Open AccessQSAR to Design an Effective Eco-friendly Inhibitor Model from Chitosan Derivatives Sea-food Waste based on DFT Calculations and Experimental Study
Author(s) -
S. El Sayed Doaa,
Ahmed Mohamed
Publication year - 2021
Publication title -
current chinese chemistry
Language(s) - English
Resource type - Journals
eISSN - 2666-0016
pISSN - 2666-0008
DOI - 10.2174/2666001601999201006125255
Subject(s) - quantitative structure–activity relationship , chemistry , dipole , molecular descriptor , density functional theory , chitosan , linear regression , corrosion , environmentally friendly , computational chemistry , thermodynamics , stereochemistry , organic chemistry , mathematics , physics , statistics , ecology , biology
Background: A density functional theory (DFT) study of some selected eco-friendlychitosan derivatives was performed, recently used as corrosion inhibitors for steel in 0.1M and 0.5MHCl. Correlation between observed and predicted inhibition efficiencies is based on QSAR by somestatistical calculations. Methods: We extracted the optimum molecular descriptors for the chitosan derivatives group understudy and it was found that these descriptors have a proper effect on increasing the inhibitionefficiency that was proved by applying the theoretical calculations (non-linear regression) on twomodels of chitosan derivatives (ChI and ChII). The quantum chemical descriptors most relevant tothe 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 oncommon 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 inhibitioneffect on steel at the same environment of the chitosan derivatives group under study. Conclusion: Experimental explanation showed good results from modelling prediction, where thecorrosion rate decreases markedly with increasing the concentration of the designed inhibitors tillthe optimum concentration where the rate becomes constant. SEM on the optimum inhibitorconcentration proved the high inhibition efficiency obtained.