This systematic review highlights self-healing waterproof coatings' mechanisms and industrial applications, suggesting advancements in protective materials.
Self-healing, waterproof coatings represent a new generation of smart protective materials designed to autonomously repair damage and maintain barrier properties during service. Conventional waterproofing systems such as polymer membranes, cementitious coatings, and crystalline admixtures provide initial resistance to water ingress but gradually deteriorate due to microcrack formation, ultraviolet radiation, thermal stresses, and mechanical loading. Once microcracks develop, moisture and aggressive ions penetrate through the coating layer and reach the underlying substrate, leading to corrosion of reinforcing steel, deterioration of concrete, and eventual structural failure. Recent advances in polymer science, nanotechnology, and bio-inspired materials have enabled the development of coatings capable of autonomously restoring their protective functionality. These coatings incorporate healing mechanisms such as microcapsule-triggered polymerization, reversible dynamic covalent bonds, supramolecular interactions, and shape-memory effects. Among these approaches, hybrid elastomeric coatings containing silane-based healing agents encapsulated within polymeric microcapsules have emerged as promising solutions for waterproofing applications. Upon crack formation, capsules rupture and release reactive silane molecules, which undergo moisture-induced hydrolysis and condensation reactions to form new siloxane networks. This process seals cracks and restores the waterproof barrier. This review discusses the fundamental concepts, healing mechanisms, materials, fabrication methods, characterization techniques, and industrial applications of self-healing waterproof coatings. Current challenges related to scalability, environmental sustainability, and long-term durability are also examined along with emerging research trends, including nanostructured coatings, multifunctional materials, and AI-assisted materials design
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Singh et al. (2026) studied this question.
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