Anticorrosion mechanism of natural acidic amino acids on steel in chloride solution: Experimental, theoretical and machine learning approaches

Abstract

This paper investigates the corrosion inhibition of steel by two natural acidic amino acids, glutamic acid and aspartic acid, in a 3.5 wt% NaCl solution. The anti-corrosion activity is evaluated using various electrochemical tests, such as potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), and linear sweep voltammetry (LSV), in addition to quantum chemical calculation and molecular dynamic simulation. Results indicate that the natural compounds act as mixed-type inhibitors with a predominance of anodic inhibition and effectively increase charge transfer resistance and resistance to the solubility of Fe into Fe2+ as inhibitor concentration increases. At a concentration of 0.2 g/L, the inhibition efficiency reaches 57.84% for glutamic acid and 42.99% for aspartic acid. Moreover, the quantum chemistry calculations and molecular dynamics simulations, based on density functional theory (DFT) and Condensed-phase Optimized Molecular Potentials for Atomistic Simulation Studies (COMPASS) force field methods respectively, demonstrate that glutamic acid shows superior corrosion inhibition performance as it has higher binding energy and more stable adsorption on the steel surface than aspartic acid. Additionally, machine learning (ML) methods, including regression analysis and Logic regression, are employed to analyze the data and confirm the better corrosion inhibition performance of glutamate compared to aspartate.