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Determination of the Diameter Distribution of Single-Wall Carbon Nanotubes from the Raman G-Band Using an Artificial Neural Network

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Abstract:

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.

Keywords: ARTIFICIAL NEURAL NETWORK; CARBON NANOTUBES; DIAMETER ANALYSIS; RAMAN SPECTROSCOPY

Document Type: Research Article

DOI: https://doi.org/10.1166/jnn.2005.025

Publication date: 2005-02-01

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  • Journal for Nanoscience and Nanotechnology (JNN) is an international and multidisciplinary peer-reviewed journal with a wide-ranging coverage, consolidating research activities in all areas of nanoscience and nanotechnology into a single and unique reference source. JNN is the first cross-disciplinary journal to publish original full research articles, rapid communications of important new scientific and technological findings, timely state-of-the-art reviews with author's photo and short biography, and current research news encompassing the fundamental and applied research in all disciplines of science, engineering and medicine.
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