RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

Abstract

No characterization method is available to quickly perform quality inspection of 2D materials produced on an industrial scale. This hinders the adoption of 2D materials for product manufacturing in many industries. Here, we report an artificial-intelligence-assisted Raman analysis to quickly probe the quality of centimeter-large graphene samples in a non-destructive manner. Chemical vapor deposition of graphene is devised in this work such that two types of samples were obtained: layer-plus-islands and layer-by-layer graphene films, at centimeter scales. Using these samples, we implemented and integrated an unsupervised learning algorithm with an automated Raman spectroscopy to precisely cluster 20,250 and 18,000 Raman spectra collected from layer-plus-islands and layer-by-layer graphene films, respectively, into five and two clusters. Each cluster represents graphene patches with different layer numbers and stacking orders. For instance, the two clusters detected in layer-by-layer graphene films represent monolayer and bilayer graphene based on their Raman fingerprints. Our intelligent Raman analysis is fully automated, with no human operation involved, is highly reliable (99.95% accuracy), and can be generalized to other 2D materials, paving the way towards industrialization of 2D materials for various applications in the future.

Recent advances in structural modification on graphitic carbon nitride (g-C 3N 4)-based photocatalysts for high-efficiency photocatalytic H 2O 2production

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Artificial intelligence in employee recruitment

“…Researchers have investigated the success rates of different methods applied in personnel recruitment. In the studies conducted in companies, the rate of hiring the right personnel in interviews conducted through AI was 82% (Leong et al., 2020). Managers in these companies stated that the hired personnel highly aligned with the company's wishes and expectations and they were very satisfied with the recruitment results.…”

Recent Progresses in Machine Learning Assisted Raman Spectroscopy

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