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3 Computational Methods of Corrosion Monitoring
Hassane Lgaz1, Abdelkarim Chaouiki2, Mustafa R. Al‐Hadeethi3, Rachid Salghi2, and Han‐Seung Lee1
1 Department of Architectural Engineering, Hanyang University‐ERICA, Ansan, Korea
2 Laboratory of Applied Chemistry and Environment, ENSA, University Ibn Zohr, Agadir, Morocco
3 Department of Chemistry, College of Education, Kirkuk University, Kirkuk, Iraq
3.1 Introduction
In recent decades, considerable progress has been made in the development of computational tools. Computer simulations were used as innovative tools to answer complex questions in a way that experiments cannot do. Like many research fields, computational methods were extensively used in corrosion inhibition research [1–4]. Density functional theory (DFT), molecular dynamics (MD) simulations, and Monte Carlo (MC) simulations were extensively applied in many corrosion inhibition studies to discover the underlying inhibition mechanism.
There is a growing body of literature in the field of corrosion science that recognizes the importance of experimental methods for monitoring the corrosion inhibition process of metals and their alloys in aggressive environments [5–7]. It is, actually, the first and the most important step in evaluating the suitability of a corrosion inhibitor. However, questions have been raised about the cost and time taken by these methods. In addition, the relationship between the inhibition efficiency obtained experimentally and the molecular structure of inhibitor compounds cannot be understood from experimental techniques.
In this chapter, efforts were made to discuss the recent progress in using computational methods; especially, quantum chemical (QC) calculations based on DFT, MD, and MC simulations for evaluating the corrosion inhibition performance and adsorption behavior of corrosion inhibitors.
3.2 Quantum Chemical (QC) Calculations‐Based DFT Method
QC calculations have emerged in the last decades as an efficient tool for accurate prediction of stability, chemical reactivity, and corrosion inhibition performance of molecules [8–11]. In fact, a good understanding of the underlying reaction mechanisms based on QC analysis of molecular structure of inhibitor molecules is necessary. In this context, DFT has been introduced as a reliable method of examining the molecular structure behavior of corrosion inhibitors. Based on DFT, a series of calculations and vital parameters can be provided at a reasonable computation cost [12]. It can also predict the physicochemical properties of complex molecules in terms of their reactivities, which might be impossible to be evaluated experimentally [13–15]. DFT calculations have been performed at a large variety of theoretical levels to characterize many molecular systems. In general, from QC point of view, successful results would expand the range of chemical compounds that can be used as corrosion inhibitors, which is of great practical importance.
The prediction of corrosion behavior and the examination of electronic proprieties of any organic inhibitors can be explored based on DFT analyses with the application of hard and soft acid–base (HSAB) theory [16–18]. It can be noted that HSAB theory offers unique opportunities in the fundamental understanding of structure–property relationships and then adsorption abilities of inhibitor molecules. Of course, Koopmans’ theorem [19] is also one of the greatest contributions in theoretical chemistry that has found extensive application in QC calculations.
In this concept of DFT, the ground‐state electronic energy is determined completely by the electron density 𝜌(r) which provides quite accurate results of a molecular system at a theoretical level with no reference to a wave function. In fact, the main goal of the DFT method is to design functionals connecting the electron density with the energy by reducing the complexity of the theoretical system defined by the Schrödinger equation. Nowadays, DFT concept is widely used to extract structural information related to the adsorption of organic inhibitor molecules on the metal surface. Hence, a basic understanding of the theory behind the DFT method is necessary.
3.2.1 Theoretical Framework
The basis of DFT was introduced in1964 by Hohenberg and Kohn as a result of two theorems elegantly demonstrated in [20, 21]. According to these theorems, Hohenberg and Kohn stated that the ground state of a fully interacting system with N electrons is a unique functional of the electronic density 𝜌(r). So, DFT is a formulation in terms of functional of the density.
For DFT‐based simulation, the general expression may then be written as [22]:
(3.1)
