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

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    Date
    2011-04
    Type
    Article
    Author
    Al-Khedher, Mohammad
    Pezeshki, Charles
    McHale, Jeanne
    Knorr, Fritz
    Metadata
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    Abstract
    Several characterization methods were developed to study 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.
    URI
    https://dspace.adu.ac.ae/handle/1/436
    DOI
    https://doi.org/10.1016/S1005-0302(11)60066-2
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    • Mechanical Engineering

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