RT info:eu-repo/semantics/article
T1 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.
A1 Souto, Ricardo Manuel
A1 Varvara, Simona
A1 Berghian-Grosan, Camelia
A1 Bostan, Roxana
A1 Lucacel Ciceo, Raluca
A1 Salarvand, Zohreh
A1 Talebian, Milad
A1 Raeissi, Keyvan
A1 Izquierdo Pérez, Javier
A2 Química
A2 Grupo de Electroquímica y CorrosiónInstituto de Materiales y Nanotecnología
K1 oxadiazole derivative
K1 copper corrosion inhibition
K1 electrochemical impedance spectroscopy
K1 Raman spectroscopy
K1 Machine Learning
K1 SVET
K1 XPS
K1 Molecular Dynamics
AB 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 exceeding99%. Surface microscopy inspection and spectroscopic analysis by Raman, SEM-EDX and XPShighlight 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.
YR 2021
FD 2021
LK http://riull.ull.es/xmlui/handle/915/25549
UL http://riull.ull.es/xmlui/handle/915/25549
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 24-nov-2024