Advanced materials for smart protective coatings: Unleashing the potential of metal/covalent organic frameworks, 2D nanomaterials and carbonaceous structures.

The detrimental impact of corrosion on metallic materials remains a pressing concern across industries. Recently, intelligent anti-corrosive coatings for safeguarding metal infrastructures have garnered significant interest. These coatings are equipped with micro/nano carriers that store corrosion inhibitors and release them when triggered by external stimuli. These advanced coatings have the capability to elevate the electrochemical impedance values of steel by 2-3 orders of magnitude compared to the blank coating. However, achieving intelligent, durable, and reliable anti-corrosive coatings requires careful consideration in the design of these micro/nano carriers. This review paper primarily focuses on investigating the corrosion inhibition mechanism of various nano/micro carriers/barriers and identifying the challenges associated with using them for achieving desired properties in anti-corrosive coatings. Furthermore, the fundamental aspects required for nano/micro carriers, including compatibility with the coating matrix, high specific surface area, stability in different environments, stimuli-responsive behavior, and facile synthesis were investigated. To achieve this aim, we explored the properties of micro/nanocarriers based on oxide nanoparticles, carbonaceous and two-dimensional (2D) nanomaterials. Finally, we reviewed recent literature on the application of state-of the art nanocarriers based on metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs). We believe that the outcomes of this review paper offer valuable insights for researchers in selecting appropriate materials that can effectively enhance the corrosion resistance of coatings.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier B.V.)