1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model

The current study is focused on the synthesis and evaluation of 1-Hexadecyl-3-methylimidazolium tetrachloroindate [C16 mim][In Cl4] based ionic liquid (IL) as a corrosion inhibitor for mild steel in 1M HCl. Various advanced methods were employed in this research, such as potentiodynamic polarization (PDP), quantum chemical computations, molecular dynamics simulations, weight loss assessments, electrochemical impedance spectroscopy (EIS) and multivariate statistics via machine learning models. The ionic liquid (IL) under investigation demonstrated a notable corrosion inhibition efficiency (93.88 % weight loss, 94. % PDP, 75 % EIS). The combine electrochemical approach suggested a mechanism influenced by electron transfer, underscoring the IL's as a mixed-type inhibitor. The experimental data based on weight loss was optimized using response surface methodology (RSM). Maximum inhibition efficiency of 93.72 % was predicted by the RSM model. Also, the machine learning models based on artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) demonstrated good predictive power in analyzing the interactive effects affecting the inhibition process. The adsorption behavior of [C16 mim][In Cl4] on the mild steel surface further conformed to the Langmuir isotherm, demonstrating a monolayer adsorption process. The comprehensive nature of this approach facilitated a more in-depth adsorption process through computational modelling based on DFT and molecular dynamics. The machine learning models aligned credibly with the experimental findings with pronounced degree of accuracy. Thus, these integrated approaches unravel the potential of the studied IL as effective and sustainable corrosion inhibitor for severe acidic environments.