A New Comprehensive Modeling Approach for Organic Corrosion Inhibitors

Corrosion poses a daunting challenge to the environmental sustainability of the built infrastructure in the oil and gas industry. The primary defense against corrosion of the infrastructure material (e.g. structural steel or aluminum) relies on the use of protective organic coatings. In typical practice, the metal surfaces are first treated with a chemical agent to passive or seal the surface and then coated with an organic coating that contains a corrosion inhibitor. While the overall durability of protective coatings has improved remarkably in recent years, the development of more effective corrosion inhibitors has not been able to keep pace. The efficient identification of new, non-toxic, and effective corrosion inhibitors requires the examination of numerous variables associated with the molecular structure and operational environment. A reliable computational method could provide a more efficient approach to select better inhibitor molecules from the chemical universe and even design optimum inhibitor chemistries. Many attempts are done on corrosion data modeling, in general, electronic parameters such as HOMO and LUMO energies^1-7 have been found to have high correlation with the corrosion inhibitor efficiency, but this approach has generally been limited to homologous sets of molecules. However, there is no consensus for the direct correlation between the efficiency of an inhibitor compound and individual molecular parameters⁸. Most importantly, each new structure-activity (SA) study results in a new model^8,9. Therefore, a rational and comprehensive approach for computational modeling of corrosion inhibitors is still needed. Recently developed QM-QSAR is based on both local and molecular descriptor parameters.  Charge distribution is one of the most important local features used in QM-QSAR models.

                Recently, a better modeling approach was introduced¹⁰ that is:  (1) robust, (2) usable on any molecule, (3) predicts the nature of the bond formed with the metal surface, i.e., physical, covalent, or dative bonding, (4) correlates with the amount of surface coverage by the inhibitor, and (5) predicts molecule properties using molecular fragments to eliminate the need for a full quantum mechanical calculation of each new molecule¹⁰. This modeling approach was established based on Langmuir adsorption isotherm and also quantum chemical bond descriptor parameters (Ñr(r) and Ѳr(r)) based on Bader’s Quantum Theory of Atoms in Molecules (QTAIM)¹¹. This unique corrosion data modeling approach solved the missing link e.g. entropic contribution by organic molecules on corrosion with respect to state of art research for corrosion data modeling. This modeling approach was used to model more than 40 experimental data sets.

A detailed comprehensive modeling approach and its strength over other models will be discussed and presented during the presentation.

References

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[3] A. Chakrabarti, J Br Corros., 19, 124 (1984).

[4] G. Gece, Corros. Sci., 50, 2981 (2008).

[5] K. F. Khaled, Corros. Sci. 53, 3457 (2011).

[6] J. Cioslowski J.  in Encyclopedia of Computational Chemistry, Vol. 5  ed. Schleyer, R. V.P.; N. L. Allinger, T. Clark, J. Gasteiger, A. P.Kollman, F. H. III Schaefer, p. 892, Wiley, New York, (1998).

[7] H. S. El Ashry, E. A. Nemr, S.  A. Essawy and S.Ragab, Prog. Org. Coatings, 61, 11 (2008).

[8] N. Khalil,  Electrochim Acta., 48, 2635 (2003).

[9] S. G. Zhang, W.  Lei, M. G. Xia and F. Y. Wang, Theo Chem., 732, 173 (2005).

[10]R.L.Cook,https://www.corrdefense.org/External/Vie List.aspx?ListID=690, 11/15/2013.

[11] C. F. Matta and R. J. BoydThe “Quantum Theory of Atoms in Molecules”. Edited by Che´rif F. Matta and Russell J. Boyd, pp 1-43 (2007).