journal article·2018-05

Novel self-healing anticorrosion coating based on L-valine and MBT-loaded halloysite nanotubes

Chundong Dong, Manxin Zhang, Tengfei Xiang, Ling Yang, Wenming Chan, Cheng Li

Journal of Materials Science· Volume 53· Issue 10
Publisher page

Abstract

This work creatively utilized the pH-dependent electrostatic interactions between L-valine (L-Val) and halloysite nanotubes (HNTs) to fabricate pH-responsive anticorrosion materials and compared with 2-mercaptobenzothiazole (MBT)-loaded HNTs, which were assembled via layer-by-layer (LbL) self-assembly. These two methods can achieve controlled release of inhibitors and self-healing performance. However, the loading capacity of L-Val-loaded HNTs is higher than that of MBT’s. There are 12 and 7 wt%, respectively. The pH-responsive release property was systematically evaluated by ultraviolet–visible (UV/Vis) spectrophotometry measurement. It demonstrates that 98% of adsorbed L-Val molecules released from HNTs within 300 min at pH 10 while the loaded MBT needs 120 h to achieve the equal ratio. Moreover, the difference of the release rate has a significant impact on the artificial crossed scratch experiment and shows a great performance gap in photographs. By comparing electrochemical impedance spectroscopy (EIS) data of three epoxy coatings, it can be seen that the epoxy coating, which was mixed with L-Val-loaded HNTs, shows a better anticorrosion ability than the epoxy coating contains MBT-loaded HNTs after immersion in 3.5 wt% sodium chloride solution for 96 h. Crucially, the pH-responsive anticorrosion material we fabricated can offer a rapid self-healing performance when the coating damaged by mechanical scratch via visual test and atomic absorption spectroscopy.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Clay minerals and soil interactions · Layered Double Hydroxides Synthesis and Applications · Mesoporous Materials and Catalysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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