Cerium-based metal organic framework (Ce-MOF) as corrosion inhibitor for API 5L X65 steel in CO2- saturated brine solution: XPS, DFT/MD-simulation, and machine learning model prediction

Abstract

In this study, we report the synergistic effect of a metal ion/cluster (cerium nitrate hexahydrate) with an organic linker (2 methyl imidazole) as a nano-hybrid corrosion inhibitor for X65 steel in CO2 solution. To achieve this fit, a wide empirical study from wet chemical synthesis of Ce-MOF (cerium-metal organic framework), electrochemical method and surface analysis were considered. The empirical data obtained from the electrochemical studies were statistically analyzed via a machine learning model (adaptive neuro fuzzy inference system-ANFIS) considering multi input and single output function (MISO). The outcome revealed that Ce-MOF hindered the dissolution of ferrite and cementite phase of the steel in CO2 solution. The electrochemical impedance spectroscopy (EIS) revealed a significant rise in resistance to charge transfer with an increase in concentration of Ce-MOF. Polarization data indicated that Ce-MOF exhibited mixed-type inhibitor characteristics. The range of inhibition efficiencies were in the range of 97% and 95% at 0.15 wt% Ce-MOF for polarization and impedance studies, respectively. The theoretical study shows a flat adsorption orientation of Ce-MOF on the steel surface. Furthermore, the predictive capability of ANFIS model based on statistical norms; shows that the coefficient of determination (R2) is unity. From the statistical view point, much credibility was attributed to ANFIS model with robust description of the nonlinear interactions between the independent and dependent variables.