Novel imidazole based ionic liquid as anti-corrosion additive for aluminum alloy: Combined experimental, DFT/MD simulation and soft computing approach

Abstract

The anti-corrosion effectiveness of novel 1‑butyl‑3-methylimidazolium tetrachloroindate ionic liquid ([C₄MIM][InCl₄] (IL)) for aluminum-silicon-titanium (Al-Si-Ti) based aluminum alloy in 1mole (M) potassium hydroxide (KOH) electrolyte at 303–343 K was explored in the current study. To realize this, standard methods such as weight loss, electrochemical investigation, density functional theory (DFT)/molecular dynamics simulation (MD-simulation), scanning electron microscope (SEM), and scanning force microscopy (SFM), were employed to scrutinize the anti-corrosion successfulness of [C₄MIM][InCl₄] for aluminum alloy in KOH solution. From our findings, the ionic liquid mitigated the corrosion of Al-Si-Ti aluminum alloy, and the inhibition efficiency (IE%) is enhanced with improved ionic liquid concentration. The inhibition efficiencies obtained at 0.8 g/L [C₄MIM][InCl₄] concentration were 88.46%, 82%, and 82.35%, for gravimetric, potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) procedures, respectively. PDP result disclosed [C₄MIM][InCl₄] performed like a mixed-type inhibitor of a cathodic predominance. The SEM/SFM examination proved that the ionic liquid developed a shield coat on the metal alloy surface. The thermodynamic probe disclosed [C₄MIM][InCl₄] molecules fastened onto Al-Si-Ti aluminum alloy surface by physisorption mechanism and best fitted the Frumkin adsorption isotherm model. The DFT/MD-simulation procedure confirmed the adsorption configuration and orientation of [C₄MIM][InCl₄] molecules in gas and aqueous phase which is in harmony with the experimental discovering. Simulated neural network (SNN), and the adaptive neuro-fuzzy inference system (ANFIS) were deployed for a robust training, forecast and modeling of the interactive effects of the input parameters and the expected feedback, Herein, training via the ANN and ANFIS designs without (GA), as well as computing the statistical indices such as the mean squared error (MSE), hybrid fractional error function (HYBRID%), absolute average relative error (AARE), Marquardt's percentage standard deviation (MPSED%) and r-squared (R²) were employed to appraise the models capability. The optimal IE% forecasted was 88.4842% and 89.0643%, for the ANN and ANFIS, respectively. Based on the numerical values of the ANN and ANFIS parameters calculated much acceptance was accorded to the ANFIS model over the ANN due its high degree of precision and robustness. The aftermath of this study furnishes additional information on systematic plan of corrosion mitigation, and proffer useful instructions for the logical use of [C₄MIM][InCl₄] as anti-corrosion additive for Al-Si-Ti aluminum alloy threatened by alkaline solution.