Tolyltriazole (TTA) is a well-defined corrosion inhibitor for copper and copper alloys. However, there is little literature about its corrosion inhibition performance for mild steels in corrosive environments. This paper studied the electrochemical behavior of TTA in 0.5 M HCl solutions. Also, the morphology and nature of TTA layers on the steel surface were investigated. Electrochemical results showed that TTA is an excellent corrosion inhibitor for mild steel in acidic media with an efficiency of 91% for 0.07 M concentration. The results also indicated that TTA is a mixed-type inhibitor. XRD analysis revealed that the inhibition mechanism of TTA is based on the formation of an organic film due to the physically adsorbed molecules of TTA. AFM and EDS results showed that the formed layers decrease the adsorptivity of corrosive elements (Cl -) on the steel surface. Density Functional Theory (DFT) study confirmed the experimental results.

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Recent years have seen a significant increase in interest in smart anti-corrosion coatings, which can detect corrosive situations and autonomously discharge corrosion inhibitors. The mild steel surface was coated with pH-sensitive nanocontainers that had been manufactured and doped into an epoxy coating. Elemental mapping, Thermogravimetric Analysis (TGA)/ Differential Scanning Calorimetry (DSC), and Electrochemical Impedance Spectroscopy (EIS) methods were used to examine dispersion homogeneity, thermal durability, and corrosion tolerance. The findings indicated that nanocontainers dispersed uniformly in epoxy and that doping nanocontainers had no effect on the epoxy properties. When immersed in NaCl solution with nanocontainer doping concentrations of 3, 6, and 9 percent, EIS findings showed a rise in epoxy corrosion resistance following 5, 10, 15, 25, and 30 days. This enhancement was attributable to the smart release of corrosion inhibitors to protect steel surfaces. IR thermography and corroded substrates images confirmed the EIS data. Korsmeyer-Peppas kinetic model was the best model for fitting the obtained data.