Unveiling the distinctive mechanical and thermal properties of γ-GeSe.

γ-GeSe is a newly identified polymorph among group-IV monochalcogenides, characterized by a distinctive interatomic bonding configuration. Despite its promising applications in electrical and thermal domains, the experimental verification of its mechanical and thermal properties remains unreported. Here, we experimentally characterize the in-plane Young's modulus (E) and thermal conductivity ([Formula: see text]) of γ-GeSe. The mechanical vibrational modes of freestanding γ-GeSe flakes are measured using optical interferometry. Nano-indentation via atomic force microscopy is also conducted to induce mechanical deformation and to extract the E. Comparison with finite-element simulations reveals that the E is 97.3[Formula: see text]7.5 GPa as determined by optical interferometry and 109.4[Formula: see text]13.5 GPa as established through the nano-indentation method. Additionally, optothermal Raman spectroscopy reveals that γ-GeSe has a lattice thermal conductivity of 2.3 [Formula: see text] 0.4 Wm-1K-1 and a total thermal conductivity of 7.5 [Formula: see text] 0.4 Wm-1K-1 in the in-plane direction at room temperature. The notably high [Formula: see text] ratio in γ-GeSe, compared to other layered materials, underscores its distinctive structural and dynamic characteristics.

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

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.