Issue 1, 2019

Predictive quantitative structure–property relationship (QSPR) modeling for adsorption of organic pollutants by carbon nanotubes (CNTs)

Abstract

Nanotechnology has introduced a new generation of adsorbents like carbon nanotubes (CNTs), which have drawn a widespread attention due to their outstanding ability for the removal of various inorganic and organic pollutants. The goal of this study was to develop regression-based quantitative structure–property relationship (QSPR) models for organic pollutants and organic solvents using only easily computable 2D descriptors to explore the key structural features essential for adsorption to multi-walled CNTs and improve the dispersibility index of single-walled CNTs. The statistical results of the developed models showed good quality and predictivity based on both internal and external validation metrics (dataset 1: R2 range of 0.893–0.920, Q2(LOO) range of 0.863–0.895, Q2F1 range of 0.887–0.919; dataset 2: R2 range of 0.793–0.845, Q2(LOO) range of 0.743–0.798, Q2F1 range of 0.783–0.890; dataset 3: R2 = 0.830, Q2(LOO) = 0.775, Q2F1 = 0.945). We have also tried to explore whether the quality of the predictions of test set compounds can be enhanced through an “intelligent” selection of multiple models using the “Intelligent consensus predictor” tool. The consensus results suggested that the consensus predictivity of the test set compounds gave better results than those from the individual MLR models based on different criteria (dataset 1: Q2F1 = 0.935, Q2F2 = 0.935, MAE(95%) = good; dataset 2: Q2F1 = 0.887, Q2F2 = 0.879, MAE(95%) = good). The contributed descriptors obtained from different models suggested that the organic pollutants may adsorb to the CNTs through hydrogen bonding interactions, π–π interactions, hydrophobic interactions and electrostatic interaction. Based on the observations obtained from the developed models, we have inferred that the adsorption of the organic pollutants onto the CNTs can be enhanced by the following factors: a higher number of aromatic rings, high unsaturation or electron richness of molecules, the presence of polar groups substituted in the aromatic ring, the presence of oxygen and nitrogen atoms, the size of the molecules, and the hydrophobic surface of the molecules. On the other hand, the presence of C–O groups, aliphatic primary alcohols and the presence of chlorine atoms may retard the adsorption of organic pollutants. The results also suggest that the organic solvents bearing the >N- fragment, a higher degree of branching (compactness), polar solvents with low donor number and lower ionization potential may be better solvents for enhancing the dispersibility of single-walled CNTs.

Graphical abstract: Predictive quantitative structure–property relationship (QSPR) modeling for adsorption of organic pollutants by carbon nanotubes (CNTs)

Supplementary files

Article information

Article type
Paper
Submitted
22 sep. 2018
Accepted
16 nóv. 2018
First published
16 nóv. 2018

Environ. Sci.: Nano, 2019,6, 224-247

Predictive quantitative structure–property relationship (QSPR) modeling for adsorption of organic pollutants by carbon nanotubes (CNTs)

J. Roy, S. Ghosh, P. K. Ojha and K. Roy, Environ. Sci.: Nano, 2019, 6, 224 DOI: 10.1039/C8EN01059E

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