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.

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Optical measurements of temperature-dependent photoluminescence (PL) spectral peak, integrated PL intensity and PL decay time, and microstructure analyses with high-resolution transmission electron microscopy showed the strong dependencies of thermal annealing effects on quantum well (QW) width in InGaN/GaN QW structures. With different QW widths, different levels of strain energy were built. Upon thermal annealing, energy relaxation resulted in the reshaping of quantum dots and hence the changes of optical properties. Thermal annealing at 800 °C of a narrow QW width (2 nm) structure led to regularly distributed quantum dots (QDs) and improved optical quality. However, thermal annealing at the same temperature of a sample of larger QW width (4 nm) did not show QD formation. In this situation, even higher local strains around QWs were speculated. Also, degraded optical quality was observed.

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.

Luminosity and correlated color temperature (CCT) have gradually become two of the most important factors in the evaluation of the performance of light sources. However, although most color performance evaluation metrics are highly correlated with CCT, these metrics often do not account for light sources with different CCTs. This paper proposes the existence of a relationship between luminosity and CCT to remove the effects of CCT and to allow for a fairer judgment of light sources under the current color performance evaluation metrics. This paper utilizes the Hyper-Spectral Imaging (HSI) technique to recreate images of a standard color checker under different luminosities, CCT, and light sources. The images are then analyzed and transformed into interpolation figures and equal color difference curves. This paper utilizes statistic tools and symmetry properties to determine an exponential relationship between luminosity and CCT in red-green-blue (RGB) LED and OLED light sources. Such a relationship presents an option to remove the effects of CCT in color evaluation standards, as well as provide a guide line for adjusting visual experience solely by adjusting luminosity when creating a lighting system.

The interplay of surface texture, strain relaxation, absorbance, grain size, and sheet resistance in textured, boron-doped ZnO (ZnO@B), transparent conductive oxide (TCO) materials of different thicknesses used for thin film, solar cell applications is investigated. The residual strain induced by the lattice mismatch and the difference in the thermal expansion coefficient for thicker ZnO@B is relaxed, leading to an increased surface texture, stronger absorbance, larger grain size, and lower sheet resistance. These experimental results reveal the optical and material characteristics of the TCO layer, which could be useful for enhancing the performance of solar cells through an optimized TCO layer.