QSAR to Design an Effective Eco-friendly Inhibitor Model from Chitosan Derivatives Sea-food Waste based on DFT Calculations and Experimental Study

(E-pub Ahead of Print)

Author(s): Doaa S. El-Sayed*, Mohamed A. Makhyoun

Journal Name: Current Chinese Chemistry

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Abstract:

Background: A density functional theory (DFT) study of some selected eco-friendly chitosan derivatives, re-cently used as corrosion inhibitors for steel in 0.1M and 0.5M HCl. Correlation between observed and predicted inhibition efficiencies are based on QSAR by some statistical calculations.

Methods and Results: 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). 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 become 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.

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(E-pub Ahead of Print)
DOI: 10.2174/2666001601999201006125255

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