  The main objective of this study was to develop a novel method of
characterizing nanomaterials based on the number of layers without the aid of
state-of-the-art electron and force microscopes. While previous research groups
have attempted to establish a correlation between optical image contrast and
layer number for inferring layer numbers of nanomaterials with already
well-known software such as ImageJ and Gwyddion SPM Analysis, the work for this
study strived to automate the image contrast-based characterization of the
layer numbers using computer vision algorithms. After acquiring the necessary
data points using graphene samples from another study and nanoscale MoTe2
samples through an experimental method consisted of using both ImageJ and
Gwyddion, curve fitting in RStudio was used to create quadratic models that
were incorporated in a computer vision method composed of three algorithms. In
total, 12 MoTe2 and 16 graphene samples were used in 28 test trials in order to
determine the efficiencies of the algorithms in layer number characterization.
Ultimately, a success rate of 89% was obtained with an average overall run time
of 15 seconds for the computer vision method. As a consequence, this
computational method may be faster, more effective, and more cost-effective
than current widely used electron, atomic force, and optical microscopy
techniques.
