摘要
Several characterization methods have been developed to investigate the mechanical and structural properties of vertically aligned carbon nanotubes （VACNTs）. Establishing analytical models at nanoscale to interpret these properties is complicated due to the nonuniformity and irregularity in quality of as-grown samples. In this paper, we propose a new methodology to investigate the correlation between indentation resistance of multi-wall carbon nanotube （MWCNT） turfs, Raman spectra and the geometrical properties of the turf structure using adaptive neuro-fuzzy phenomenological modeling. This methodology yields a novel approach for modeling at the nanoscale by evaluating the effect of structural morphologies on nanomaterial properties using Raman spectroscopy.
Several characterization methods have been developed to investigate the mechanical and structural properties of vertically aligned carbon nanotubes （VACNTs）. Establishing analytical models at nanoscale to interpret these properties is complicated due to the nonuniformity and irregularity in quality of as-grown samples. In this paper, we propose a new methodology to investigate the correlation between indentation resistance of multi-wall carbon nanotube （MWCNT） turfs, Raman spectra and the geometrical properties of the turf structure using adaptive neuro-fuzzy phenomenological modeling. This methodology yields a novel approach for modeling at the nanoscale by evaluating the effect of structural morphologies on nanomaterial properties using Raman spectroscopy.
基金
supported in part by the National Science Foundation under Grant No. CTS-0404370
the US Army SMDC under Contract DASG60-02-C-0084
