A Horizontal-Gate Monolayer MoS2 Transistor Based on Image Force Barrier Reduction

Large area MoS2 thin film growth by direct sulfurization

In this study, we present the growth of monolayer MoS₂ (molybdenum disulfide) film. Mo (molybdenum) film was formed on a sapphire substrate through e-beam evaporation, and triangular MoS₂ film was grown by direct sulfurization. First, the growth of MoS₂ was observed under an optical microscope. The number of MoS₂ layers was analyzed by Raman spectrum, atomic force microscope (AFM), and photoluminescence spectroscopy (PL) measurement. Different sapphire substrate regions have different growth conditions of MoS₂. The growth of MoS₂ is optimized by controlling the amount and location of precursors, adjusting the appropriate growing temperature and time, and establishing proper ventilation. Experimental results show the successful growth of a large-area single-layer MoS₂ on a sapphire substrate through direct sulfurization under a suitable environment. The thickness of the MoS₂ film determined by AFM measurement is about 0.73 nm. The peak difference between the Raman measurement shift of 386 and 405 cm^-1 is 19.1 cm^-1, and the peak of PL measurement is about 677 nm, which is converted into energy of 1.83 eV, which is the size of the direct energy gap of the MoS₂ thin film. The results verify the distribution of the number of grown layers. Based on the observation of the optical microscope (OM) images, MoS₂ continuously grows from a single layer of discretely distributed triangular single-crystal grains into a single-layer large-area MoS₂ film. This work provides a reference for growing MoS₂ in a large area. We expect to apply this structure to various heterojunctions, sensors, solar cells, and thin-film transistors.

Optical and Material Characteristics of MoS2/Cu2O Sensor for Detection of Lung Cancer Cell Types in Hydroplegia

In this study, n-type MoS₂ monolayer flakes are grown through chemical vapor deposition (CVD), and a p-type Cu₂O thin film is grown via electrochemical deposition. The crystal structure of the grown MoS₂ flakes is analyzed through transmission electron microscopy. The monolayer structure of the MoS₂ flakes is verified with Raman spectroscopy, multiphoton excitation microscopy, atomic force microscopy, and photoluminescence (PL) measurements. After the preliminary processing of the grown MoS₂ flakes, the sample is then transferred onto a Cu₂O thin film to complete a p-n heterogeneous structure. Data are confirmed via scanning electron microscopy, SHG, and Raman mapping measurements. The luminous energy gap between the two materials is examined through PL measurements. Results reveal that the thickness of the single-layer MoS₂ film is 0.7 nm. PL mapping shows a micro signal generated at the 627 nm wavelength, which belongs to the B2 excitons of MoS₂ and tends to increase gradually when it approaches 670 nm. Finally, the biosensor is used to detect lung cancer cell types in hydroplegia significantly reducing the current busy procedures and longer waiting time for detection. The results suggest that the fabricated sensor is highly sensitive to the change in the photocurrent with the number of each cell, the linear regression of the three cell types is as high as 99%. By measuring the slope of the photocurrent, we can identify the type of cells and the number of cells.

Growth Mechanism of Periodic-Structured MoS2 by Transmission Electron Microscopy

Molybdenum disulfide (MoS₂) was grown on a laser-processed periodic-hole sapphire substrate through chemical vapor deposition. The main purpose was to investigate the mechanism of MoS₂ growth in substrate with a periodic structure. By controlling the amount and position of the precursor, adjusting the growth temperature and time, and setting the flow rate of argon gas, MoS₂ grew in the region of the periodic holes. A series of various growth layer analyses of MoS₂ were then confirmed by Raman spectroscopy, photoluminescence spectroscopy, and atomic force microscopy. Finally, the growth mechanism was studied by transmission electron microscopy (TEM). The experimental results show that in the appropriate environment, MoS₂ can be successfully grown on substrate with periodic holes, and the number of growth layers can be determined through measurements. By observing the growth mechanism, composition analysis, and selected area electron diffraction diagram by TEM, we comprehensively understand the growth phenomenon. The results of this research can serve as a reference for the large-scale periodic growth of MoS₂. The production of periodic structures by laser drilling is advantageous, as it is relatively simpler than other methods.

Bandgap prediction of two-dimensional materials using machine learning

The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

Intelligent Identification of MoS2 Nanostructures with Hyperspectral Imaging by 3D-CNN

Increasing attention has been paid to two-dimensional (2D) materials because of their superior performance and wafer-level synthesis methods. However, the large-area characterization, precision, intelligent automation, and high-efficiency detection of nanostructures for 2D materials have not yet reached an industrial level. Therefore, we use big data analysis and deep learning methods to develop a set of visible-light hyperspectral imaging technologies successfully for the automatic identification of few-layers MoS₂. For the classification algorithm, we propose deep neural network, one-dimensional (1D) convolutional neural network, and three-dimensional (3D) convolutional neural network (3D-CNN) models to explore the correlation between the accuracy of model recognition and the optical characteristics of few-layers MoS₂. The experimental results show that the 3D-CNN has better generalization capability than other classification models, and this model is applicable to the feature input of the spatial and spectral domains. Such a difference consists in previous versions of the present study without specific substrate, and images of different dynamic ranges on a section of the sample may be administered via the automatic shutter aperture. Therefore, adjusting the imaging quality under the same color contrast conditions is unnecessary, and the process of the conventional image is not used to achieve the maximum field of view recognition range of ~1.92 mm². The image resolution can reach ~100 nm and the detection time is 3 min per one image.