Machine Learning-Assisted Identification of Single-Layer Graphene via Color Variation Analysis

Author:

Yang Eunseo12,Seo Miri13ORCID,Rhee Hanee14,Je Yugyeong1,Jeong Hyunjeong1,Lee Sang Wook1ORCID

Affiliation:

1. Department of Physics, Ewha Womans University, Seoul 03760, Republic of Korea

2. Department of Artificial Intelligence and Software, Ewha Womans University, Seoul 03760, Republic of Korea

3. Department of Medicine, Kyung Hee University College of Medicine, Seoul 02447, Republic of Korea

4. Department of Mathematical Sciences, Seoul National University, Seoul 08826, Republic of Korea

Abstract

Techniques such as using an optical microscope and Raman spectroscopy are common methods for detecting single-layer graphene. Instead of relying on these laborious and expensive methods, we suggest a novel approach inspired by skilled human researchers who can detect single-layer graphene by simply observing color differences between graphene flakes and the background substrate in optical microscope images. This approach implemented the human cognitive process by emulating it through our data extraction process and machine learning algorithm. We obtained approximately 300,000 pixel-level color difference data from 140 graphene flakes from 45 optical microscope images. We utilized the average and standard deviation of the color difference data for each flake for machine learning. As a result, we achieved F1-Scores of over 0.90 and 0.92 in identifying 60 and 50 flakes from green and pink substrate images, respectively. Our machine learning-assisted computing system offers a cost-effective and universal solution for detecting the number of graphene layers in diverse experimental environments, saving both time and resources. We anticipate that this approach can be extended to classify the properties of other 2D materials.

Funder

National Research Foundation of Korea

Human Frontier Science Program

Publisher

MDPI AG

Reference37 articles.

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