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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.

We fabricated hybrid nanostructures consisting of MoS2 monolayers and Au nanopillar (Au-NP) arrays. The surface morphology and Raman spectra showed that the MoS2 flakes transferred onto the Au-NPs were very flat and nonstrained. The Raman and photoluminescence intensities of MoS2/Au-NP were 3- and 20-fold larger than those of MoS2 flakes on a flat Au thin film, respectively. The finite-difference time-domain calculations showed that the Au-NPs significantly concentrated the incident light near their surfaces, leading to broadband absorption enhancement in the MoS2 flakes. Compared with a flat Au thin film, the Au-NPs enabled a 6-fold increase in the absorption in the MoS2 monolayer at a wavelength of 615 nm. The contact potential difference mapping showed that the electric potential at the MoS2/Au contact region was higher than that of the suspended MoS2 region by 85 mV. Such potential modulation enabled the Au-NPs to efficiently collect photogenerated electrons from the MoS2 flakes, as revealed by the uniform positive surface photovoltage signals throughout the MoS2 surface.