Superior potentials of hydrazone Schiff bases for efficient corrosion protection of mild steel in 1.0 M HCl
Source abstract
Abstract
Steel is usually cleaned by acid solutions before its application in various industries. Such a treatment increases the possibility of steel corrosion, making the use of inhibitors in the cleaning bath indispensable. On this basis, the introduction of novel, efficient corrosion inhibitors is always of great importance. In the present research, three series of hydrazone Schiff bases were synthesized and investigated as corrosion inhibitors for mild steel in 1.0 M HCl solution. Corrosion experiments included weight loss, electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP), which were performed at different temperatures. SEM images provided the surface analysis of the steel sheets in the absence and presence of the inhibitors. The prepared Schiff bases exhibited inhibition efficiencies higher than 92% at an optimum concentration of 0.01 M. The quantitative structure–activity relationship (QSAR) was used to obtain a correlation between the inhibition efficiency and the quantum chemical parameters of the inhibitors.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Corrosion Behavior and Inhibition · Concrete Corrosion and Durability · Hydrogen embrittlement and corrosion behaviors in metals
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electrochemistry Research Laboratory, Department of Physical Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran
Funding
University of Tabriz
References (35)
Show source-provided references
1. 10.1016/j.carbpol.2020.11611010.1016/j.carbpol.2020.116110 ↗
2. 10.1016/j.molliq.2020.11308510.1016/j.molliq.2020.113085 ↗
3. 10.1016/j.conbuildmat.2020.11937710.1016/j.conbuildmat.2020.119377 ↗
4. 10.1016/j.cdc.2019.10028010.1016/j.cdc.2019.100280 ↗
5. 10.1016/j.surfcoat.2020.12544510.1016/j.surfcoat.2020.125445 ↗
6. 10.1016/j.cdc.2019.10024610.1016/j.cdc.2019.100246 ↗
7. 10.1080/01694243.2020.177197310.1080/01694243.2020.1771973 ↗
8. 10.1080/01694243.2019.158503010.1080/01694243.2019.1585030 ↗
9. 10.1080/01694243.2019.158502910.1080/01694243.2019.1585029 ↗
10. 10.1080/01694243.2019.159177410.1080/01694243.2019.1591774 ↗
11. 10.1016/j.molstruc.2017.08.01810.1016/j.molstruc.2017.08.018 ↗
12. Shabani-Nooshabadi M · J Nanostructures · 2015 · 5 · 423
13. 10.1016/j.jiec.2015.06.02810.1016/j.jiec.2015.06.028 ↗
14. 10.1016/j.arabjc.2017.07.01310.1016/j.arabjc.2017.07.013 ↗
15. 10.1016/j.matpr.2019.08.05710.1016/j.matpr.2019.08.057 ↗
16. 10.1016/j.porgcoat.2019.04.00510.1016/j.porgcoat.2019.04.005 ↗
17. 10.1016/j.porgcoat.2019.05.03510.1016/j.porgcoat.2019.05.035 ↗
18. 10.1016/j.porgcoat.2018.10.00110.1016/j.porgcoat.2018.10.001 ↗
19. 10.1016/j.molstruc.2019.12744210.1016/j.molstruc.2019.127442 ↗
20. 10.1016/j.molstruc.2020.12768510.1016/j.molstruc.2020.127685 ↗
21. 10.1039/C4CP04432K10.1039/C4CP04432K ↗
22. 10.1016/j.molliq.2019.01.06610.1016/j.molliq.2019.01.066 ↗
23. 10.1016/j.physe.2014.10.03510.1016/j.physe.2014.10.035 ↗
24. 10.1016/j.corsci.2018.10.00210.1016/j.corsci.2018.10.002 ↗
25. 10.1016/j.cdc.2020.10033710.1016/j.cdc.2020.100337 ↗
26. 10.1016/j.molstruc.2019.12736210.1016/j.molstruc.2019.127362 ↗
27. 10.1016/j.molliq.2018.08.02510.1016/j.molliq.2018.08.025 ↗
28. 10.1080/01694243.2019.166220210.1080/01694243.2019.1662202 ↗
29. 10.1080/01694243.2019.170756110.1080/01694243.2019.1707561 ↗
30. 10.1016/j.electacta.2004.12.01910.1016/j.electacta.2004.12.019 ↗
31. 10.1016/j.apsusc.2004.05.14310.1016/j.apsusc.2004.05.143 ↗
32. 10.1016/j.ica.2018.09.02210.1016/j.ica.2018.09.022 ↗
33. Ashassi-Sorkhabi H · J Adhes Sci Technol · 2019 · 34 · 961
34. 10.1016/j.corsci.2019.04.04010.1016/j.corsci.2019.04.040 ↗
35. 10.1016/0254-0584(94)01436-K10.1016/0254-0584(94)01436-K ↗