Prediction of the adsorption coefficients of some aromatic compounds on multi-wall carbon nanotubes by the Monte Carlo method.

In this investigation, quantitative structure-property relationship (QSPR) modelling of adsorption coefficients of 69 aromatic compounds on multi-wall carbon nanotubes (MWCNTs) was studied using the Monte Carlo method. QSPR models were calculated with CORAL software, and optimal descriptors were calculated with the simplified molecular input line entry system (SMILES) and hydrogen-suppressed molecular graphs (HSGs). The aromatic compound data set was randomly split into training, invisible training, calibration and validation sets. Analysis of three probes of the Monte Carlo optimization with three random splits was done. The results from three random splits displayed robust, very simple, predictable and reliable models for the training, invisible training, calibration and validation sets with a coefficient of determination (r2) equal to 0.9463-0.8528, 0.9020-0.8324, 0.9606-0.9178 and 0.9573-0.8228, respectively. As a result, the models obtained help to identify the hybrid descriptors for the increase and the decrease of the adsorption coefficient of aromatic compounds on MWCNTs. This simple QSPR model can be used for the prediction of the adsorption coefficient of numerous aromatic compounds on MWCNTs.