Modelling the adsorption on oxidized carbon nanotubes using a statistical mechanics approach.

Carbon nanotubes have several applications including the removal of pollutants via adsorption. Many studies were carried out in order to evaluate how the functionalization of these materials improves the efficiency of the process. However, a better understanding of the mechanisms involved in adsorption on nanotubes is still needed. In this work it was evaluated how the oxidation of nanotubes influences the adsorption of a model molecule (methylene blue). For this purpose, a realistic model described on the grand canonical ensemble was used. In this approach, the experimental isotherms were adjusted and provided relevant information about the process, such as the number of layers and the orientation of the adsorbed molecules. In addition, since the treatment is based on statistical mechanics, it was possible to calculate the configurational entropy and Gibbs free energy of the process.

Adsorbate-adsorbent potential energy function from second virial coefficient data: a non-linear Hopfield Neural Network approach.

The Hopfield Neural Network has been successfully applied to solve ill-posed inverse problems in different fields of chemistry and physics. In this work, the non-linear approach for this method will be applied to retrieve the empirical parameters of potential energy function, [Formula: see text], between adsorbate and adsorbent from experimental data. Since the adsorption data is related to the second virial coefficient and therefore to [Formula: see text] through an integral equation, the Hopfield Neural Network will be used to find the best parameters which fits the experimental data. Initially simulated results will be analyzed to verify the method performance for data sets with and without noise addition. Then, experimental data for adsorption of propionitrile on activated carbon will be treated. Results presented here corroborate to the robustness of this method.