Issue 46, 2013

Development of a 3D QSPR model for adsorption of aromatic compounds by carbon nanotubes: comparison of multiple linear regression, artificial neural network and support vector machine

Abstract

Adsorption coefficients of 39 aromatic compounds onto multi-walled carbon nanotubes have been compiled. To understand the relationship between adsorption coefficients and physicochemical properties of aromatic compounds, a 3D quantitative structure–property relationship (QSPR) model was developed by the utilization of 3D molecular structures of 39 aromatic compounds. A Monte Carlo computational algorithm was utilized to generate 3D molecular descriptors and physicochemical properties for the QSPR model. Of the physicochemical descriptors: log Kow, number of nitrogen and oxygen atoms and number of atoms in rings present positive correlation. However, the dipole moment of the molecule and number of hydrogen bonds accepted by the solute present negative correlations. In the model development process, three different learning approaches, multiple linear regression (MLR), artificial neural network (ANN) and support vector machine (SVM), were used. The validation results showed that SVM- and ANN-based models resulted in a better agreement between predicted and measured values, with the coefficient of determination (R2) of 0.8317 and 0.7829, than the MLR-based model with R2 of 0.5093.

Graphical abstract: Development of a 3D QSPR model for adsorption of aromatic compounds by carbon nanotubes: comparison of multiple linear regression, artificial neural network and support vector machine

Article information

Article type
Review Article
Submitted
14 7月 2013
Accepted
18 9月 2013
First published
18 9月 2013

RSC Adv., 2013,3, 23924-23934

Development of a 3D QSPR model for adsorption of aromatic compounds by carbon nanotubes: comparison of multiple linear regression, artificial neural network and support vector machine

Q. ‘. Wang, O. G. Apul, P. Xuan, F. Luo and T. Karanfil, RSC Adv., 2013, 3, 23924 DOI: 10.1039/C3RA43599G

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements