Carbon nanotubes (CNTs) are emerging nanomaterials exhibiting strong adsorption affinities to organic chemicals. Voluminous research was focused on the removal of organic contaminants by CNTs. However available data still covers a small portion of all synthetic organic compounds. Therefore, a predictive model is of great significance in predicting the adsorption of organic contaminants without conducting any experiment. In addition, models can provide insights to the complex adsorption interactions between CNTs and organic compounds. In the present study, statistical based mathematical modeling approaches were applied to develop predictive equations for adsorption of aromatic contaminants by CNTs. Adsorption isotherms of 69 aromatic organic compounds by CNTs were compiled from 17 different studies. Quantitative structure-activity relationships (QSAR) and linear solvation energy relationships (LSER) were examined and fitting equations were obtained by multiparameter regression. Equations were externally validated with a new set of compounds. In depth analysis of model parameters were performed to better understand the intermolecular interactions and relative contribution of these interactions to overall CNT adsorption.

Predictive model development for adsorption of organic contaminants by carbon nanotubes

243rd National Meeting (2012)

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