Combined Electrochemical, Raman Analysis and Machine Learning Assessments of the Inhibitive Properties of an 1,3,4-Oxadiazole-2-Thiol Derivative against Carbon Steel Corrosion in HCl Solution

Simona Varvara; Camelia Berghian-Grosan; Gianina Damian; Maria Popa; Florin Popa

doi:10.3390/ma15062224

Combined Electrochemical, Raman Analysis and Machine Learning Assessments of the Inhibitive Properties of an 1,3,4-Oxadiazole-2-Thiol Derivative against Carbon Steel Corrosion in HCl Solution

Materials (Basel). 2022 Mar 17;15(6):2224. doi: 10.3390/ma15062224.

Authors

Simona Varvara¹, Camelia Berghian-Grosan², Gianina Damian¹, Maria Popa¹, Florin Popa³

Affiliations

¹ Department of Cadastre, Civil and Environmental Engineering, "1 Decembrie 1918" University of Alba Iulia, 15-17 Unirii Street, 510009 Alba Iulia, Romania.
² National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donat Street, 400293 Cluj-Napoca, Romania.
³ Materials Science and Engineering Department, Technical University of Cluj-Napoca, 103-105 Muncii Avenue, 400641 Cluj-Napoca, Romania.

Abstract

The inhibiting properties of 5-(4-pyridyl)-1,3,4-oxadiazole-2-thiol (PyODT) on the corrosion of carbon steel in 1.0 M HCl solution were investigated by potentiodynamic polarization, electrochemical impedance spectroscopy, Raman spectroscopy, and SEM-EDX analysis. An approach based on machine learning algorithms and Raman data was also applied to follow the carbon steel degradation in different experimental conditions. The electrochemical measurements revealed that PyODT behaves as a mixed-type corrosion inhibitor, reaching an efficiency of about 93.1% at a concentration of 5 mM, after 1 h exposure to 1.0 M HCl solution. Due to the molecular adsorption and structural organization of PyODT molecules on the C-steel surface, higher inhibitive effectiveness of about 97% was obtained at 24 h immersion. The surface analysis showed a significantly reduced degradation state of the carbon steel surface in the presence of PyODT due to the inhibitor adsorption revealed by Raman spectroscopy and the presence of N and S atoms in the EDX spectra. The combination of Raman spectroscopy and machine learning algorithms was proved to be a facile and reliable tool for an incipient identification of the corrosion sites on a metallic surface exposed to corrosive environments.

Keywords: 1,3,4-oxadiazole derivative; Raman spectroscopy; SEM-EDX; carbon steel; corrosion; electrochemical impedance spectroscopy; machine learning algorithms; polarization curves.