article · Applied Surface Science Advances
This research evaluates the anti-corrosion performance of a novel ionic liquid, 1-butyl-3-methylimidazolium tetrachloroindate, on an aluminium-silicon-titanium alloy exposed to an alkaline potassium hydroxide solution across temperatures from 303 to 343 Kelvin. Using weight loss and electrochemical techniques, the study demonstrates that higher concentrations of the ionic liquid progressively improve protection, reaching an inhibition efficiency of over 88 percent at 0.8 grams per litre. Microscopic analyses confirmed that the compound forms a protective surface layer, whilst thermodynamic assessments indicated a physical adsorption mechanism following the Frumkin isotherm model. Computational simulations using density functional theory and molecular dynamics aligned with experimental findings regarding molecular orientation. Furthermore, machine learning models, specifically artificial neural networks and an adaptive neuro-fuzzy inference system, accurately predicted performance, with the neuro-fuzzy framework offering superior precision.
Aluminium alloys are widely used across multiple sectors but suffer severe degradation when exposed to harsh alkaline environments. By demonstrating an effective ionic liquid additive that physically protects the metal surface, this work provides a clearer pathway for formulating protective chemical treatments. The integration of advanced computer modeling also helps engineers accurately predict material preservation without relying exclusively on extensive physical trials.
The findings point to potential applications in formulating protective chemical additives for industries handling aluminium alloys in alkaline environments. Chemical manufacturers and industrial maintenance operators are the primary prospective users. Currently, this represents early-stage laboratory research combining experimental bench tests and computational predictions, meaning significant pilot testing and formulation development are needed before any commercial adoption.
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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.
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DOI: 10.1016/j.apsadv.2024.100578
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