Experimental and DFT insights into nitrogen and sulfur co-doped carbon nanotubes for effective desulfurization of liquid phases: Equilibrium & kinetic study

Herein, nitrogen and sulfur co-doped carbon nanotubes (NS-CNT) adsorbents were synthesized via the chemical vapor deposition technique at 1000 degrees C by employing the camphor, urea and sulfur trioxide pyridine. In this study, desulfurization of two types of mercaptans (dibenzothiophene (DBT) and tertiary butyl mercaptan (TBM) as nonlinear and linear forms of mercaptan) was studied. In this regard, a maximum capacity of NS-CNT was obtained as 106.9 and 79.4 mg/g and also the removal efficiencies of 98.6% and 88.3% were achieved after 4 h at 298K and 0.9 g of NS-CNT for DBT and TBM, respectively. Characterization of the NS-CNTs was carried out through exploiting scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and elemental analysis (CHN). The isotherm equilibrium data could be ascribed to the Freundlich nonlinear regression form and the kinetic data was fitted by nonlinear form of the pseudo second order model. The negative values of Delta S-0, Delta H-0 and Delta G(0) specify that the adsorption of both types of mercaptans was a natural exothermic process with a reduced entropy. Maintenance of more than 96% of the adsorption capacity even after nine cycles suggest the NS-CNT as a superior adsorbent for mercaptans removal in the industry. Density functional theory (DFT) calculations were also performed to peruse the effects of S/N co-doping and carbon monovacancy defects in CNTs toward the adsorption of DBT and TBM.

Automated summary

Nitrogen and sulfur co-doped carbon nanotubes (NS-CNT) adsorbents were successfully synthesized and applied for the removal of two types of mercaptans, demonstrating high removal efficiency and cycling stability. Characterization of NS-CNT through various techniques revealed that the adsorption process could be described using the Freundlich model and pseudo second order model.