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• Progress from passive to emerging intelligent corrosion-protective organic coatings. • Multivariate testing protocols critical for performance validation. • ML/AI-driven models improve the prediction accuracy of coating service life. • Advanced technologies enable real-time monitoring and adaptive maintenance of coatings. Organic coatings serve as critical protective barriers in a wide range of industrial sectors, effectively shielding substrates from corrosion, mechanical wear and environmentally induced damage. Among these, anti-corrosive organic coatings have become one of the most prominently developed categories. This paper provides a comprehensive analysis of the current development of anti-corrosive organic coating systems by systematically organizing information on their construction mechanisms, performance evaluation methods, performance enhancements and service life prediction models. We focus on the intrinsic degradation mechanisms of organic coatings and establish a relationship model between coating degradation pathways and their interaction with the surrounding environment. Additionally, the paper emphasizes the necessity of evaluating corrosion resistance performance of coating through a combination of multivariate techniques. These techniques serve as powerful tools for monitoring and predicting coating degradation, providing valuable insights into the effects of defects on coating performance and substrate corrosion. Furthermore, the article delves into the transformative impact of nanoparticles and nanocomposites on coating properties, particularly their significant role in enhancing corrosion resistance. Overall, a critical evaluation of the progress, limitations and prospects of service life prediction of organic coatings not only provides valuable insight for advancing research but helpful in optimizing engineering practices and supporting the coating industry with more effective and sustainable solutions.
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Muhammad Mubeen
Salman Khalid
Mohammad Tabish
Corrosion Communications
Chinese Academy of Sciences
University of Science and Technology of China
Beijing University of Chemical Technology
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Mubeen et al. (Fri,) studied this question.
www.synapsesocial.com/papers/6a080acea487c87a6a40cd3d — DOI: https://doi.org/10.1016/j.corcom.2026.02.008