2024 ASEE Annual Conference & Exposition

In this study, a simple yet versatile method is proposed for identifying the number of exfoliated
graphene layers transferred on an oxide substrate from optical images, utilizing a limited number
of input images for training, paired with a more traditional number of a few thousand well published Github images for testing and predicting. Two thresholding approaches, namely the
standard deviation-based approach and the linear regression-based approach, were employed in
this study. The method specifically leverages the red, green, and blue color channels of image
pixels and creates a correlation between the green channel of the background and the green
channel of the various layers of graphene. This method proves to be a feasible alternative to deep
learning-based graphene recognition and traditional microscopic analysis. The proposed
methodology performs well under conditions where the effect of surrounding light on the
graphene-on-oxide sample is minimum and allows rapid identification of the various graphene
layers. The study additionally addresses the functionality of the proposed methodology with
nonhomogeneous lighting conditions, showcasing successful prediction of graphene layers from
images that are lower in quality compared to typically published in literature. In all, the proposed
methodology opens up the possibility for the non-destructive identification of graphene layers
from optical images by utilizing a new and versatile method that is quick, inexpensive, and
works well with fewer images that are not necessarily of high quality.