Unravelling adsorption potential of expired clindamycin as corrosion inhibitor at mild Steel/HCl interface via response surface methodology, artificial neural network, electrochemical, and computational strategies

Corrosion has produced unprecedented disintegration of metals, constituting an imminent danger to mankind and triggering catastrophic global economic losses. The effectiveness of expired clindamycin (ECLI) as a low-cost corrosion control agent for mild steel was investigated utilising response surface methodology (RSM), artificial neural network (ANN), potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), quantum chemical computation (QCC), and molecular dynamic simulation (MDS) studies in conjunction with thermometric and gasometric protocols at different HCl concentrations. The RSM model demonstrated an outstanding level of accuracy in predicting the mild steel corrosion inhibition efficiency (IE), the volume of hydrogen gas (VHG), reaction number (RN), and corrosion rate (CR). The model was significantly influenced by the operational parameters that were investigated, such as temperature (299–333 K), ECLI concentrations (100–500 mg/L), immersion time (1–6 h), and acid concentration (0.5–2.5 M). It was observed that as ECLI concentration increases, the VHG, RN, and CR decreased per time as well as % IE increased. The thermometric, gasometric, PDP, and EIS results showed percentage inhibition efficiency of 69.10, 69.49, 83.17, and 77.87 %, respectively. PDP revealed that ECLI operates as a mixed type of inhibitor, and EIS indicated that the inhibition process involves charge transfer. The Langmuir isotherm suits better and accurately describes the ECLI adsorption process on mild steel. The electron transfer propensity of the ECLI on the metal surface is measured by QCC using the DFT approach. MDS was implemented to establish the optimal adsorption orientation between ECLI and Fe (110). The inspection of surface morphology by SEM displayed the formation of a blanket-like layer on the steel by ECLI. To validate the experimental results, RSM and ANN prediction models were utilised, which were evaluated using a normal plot of residual, predicted versus actual, and residual versus run, and were found to be effective modelling tools. This study illustrates that ECLI can be utilised as a potent and affordable mild steel inhibitor, even at high acid concentrations.