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

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 CO 2 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 CO 2 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 (R 2 ) 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.