Empirical Modeling of Nanoindentation of Vertically Aligned Carbon Nanotube Turfs using Intelligent Systems
McHale, J. L.
Knorr, F. J.
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Establishing analytical models at the nanoscale to interpret the mechanical and structural properties of vertically aligned carbon nanotubes (VACNTs) is complicated due to the nonuniformity in quality of as-grown samples and the lack of an accurate procedure to evaluate structural properties of nanotubes in these samples. In this paper, we present a comparative study of empirical methodologies to investigate the correlation between indentation resistance of multi-wall carbon nanotube (MWCNT) turfs, Raman features and the morphological properties of the turf structure using adaptive neuro-fuzzy system and probabilistic neural networks. Both methodologies provide comprehensive and innovative approaches for phenomenological modeling of VACNTs morphologies, mechanical properties and Raman Spectra using intelligent-based systems.