Determination of the diameter distribution of single-wall carbon nanotubes from the Raman G-band using an artificial neural network

A novel, artificial neural network-based method is now available for obtaining the mean diameter of single wall carbon nanotube (SWCNT) samples from the diameter dispersive features of their Raman G-band. The method is demonstrated here for six different diameter SWCNT samples and 14 different excitation wavelengths. With an adequately large pool of standard nanotube samples, the suggested method is a useful complementary technique for SWCNT diameter analysis as it is capable of rapid diameter evaluation without prior knowledge of the relevant phonon dispersion relations.