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Optical image analysis for graphene layer detection: Enhanced green channel methodology

Journal Article · · Computational Materials Science
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  1. Bangladesh University of Engineering and Technology (BUET), Dhaka (Bangladesh)
  2. Brookhaven National Laboratory (BNL), Upton, NY (United States). Center for Functional Nanomaterials (CFN)
  3. State Univ. of New York (SUNY), Buffalo, NY (United States)
+ Show Author Affiliations
Graphene, a material of increasing research interest, requires accurate layer identification due to its sensitivity to layer count. Existing methods for graphene layer number identification are either time-consuming or of low accuracy, with high-accuracy methods often requiring expensive processes. This paper aims to address this challenge by proposing a cost-effective and efficient approach. Specifically, the current work highlights only the green channel—one of the three primary color channels (red, green, blue) that make up an optical image—from images of exfoliated graphene flakes for layer count identification. A linear regression is performed between pixel position and substrate green channel value, and this effect is subtracted from the entire optical image to mitigate background effects. By storing the range of green channel values for each type of flake (monolayer, bilayer, or tri-layer) based on a few images, we establish thresholds for identifying different types of layers in a particular setup. Additionally, our methodology allows for flexible threshold tuning using a single reference image, enabling adjustment to changes in detection setup such as illumination level, magnification, or microscope used. Finally, demonstrating high accuracy and flexibility, this methodology presents a suitable technique for graphene layer number identification without the need for large datasets or expensive instruments.
Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)
Grant/Contract Number:
SC0012704
OSTI ID:
2406891
Report Number(s):
BNL--225835-2024-JAAM
Journal Information:
Computational Materials Science, Journal Name: Computational Materials Science Vol. 244; ISSN 0927-0256
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (10)

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Related Subjects

2D materials
77 NANOSCIENCE AND NANOTECHNOLOGY
graphene
green channel
image processing
optical image