Advanced Corrosion Modelling: an Atomistic Approach to Describe the Corrosion Inhibition Process

Abstract

Toxic inorganic compounds, usually used as corrosion inhibitors within paint films, are being replaced by more environmentally friendly compounds in coatings across aerospace, automotive and infrastructure industries. However, the corrosion inhibition mechanism behind these compounds must be better understood. Molecular modelling can provide insights by analyzing the metal-inhibitor-water interface, but a realistic representation of the electrochemical processes is crucial. In this thesis, six critical aspects (A) must be considered to realistically describe the corrosion inhibition process in modelling: (A1) the electronic properties of isolated inhibitors, (A2) the interaction of the inhibitor with the surface, (A3) the surface model, (A4) the effect of the anodic and cathodic zones on the surface, (A5) the solvent effects, and (A6) the electrodes' potential effects. While aspects A1-A3 are usually considered in the literature, aspects A4-A6 and some more complex surface models in A3 are not. Here, two main ones will be the focus: the applied electrode potential and solvent effects._x000D_ The system chosen to showcase these two aspects is the adsorption of 2-mercaptobenzoimidazole (MBI) on Cu(111) surfaces. First, molecular modelling based on Density Functional Theory (DFT) combined with the Non-Equilibrium Green's Functions (NEGF) formalism was used to address the effect of the applied electrode potential in the MBI adsorption under different coverage and voltages. Our results show that the system becomes more stable as the coverage of molecules increases, forming a self-assembled monolayer (SAM). The electronic structure is sensitive to the applied voltage, inducing a charge redistribution in the system, where the MBI is acting as a metal and accumulates the induced charge at the molecule's edge. _x000D_ The final step addressed the solvent effects in the MBI adsorption, where the methodology combining the NEGF formalism and the Quantum Mechanics/Molecular Mechanics (QM/MM) method was used under varying coverage conditions: bare surface, a low-coverage with one MBI adsorbed, and a high-coverage case with six MBI adsorbed, and under different applied voltages. The results suggest that a stable SAM is formed in the high-coverage case. It prevails along the dynamics and acts as a physical barrier between the water molecules and the surface, preventing their interaction with the metallic surface. The results in the bare surface and with one MBI adsorbed showed a water layer directly in contact with the Cu surface, contrary to the case with a SAM adsorbed, where the first water layer is located at the end of the SAM structure._x000D_ Finally, a collaboration with experimental partners in RMIT University was performed to investigate the adsorption of 2-mercaptobenzothiazole (2-MBT) and 2-aminobenzothiazole (2-ABT) on galvanized steel, using a Zn(0001) model surface. The findings aligned with experimental data using X-ray Photoelectron Spectroscopy (XPS), which revealed the bonding between 2-ABT and the Zn through the endo S and the 2-MBT through the endo N and exo S. Electronic structure analysis revealed the inhibitor's charge density donation to the Zn surface. The experimental data also highlighted the strong interaction between 2-MBT and the surface, potentially forming complexes with Zn2+ ions._x000D_ The results from this QM/MM-NEGF approach differentiate from the usual CI modelling, which lacks a precise description of the atomistic process occurring at the metallic inhibitor aqueous interface. Instead, the QM/MM-NEGF approach provides an accurate and predictive way to simulate fundamental interactions that lead to CI properties. Furthermore, the results from this approach can potentially be used to feed artificial intelligence (AI) methods and multiscale models, which can bridge the gap between nanoscale CI modelling and the continuum scale of CI processes. This could enhance our understanding of corrosion inhibition and lead to more effective solutions.

Date of Award	6 Jun 2024
Original language	English
Supervisor	Ernane de Freitas Martins (Director), Ivan Cole (Director) & Pablo Jesús Ordejón Rontome (Director)