Adaptive Neuro-Fuzzy Modeling of Mechanical Behavior for Vertically Aligned Carbon Nanotube Turfs

Abstract

Abstract: 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 e®ect of structural morphologies on nanomaterial properties
using Raman spectroscopy.

Key words: Adaptive neuro-fuzzy, Carbon nanotubes, Image analysis, Nanoindentation, Raman spectroscopy

Mohammad Al-Khedher, Charles Pezeshki, Jeanne McHale, Fritz Knorr. Adaptive Neuro-Fuzzy Modeling of Mechanical Behavior for Vertically Aligned Carbon Nanotube Turfs[J]. J Mater Sci Technol, 2011, 27(4): 301-308.

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