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.

Partial radial distribution functions for a two-component glassy solid, GeSe[Formula: see text], from scattering experimental data using an artificial intelligence framework.

The Hopfield neural network has been applied successfully to solve ill-posed inverse problems in simple monoatomic liquids structure using scattering experimental data to retrieve the radial distribution function, g(r), and direct correlation function, C(r). In this work, the method was extended to a more complex system: a two-component glassy solid, GeSe3. To acquire results with correct peak intensities and behavior for large values of r, it was necessary to carry out the calculations a few times by adjusting the initial conditions to solve a set of coupled equations. However, the new initial conditions are simple and can be defined based on the results obtained at each run. In this sense, the method robustness is also evident while retrieving the radial distribution function for more complex systems from experimental data.