Empirical Modeling of Nanoindentation of Vertically Aligned Carbon Nanotube Turfs using Adaptive Neuro-Fuzzy System

Abstract

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 and irregularity 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 propose a new methodology 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 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