Electrochemical-kinetics, MD-simulation and multi-input single-output (MISO) modeling using adaptive neuro-fuzzy inference system (ANFIS) prediction for dexamethasone drug as eco-friendly corrosion inhibitor for mild steel in 2 M HCl electrolyte.

In this research, the effect of Dexamethasone drug (DM) on mild steel corrosion in  2 M HCl was analyzed using weight loss, potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) and MD-simulation. In addition, Fourier transform infrared spectra (FTIR), scanning electron microscopy (SEM), Energy dispersive x-ray spectroscopy (EDX), and atomic force microscopy (AFM) were employed to inspect the mild steel surface in the blank and inhibited medium. For the optimization tool, adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the inhibition efficiency. The experimental data was categorized into two different sections for training and testing the ANFIS model. The developed model aimed to evaluate the fitness between the experimental and predicted values. From the results generated, optimum value (IE%) of DM was recorded as 80%, 81% and 83% at concentration of 0.4 g/L for weight loss, EIS and PDP respectively. Potentiodynamic polarization results reveal that Dexamethasone functions as a mixed-type inhibitor, whereas studies of EIS show that the inhibition mechanism is by the transfer of charges. Mild steel surface examination confirmed the presence of a protective adsorbed film on the mild steel surface. Thermodynamic parameters obtained imply that Dexamethasone is adsorbed on the steel surface by a physiochemical process and obeys Langmuir adsorption isotherm. Also the MD-simulation results evidenced that DM forms a metallic surface adsorbed film on the steel surface. From the ANFIS model, the sensitivity analysis shows that time and inhibitor concentration were the most important input variable while other input variables could not be neglected. ANFIS model coefficient of determination (R (2) 0.993) was found between the observed and predicted values. ANFIS model gave optimum prediction (80%) with high degree accuracy and robustness. The outcomes of this investigation provide more information, simulation, and prediction about inhibition of metal corrosion.