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Theoretical evaluation of some α-amino acids for corrosion inhibition of copper in acidic medium: DFT calculations, Monte Carlo simulations and QSPR studies
- Source :
- Journal of King Saud University: Science, Vol 32, Iss 1, Pp 163-171 (2020)
- Publication Year :
- 2020
- Publisher :
- Elsevier, 2020.
-
Abstract
- The quantitative structure–property relationship (QSPR) models of the inhibition efficiency of seventeen α-amino acids for copper in acidic medium to their calculated reactivity indicators were developed. DFT calculations and Monte Carlo simulations were employed to find out these indicators. Both multi-linear regression (MLR) and artificial neural network (ANN) methods were employed. The most relevant global descriptors were selected using the simulated annealing algorithm. The QSPR studies showed that the inhibiting performance of the investigated compounds was influenced by their electronegativity, LUMO energy, fraction of electron transferred and total negative charge. The results show that the ANN based model exhibits a great predictive performance compared with MLR model according to correlation coefficient and the root-mean-squared error. In addition, this indicates that the corrosion inhibition of copper by these α-amino acids is mainly a complex phenomenon. Moreover, by analysis of local reactivity indicators and using the ANN constructed model, ten new designed derivative compounds with their predicted inhibition efficiency were proposed. Keywords: Corrosion, Modeling, DFT, Monte Carlo, Simulated annealing, QSPR
- Subjects :
- Quantitative structure–activity relationship
Multidisciplinary
Correlation coefficient
Chemistry
Monte Carlo method
chemistry.chemical_element
02 engineering and technology
Derivative
010501 environmental sciences
021001 nanoscience & nanotechnology
01 natural sciences
Copper
Electronegativity
Computational chemistry
Reactivity (chemistry)
0210 nano-technology
lcsh:Science (General)
HOMO/LUMO
0105 earth and related environmental sciences
lcsh:Q1-390
Subjects
Details
- Language :
- English
- ISSN :
- 10183647
- Volume :
- 32
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Journal of King Saud University: Science
- Accession number :
- edsair.doi.dedup.....acc1f6074db5f71060bb7a820ff058d3