Optical nonlinearity in 2D materials excited by spatial Gaussian laser beam is a novel and peculiar optical phenomenon, which exhibits many novel and interesting applications in optical nonlinear devices. Passive photonic devices, such as optical switches, optical logical gates, photonic diodes, and optical modulators, are the key compositions in the future all‐optical signal‐processing technologies. Passive photonic devices using 2D materials to achieve the device functionality have attracted widespread concern in the past decade. In this Review, an overview of the spatial self‐phase modulation (SSPM) in 2D materials is summarized, including the operating mechanism, optical parameter measurement, and tuning for 2D materials, and applications in photonic devices. Moreover, some current challenges are also proposed to solve, and some possible applications of SSPM method are predicted for the future. Therefore, it is anticipated that this summary can contribute to the application of 2D material‐based spatial effect in all‐optical signal‐processing technologies.
Digital image feature recognition is significant to industrial information applications, such as bioengineering, medical diagnosis, and machinery industry. In order to supply an effective and reasonable technology of the severity assessment mission of COVID-19, we propose a new method that identifies rich features of lung infections from a chest CT image, and then assesses the severity of COVID-19 based on the extracted features. First, in a chest CT image, the lung contours are corrected for the segmentation of bilateral lungs. Then, the lung contours and areas are obtained from the lung regions. Next, the coarseness, contrast, roughness, and entropy texture features are extracted to confirm the COVID-19 infected regions, and then the lesion contours are extracted from the infected regions. Finally, the texture features and V-descriptors are fused as an assessment descriptor for the COVID-19 severity estimation. In the experiments, we show the feature extraction and lung lesion segmentation results based on some typical COVID-19 infected CT images. In the lesion contour reconstruction experiments, the performance of V-descriptors is compared with some different methods, and various feature scores indicate that the proposed assessment descriptor reflects the infected ratio and the density feature of the lesions well, which can estimate the severity of COVID-19 infection more accurately.
Sensing devices are key nodes for information detection, processing, and conversion and are widely applied in different fields such as industrial production, environmental monitoring, and defense. However, increasing demand of these devices has complicated the application scenarios and diversified the detection targets thereby promoting the continuous development of sensing materials and detection methods. In recent years, Tin+1CnTx (n = 1, 2, 3) MXenes with outstanding optical, electrical, thermal, and mechanical properties have been developed as ideal candidates of sensing materials to apply in physical, chemical, and biological sensing fields. In this review, depending on optical and electrical sensing signals, we systematically summarize the application of Tin+1CnTx in nine categories of sensors such as strain, gas, and fluorescence sensors. The excellent sensing properties of Tin+1CnTx allow its further development in emerging intelligent and bionic devices, including smart flexible devices, bionic E-skin, neural network coding and learning, bionic soft robot, as well as intelligent artificial eardrum, which are all discussed briefly in this review. Finally, we present a positive outlook on the potential future challenges and perspectives of MXene-based sensors. MXenes have shown a vigorous development momentum in sensing applications and can drive the development of an increasing number of new technologies.
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