Physisorption or chemisorption: Insight from AI computing model based on DFT, MC/MD-simulation for prediction of MOF-based inhibitor adsorption on Cu in brine solution

Abstract

This study investigates the dynamics of cerium metal organic framework (Ce-MOF) adsorption on the Cu surface in brine solution. This research integrates computational modelling based on Density Functional Theory (DFT), Monte Carlo (MC), and Molecular Dynamics (MD) simulations for enhanced predictive modelling. Additionally, by the utilization of the Dmol3 module within Biovia materials studio software, DFT calculations /geometric configurations through the Double Numerical plus d-functions (DND) were achieved. Notably, the results demonstrate a robust negative adsorption energy (-363.54 kcal/mol), indicating a highly stable interaction leading to the formation of a protective film on the metal surface. The study also proposes a corrosion inhibition mechanism involving physical and chemical barriers. The findings not only contribute to fundamental corrosion science but also propose innovative strategies for designing MOF-based inhibitors tailored to specific environmental conditions. This research introduces a novel perspective by combining theoretical calculations, and simulation models for predicting inhibitor behavior, fostering advancements in corrosion protection strategies.