Organic protective coatings are extensively employed to reduce the corrosion of metallic materials in a wide range of industrial applications. However, mechanical defects such as scratches, pinholes, and microcracks can significantly impair their barrier properties, providing pathways for aggressive species and promoting the onset of localized corrosion. In the present work, supramolecular and thermoplastic ionic polymers containing carboxylic acid functionalities, partially or fully neutralized with metal ions, were synthesized and deposited onto aluminum alloy substrates. These coatings were designed to impart enhanced anticorrosive performance while simultaneously exhibiting self-healing and photocatalytic capabilities. Surface topography, coating integrity, and corrosion-related degradation were systematically analyzed using scanning electron microscopy and atomic force microscopy. Interfacial interactions, elemental distribution, and complex formation within the coatings were examined through energy-dispersive X-ray spectroscopy and X-ray diffraction analyses. The photocatalytic performance of the coatings was evaluated under controlled light exposure, revealing effective self-cleaning behavior without compromising coating adhesion or structural stability. Corrosion resistance was assessed using open-circuit potential measurements, potentiodynamic polarization, and electrochemical impedance spectroscopy in aggressive corrosive environments. Autonomous self-healing behavior was investigated by introducing artificial defects into the coatings, with the recovery process monitored using in situ Raman spectroscopy and electrochemical impedance spectroscopy. The developed composite coatings demonstrated a progressive increase in impedance modulus and polarization resistance with immersion time, confirming durable corrosion protection. Overall, the results highlight the successful integration of corrosion resistance, self-healing ability, and photocatalytic functionality within a single coating system.

This research was supported by the Research Excellence Partnerships-SEA project: GoSmartSurf; project code: 10574.