Experimental characterization, machine learning analysis and computational modelling of the high effective inhibition of copper corrosion by 5-(4-pyridyl)-1,3,4-oxadiazole-2-thiol in saline environment.

Abstract

An oxadiazole derivative with functional groups favouring its adsorption on copper surface, namely 5-(4-Pyridyl)-1,3,4-oxadiazole-2-thiol, has been explored as potential inhibitor of copper corrosion in 3.5 wt.% NaCl. Electrochemical evaluation by electrochemical impedance spectroscopy, potentiodynamic polarization and SVET reveals inhibition efficiencies exceeding 99%. Surface microscopy inspection and spectroscopic analysis by Raman, SEM-EDX and XPS highlight the formation of a compact barrier film responsible for long-lasting protection, that is mainly composed of the organic molecules. Machine Learning algorithms used in combination with Raman spectroscopy data were used successfully for the first time in corrosion studies to allow discrimination between corroded and inhibitor-protected metal surfaces. Quantum Chemistry calculations in aqueous solution and Molecular Dynamic studies predict a strong interaction between copper and the thiolate group and an extensive coverage of the metal surface, responsible for the excellent protection against corrosion.