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

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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)

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Publication details

The article was received on 22 Sep 2018, accepted on 16 Nov 2018 and first published on 16 Nov 2018


Article type: Paper
DOI: 10.1039/C8EN01059E
Citation: Environ. Sci.: Nano, 2019, Advance Article
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    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, Advance Article , DOI: 10.1039/C8EN01059E

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