Abstract
Background: A density functional theory (DFT) study of some selected eco-friendly
chitosan derivatives was performed, recently used as corrosion inhibitors for steel in 0.1M and 0.5M
HCl. Correlation between observed and predicted inhibition efficiencies is based on QSAR by some
statistical calculations.
Methods: We extracted the optimum molecular descriptors for the chitosan derivatives group under
study and it was found that these descriptors have a proper effect on increasing the inhibition
efficiency that was proved by applying the theoretical calculations (non-linear regression) on two
models of chitosan derivatives (ChI and ChII). The quantum chemical descriptors most relevant to
the corrosion inhibitors potential effect have been calculated in the aqueous phase. They include:
EHOMO, ELUMO, dipole moment (D), molecular area (MA), molecular volume (MV), the charge on
common oxygen (O Charge), the charge on common nitrogen (N Charge), nuclear repulsion energy
(NRE), final single point energy (E) and total positive charge (TPC).
Results: The optimum parameters resulted using multiple linear regression are EHOMO, CCO, CCN,
and D. Using these optimum parameters, the models designed show good results in their inhibition
effect on steel at the same environment of the chitosan derivatives group under study.
Conclusion: Experimental explanation showed good results from modelling prediction, where the
corrosion rate decreases markedly with increasing the concentration of the designed inhibitors till
the optimum concentration where the rate becomes constant. SEM on the optimum inhibitor
concentration proved the high inhibition efficiency obtained.
Keywords:
DFT calculations, QSAR, statistical analysis, experimental study, chitosan derivatives, corrosion inhibition, model
designing.
Graphical Abstract
[1]
Sheldon, R.A. Metrics of green chemistry and sustainability: past, present, and future. Chem. Eng., 2018, 6(1), 32-48.
[7]
Hiano, S.; Inui, H.; Kosaki, H.; Uno, Y.; Toda, T. Biotechnology and bioactive polymers; Springer, 1994.
[11]
Abdallah, M.; Asghar, B.H.; Zaafarany, I.; Fouda, A.S. The inhibition of carbon steel corrosion in hydrochloric acid solution using some phenolic compounds. Int. J. Electrochem. Sci., 2012, 7(1), 282-304.
[13]
Alsabagh, A.M.; Elsabee, M.Z.; Moustafa, Y.M.; Elfky, A.; Mors, R.E. Corrosion inhibition efficiency of some hydrophobically modified chitosan surfactants in relation to their surface active properties. Egyptian. J. Petrol., 2014, 23, 349-359.
[15]
Darmokoesoemo, H.; Suyanto, S.; Anggara, L.S.; Amenaghawon, A.N.; Kusuma, H.S. Application of carboxymethyl chitosan-benzaldehyde as anticorrosion agent on steel. Int. J. Chem. Eng., 2018, 2, 9.
[18]
El-Mahdy, G.A.; Atta, A.M.; Al-Lohedan, H.A.; Ezzat, A.O. Synthesis of water soluble hyperbranched poly (amine-ester) as corrosion inhibitors for steel. Int. J. Electrochem. Sci., 2015, 10, 5812-5826.
[19]
Toy, M.; Tanak, H.; Theo, J. DFT quantum chemical studies on 1-[n-(2-pyridyl) aminomethylidene]-2 (1h)-naphtalenone. Comput. Chem., 2012, 11, 745.
[20]
Frank, N. The ORCA program system. Interdiscip. Rev. Comput. Mol. Sci., 2012, 8, 73-78.
[27]
A)Mills, N. ChemDraw Ultra 10.0 CambridgeSoft, 100 CambridgePark Drive, Cambridge, MA. J. Adm. Soc., 2006, 128(41), 13649-13650.
B)Zhurko, G.A.; Zhurko, D.A. ChemCraft: Tool for Treatment of Chemical Data, Lite Version Build 08 (freeware) 2005.
[30]
Le, T.; Epa, N.V.; Burden, F.R.; Winkler, D.A. Quantitative structure property relationship modeling of diverse materials properties. Chem. Rev., 2012, 112, 2889-2919.
[31]
Gupta, M.K.; Gupta, S.; Rawal, R.K. Impact of artificial neural networks in qsar and computational modeling. In: M. Puri, Y Pathak, VK Sutariya, S Tipparaju and W Moreno. Eds. Artificial Neural Network for Drug Design, Delivery and Disposition. Eds; Elsevier (Academic Press): London UK, 2015; pp. 153-179.
[33]
Norman, H.; Dale, H.; Hull, C.H. SPSS Statistical Package for the Social Sciences, 1970.
