Empirical Modeling of Nanoindentation of Vertically Aligned Carbon Nanotube Turfs using Intelligent Systems

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.