Corrosion Inhibitor Potential of Four Phenyltetrazoles Derivatives using Density Functional Theory and Quantitative Structure-Activity Relationships Approach

The corrosion inhibition potential of four phenyltetrazole derivatives (M 1-4) were investigated by theoretical methods. The efficiency of corrosion inhibitors depends on many quantum chemical descriptors: chemical hardness, softness, electronegativity, dipole moments, molecular volume, surface area...

Full description

Saved in:
Bibliographic Details
Published inJournal of applied science & environmental management Vol. 23; no. 4; pp. 665 - 671
Main Authors Ogunyemi, BT, Adejoro, IA
Format Journal Article
LanguageEnglish
Published Port Harcourt Dr. Michael Horsfall Jnr, University of Port Harcourt, Department of Pure and Industrial Chemistry 01.04.2019
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:The corrosion inhibition potential of four phenyltetrazole derivatives (M 1-4) were investigated by theoretical methods. The efficiency of corrosion inhibitors depends on many quantum chemical descriptors: chemical hardness, softness, electronegativity, dipole moments, molecular volume, surface area, as well as electronic orbital energies: EHOMO (highest occupied molecular orbital energy); ELUMO (lowest unoccupied molecular orbital energy) and energy gap (AE) calculated from DFT approach. A statistical ordinary least square method was used to perform regression analysis that determined the correlations between the calculated descriptors and the experimental inhibition efficiency for phenyltetrazole derivatives while the QSAR model developed was used to predict their corrosion inhibition efficiency. The predicted corrosion inhibition efficiencies of the phenyltetrazoles derivatives correspond well to the experimental measurements. The correlation coefficient was 0.9984 and the root mean square error (%), was 1.36. With the embedded QSAR model, the corrosion inhibition efficiencies of two novel phenyltetrazole derivatives (M5-6) were predicted.
ISSN:1119-8362
DOI:10.4314/iasem.v23i4.15