Versatile Recognition of Graphene Layers from Optical Images Under Controlled Illumination Through Green-Channel Correlation Method
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
- Tagged Topic
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Data Science & Analytics Constituent Committee (DSA)
- Permanent URL
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https://peer.asee.org/48254
Paper Authors
Saquib Ahmed The State University of New York Buffalo State University
Dr. Ahmed uses DFT, MD, and various Data Analytics tools such as ML and Neural Networks to probe atomistic, molecular, and device level phenomena within photovoltaics, battery and supercapacitors, 2D and quantum materials, and semiconductors.
Abstract
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.
Ahmed, S. (2024, June), Versatile Recognition of Graphene Layers from Optical Images Under Controlled Illumination Through Green-Channel Correlation Method Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/48254
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