Intermolecular interactions of substituted benzenes on multi-walled carbon nanotubes grafted on HPLC silica microspheres and interaction study through artificial neural networks

Purified multi-walled carbon nanotubes (MWCNTs) grafted onto silica microspheres by gamma-radiation were applied as a HPLC stationary phase for investigating the intermolecular interactions between MWCNTs and substituted benzenes. The synthetic route, simple and not requiring CNTs derivatization, involved no alteration of the nanotube original morphology and physical–chemical properties. The affinity of a set of substituted benzenes for the MWCNTs was studied by correlating the capacity factor (k′) of each probe to its physico-chemical characteristics (calculated by Density Functional Theory). The correlation was found through a theoretical approach based on feedforward neural networks. This strategy was adopted because today these calculations are easily affordable for small molecules (like the analytes), and many critical parameters needed are not known. This might increase the applicability of the proposed method to other cases of study. Moreover, it was seen that the normal linear fit does not provide a good model. The interaction on the MWCNT phase was compared to that of an octadecyl (C18) reversed phase, under the same elution conditions. Results from trained neural networks indicated that the main role in the interactions between the analytes and the stationary phases is due to dipole moment, polarizability and LUMO energy. As expected for the C18 stationary phase correlation, is due to dipole moment and polarizability, while for the MWCNT stationary phase primarily to LUMO energy followed by polarizability, evidence for a specific interaction between MWCNTs and analytes. The CNT-based hybrid material proved to be not only a chromatographic phase but also a useful tool to investigate the MWCNT-molecular interactions with variously substituted benzenes.