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.

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.

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, GeSe. 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.