Computational Exploration of Phenolic Compounds in Corrosion Inhibition: A Case Study of Hydroxytyrosol and Tyrosol

Abstract

The corrosion of materials remains a critical challenge with significant economic and infrastructural impacts. A comprehensive understanding of adsorption characteristics of phytochemicals can facilitate the effective design of high-performing environmentally friendly inhibitors. This study conducted a computational exploration of hydroxytyrosol (HTR) and tyrosol (TRS) (potent phenolic compounds found in olive leaf extracts), focusing on their adsorption and reactivity on iron surfaces. Utilizing self-consistent-charge density-functional tight-binding (SCC-DFTB) simulations, molecular dynamics (MD) simulations, and quantum chemical calculations (QCCs), we investigated the molecules' structural and electronic attributes and interactions with iron surfaces. The SCC-DFTB results highlighted that HTR and TRS coordinated with iron atoms when adsorbed individually, but only HTR maintained bonding when adsorbed alongside TRS. At their individual adsorption, HTR and TRS had interaction energies of -1.874 and -1.598 eV, which became more negative when put together (-1.976 eV). The MD simulations revealed parallel adsorption under aqueous and vacuum conditions, with HTR demonstrating higher adsorption energy. The analysis of quantum chemical parameters, including global and local reactivity descriptors, offered crucial insights into molecular reactivity, stability, and interaction-prone atomic sites. QCCs revealed that the fraction of transferred electron ∆N aligned with SCC-DFTB results, while other parameters of purely isolated molecules failed to predict the same. These findings pave the way for potential advancements in anticorrosion strategies leveraging phenolic compounds.

Keywords: adsorption characteristics; corrosion inhibition; density-functional tight-binding; green inhibitor; hydroxytyrosol; molecular dynamics simulation; phenolic compounds; quantum chemical calculation; tyrosol.

Figure 1

(a) Iron crystal structure, (b) DFTB-simulated box, and (c) molecular structure of HTR and TRS molecules.

Figure 2

DFT-refined structures of HTR and TRS molecules as derived through the DFT/GGA approach in panels (a). Visualizations of the HOMO and the LUMO are correspondingly represented in panels (b) and (c), respectively.

Figure 3

Depiction of Fukui function and dual descriptor values associated with HTR and TRS molecules as determined by the DFT/GGA technique.

Figure 4

MESP of HTR and TRS molecules obtained by DFT/B3LYB method.

Figure 5

SCC-DFTB optimized adsorption structures of the hydroxytyrosol (HTR) molecule on the Fe(110) surface: (a) lateral perspective, (b) superior perspective, and (c) magnified lateral view. Bond lengths are denoted in angstroms (Å).

Figure 6

SCC-DFTB optimized adsorption structures of the tyrosol (TRS) molecule on the Fe(110) surface: (a) lateral perspective, (b) superior perspective, and (c) magnified lateral view. Bond lengths are denoted in angstroms (Å).

Figure 7

SCC-DFTB optimized adsorption structures of the hydroxytyrosol (HTR) and tyrosol (TRS) molecules on the Fe(110) surface: (a) lateral perspective, and (b) superior perspective. Bond lengths are denoted in angstroms (Å).

Figure 8

The PDOS for isolated HTR and TRS molecules on the Fe(110) surface, positioned 7 Å above the highest iron layer. The Fermi energy (EF) is selected as the reference point for the zero-energy level.

Figure 9

The PDOS for adsorbed HTR and TRS molecules on the Fe(110) surface. The Fermi energy (EF) is selected as the reference point for the zero-energy level.

Figure 10

The most thermodynamically stable adsorption configurations for HTR molecules on the Fe(110) surface in (a) aqueous environment and (a’) vacuum, as derived from molecular dynamics (MD) simulations. (b,b’) are zoomed views of (a,a’).

Figure 11

The most thermodynamically stable adsorption configurations for TRS molecules on the Fe(110) surface in (a) aqueous environment and (a’) vacuum, as derived from molecular dynamics (MD) simulations. (b,b’) are zoomed views of (a,a’).