Issue 23, 2022

Corrosion self-warning and repair tracking of polymeric coatings based on stimulus responsive nanosensors

Abstract

Smart polymeric coatings with early corrosion self-warning and damage self-repairing characteristics have garnered tremendous interest due to their ability to sense corrosion reactions and repair coating defects. However, tracking the repair process and its underlying protection mechanism is highly challenging. Herein, we report the construction of a novel composite coating by incorporating multifunctional nanosensors (graphene oxide-zeolitic imidazole frameworks loaded with 1,10-phenanthroline) into a thermo-responsive polyurethane. Under damaging events, the localized acidity derived from metal corrosion stimulates the decomposition of the nanosensors to produce 1,10-phenanthroline and benzimidazole. The generated ferrous ions are rapidly sensed by the released 1,10-phenanthroline to produce a conspicuous red color, which warns of the corrosion occurrence. In profiting from the photothermal effect of graphene oxide, the composite coating exhibits efficient crack closure behavior under near-infrared light irradiation. Morphology observation indicates that a coating scratch (about 30 μm wide) almost closed with 20 s of irradiation. The photothermally activated crack closure combined with benzimidazole inhibition endows the prepared coating with superior self-repairing performance. Interestingly, the change in color intensity around the coating defect can assist in tracking the repair process. Therefore, this work provides a novel strategy to visualize microscopic behaviors during damage and repair processes.

Graphical abstract: Corrosion self-warning and repair tracking of polymeric coatings based on stimulus responsive nanosensors

Supplementary files

Article information

Article type
Paper
Submitted
12 Mar 2022
Accepted
07 May 2022
First published
09 May 2022

Nanoscale, 2022,14, 8429-8440

Corrosion self-warning and repair tracking of polymeric coatings based on stimulus responsive nanosensors

C. Liu, L. Cheng, B. Qian, L. Cui and R. Zeng, Nanoscale, 2022, 14, 8429 DOI: 10.1039/D2NR01406H

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