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.
Kukovecz
A
A
Department of Applied and Environmental Chemistry, University of Szeged, Rerrich Bela ter 1, H-6720 Szeged, Hungary.
Smolik
M
M
Bokova
S
S
Kataura
H
H
Achiba
Y
Y
Kuzmany
H
H
D001185:Artificial Intelligence; D002244:Carbon / Q000737:chemistry*; D005240:Feasibility Studies; D036103:Nanotechnology / Q000379:methods; D037742:Nanotubes, Carbon / Q000737:chemistry*; D016571:Neural Networks, Computer; D013059:Spectrum Analysis, Raman
D037742:Nanotubes, Carbon; D002244:Carbon