Fast Prediction of Oxygen Reduction Reaction Activity on Carbon Nanotubes with a Localized Geometric Descriptor
Oxygen reduction reaction (ORR) is a process of primary importance in fuel cell technology. Efficiency of carbon-based materials in this field is already attested by many experimental studies, especially when doped with nitrogen or boron. In this work, we proposed a localized geometric descriptor, based on the pyramidalization angle to report ORR activity on carbon nanotubes (CNTs). Our descriptor reflects the local curvature of the surface and the torsion of the π orbital system. We showed the surface reactivity is directly related to the pyramidalization angle at the active sites. Nitrogen and boron doping makes it possible to reach a low overpotential for weakly curved surface, whereas for undoped surfaces ideal ORR activity is only reached for highly curved CNTs. Consequently, the optimal size of the nanotube is determined by the doping type. Hence, we demonstrated a high statistical quality correlation between adsorption energies of surface species and the pyramidalization angle at the active site. Our descriptor enables ready identification of the optimal diameter and the best doping type for the CNT surfaces, which is not possible with usual descriptors such as OH species adsorption. As a result, ORR geometric descriptors are very promising since their performance is comparable to electronic descriptors. In addition, they are less time-demanding in computation, and they are less sensitive to the accuracy of the method of calculation.