Experimental and computational studies of furan derivatives in corrosion inhibition of mild steel

Abstract

Corrosion of metals causes huge losses in resources and industrial equipment especially when they are exposed to acidic medium. One of the most practical methods to control the corrosion of a metal is the use of heterocyclic organic compounds as corrosion inhibitors. A large number of organic compounds have been investigated as corrosion inhibitors; however, only few furan derivatives have been studied. In this study, eighteen furan derivatives were investigated as corrosion inhibitors for mild steel in hydrochloric acid. Furan derivatives were chosen as promising corrosion inhibitors based on their heterocyclic structures. The inhibition performance and corrosion process were studied using several techniques, namely potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), weight loss, adsorption isotherms, field emission scanning electron microscopy (FESEM), and X-ray photoelectron spectroscopy (XPS). The results showed the ability of furan derivatives to inhibit corrosion of mild steel in acidic solution and some of them showed high inhibition efficiencies of up to 96%. In addition, quantum chemical calculations using density functional theory (DFT) were used to evaluate inhibition performances of selected inhibitors and investigate active sites on the inhibitor molecule. The results showed the ability of DFT to explain the inhibition performances and assign the active sites of the inhibitors. Furthermore, several quantitative structure–activity relationship (QSAR) procedures were applied such as genetic algorithm-partial least square (GA-PLS), interval-PLS (IPLS), penalized multiple linear regression (PMLR) using ridge, LASSO and elastic net and sparse multiple linear regression (SMLR). The results showed that PMLR based on LASSO and elastic net, and SMLR based on elastic net were useful for the regression of the inhibition efficiencies. In conclusion, the quantum calculations and QSAR procedures complement the experimental investigations and interpret experimental results.