材料科学
腐蚀
自愈
涂层
多孔性
钝化
陶瓷
冶金
复合材料
图层(电子)
医学
替代医学
病理
作者
Pengxu Wei,Lianxi Chen,Xiaorong Li,Gu Haicheng,Dongchu Chen
标识
DOI:10.1080/01694243.2023.2251759
摘要
AbstractMicro-arc oxidation (MAO) is widely applied to improve the corrosion resistance of Mg alloys by forming a ceramic passivation film on their surface. The high cohesive strength and randomly distributed micro-porous structure of MAO films have significantly expanded their operation scope, serving as the base or bonding layer for composite or multi-functional coatings. However, mechanical scratches or impacts during installation and service periods can damage the protective performance of MAO films, leading to local corrosion or the rapid loss of mechanical integrity in Mg alloys. Therefore, the development of MAO coatings with self-healing properties has been extensively studied in recent decades. This paper aims to illustrate the gaps between the anti-corrosion characteristics and self-repairing mechanisms dependent on the oxide composition or porous structure of MAO films. The progress in preparation and the principle of inhibitor loading in self-healing functional MAO-based composite coatings are summarized, and future directions are highlighted.Keywords: MaoMg alloysself-healingcorrosion resistance AcknowledgementsWe would like to express our sincere gratitude to them. Also thank the authors of the references cited in this article, whose research results have laid a solid foundation for this article.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work has received patient help from teachers Dongchu Chen and Lianxi Chen of the research group, as well as strong support from three project funds (Guangdong Provincial Natural Science Foundation Project [Project Number: 2020B1515120093], Key R&D Plan Projects in Guangdong Province [Project Number: 2020B010186001] and Guangdong Basic and Applied Basic Research Foundation [Project Number: 2022A1515011752]).
科研通智能强力驱动
Strongly Powered by AbleSci AI