Quality classification via Raman identification of carbon nanotube bundles using artificial neural networks

Abstract

One of the major obstacles for successful mass-production of carbon nanotubes (CNTs) is performing quick and precise characterization of the properties of a given batch of nanotubes. In this paper, we have identified a set of intermediate steps that will lead to a comprehensive, scalable set of procedures for analyzing nanotubes. The proposed methodology was originated with data processing of Raman spectra of multi-wall carbon nanotubes (MWCNT) turfs and image enhancement of SEM micrographs. Image analysis techniques were employed and stereological relations were determined for SEM images of CNT structures; these results were utilized to estimate the morphology of the turf (i.e. CNTs alignment and curvature) using Artificial Neural Networks (ANN) classifier. This model was also used to investigate the link between Raman spectra of CNTs and the quality of the structure morphology. This novel methodology will improve our capability to control the quality of the grown nanotubes through the use of this system in a supervised growth environment.