Abstract
The performance of a gemini surfactant (GS), 1,6-Hexyldiyl-bis-(dimethyldodecylammonium bromide), as a corrosion inhibitor for mild steel in 1 M H2SO4, was studied using response surface methodology (RSM), artificial neural network (ANN), density functional theory (DFT), and molecular dynamics (MD) simulations. Excellent developed predictive RSM models (R-2 =0.993-0.999) for steel inhibition efficiency, corrosion rate and weight loss were significantly influenced by the investigated operational variables; exposure time (2-48 hours), temperature (25-65 degrees C) and the GS concentration (0.01-0.1 mM). Electrochemical studies using LPR, PDP, and EIS techniques yielded improved inhibition efficiencies of 99.2, 99.67, and 99.37% respectively at 1 mM concentration of the GS. PDP results showed the GS to act as a mixed-type inhibitor. The GS adsorption onto the mild steel was described by the Langmuir model. The quantum chemical calculations using DFT indicate electron donating ability of the GS. MD simulations were completed to explore the adsorption configurational performance of the GS on the Fe (1 1 0) interface. The surface morphology investigations by SEM/EDS and XPS analysis confirm the adsorption of GS to form protective film on the mild steel surface. Both the RSM and ANN models' predictions were experimentally verified with the ANN models' predictive outputs possessing superior performance. This study demonstrates that the designed gemini surfactant can be used as a cost-effective and efficient inhibitor for mild steel acid-induced corrosion in various fields. (C) 2022 Elsevier B.V. All rights reserved.