PEO layer AZ31 Mg alloy LDH Corrosion Machine learning |
| title | Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis |
| title_full | Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis |
| title_fullStr | Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis |
| title_full_unstemmed | Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis |
| title_short | Divalent cation engineering of PEO/LDH coatings for corrosion protection of AZ31 magnesium alloy supported by machine learning analysis |
| title_sort | divalent cation engineering of peo ldh coatings for corrosion protection of az31 magnesium alloy supported by machine learning analysis |
| topic | PEO layer AZ31 Mg alloy LDH Corrosion Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425017089 |
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