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• W893263614 abstract "Quantitative structure property relationship (QSPR) models were developed to predict the adsorption of aromatic compounds by carbon nanotubes (CNTs). Five descriptors chosen by combining self-organizing map and stepwise multiple linear regression (MLR) techniques were used to connect the structure of the studied chemicals with their adsorption descriptor (K∞) using linear and nonlinear modeling techniques. Correlation coefficient (R2) of 0.99 and root-mean square error (RMSE) of 0.29 for multilayered perceptron neural network (MLP-NN) model are signs of the superiority of the developed nonlinear model over MLR model with R2 of 0.93 and RMSE of 0.36. The results of cross-validation test showed the reliability of MLP-NN to predict the K∞ values for the aromatic contaminants. Molar volume and hydrogen bond accepting ability were found to be the factors much influencing the adsorption of the compounds. The developed QSPR, as a neural network based model, could be used to predict the adsorption of organic compounds by CNTs." @default.
• W893263614 created "2016-06-24" @default.
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• W893263614 date "2015-11-01" @default.
• W893263614 modified "2023-10-03" @default.
• W893263614 title "Predicting adsorption of aromatic compounds by carbon nanotubes based on quantitative structure property relationship principles" @default.
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• W893263614 doi "https://doi.org/10.1016/j.molstruc.2015.06.085" @default.
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