Effect of Adsorption and Interactions of New Triazole-Thione-Schiff Bases on the Corrosion Rate of Carbon Steel in 1 M HCl Solution: Theoretical and Experimental Evaluation.

Abstract

Due to the unique properties of steel, including its hardness, durability, and superconductivity, which make it an essential material in many industries, it lacks corrosion resistance. Herewith, two novel triazole-thione Schiff bases, namely, (E)-5-methyl-4-((thiophen-2-ylmethylene)amino)-2,4-dihydro-3H-1,2,4-triazole-3-thione (TMAT) and (E)-4-(((5-(dimethylamino)thiophen-2-yl)methylene)amino)-5-methyl-2,4-dihydro-3H-1,2,4-triazole-3-thione (DMTMAT), were synthesized and characterized. The corrosion inhibition (CI) ability of these two molecules on carbon steel in an aqueous solution of 1 M HCl as well as their interaction with its surface was studied using a number of different techniques. The results confirmed that the CI capability of these organic molecules depends on their strong adsorption on the metal surface and the formation of a protective anticorrosion film. Weight loss tests revealed that the inhibition efficiencies of TMAT and DMTMAT were 91.1 and 94.0%, respectively, at 1 × 10-3 M concentrations. The results of electrochemical impedance spectroscopy (EIS) indicated that there was a direct relationship between the inhibitor concentration and the transfer resistance. Potentiodynamic polarization (PDP) experiments have proven to be mixed-type inhibitors of C-steel in aqueous hydrochloric acid solution and follow the Langmuir adsorption isotherm model. Several thermodynamic and kinetic parameters were calculated. The negative values of the adsorption-free energy are -36.7 and -38.5 kJ/mol for TMAT and DMTMAT, respectively, confirming the spontaneity of the adsorption process. The MD simulation study's findings show that the inhibitor molecules are nearly parallel to the metal surface. The interaction energy calculated by the MD simulation and the inhibitory trend are the same. The practical implementation is consistent with what the computer models predicted.