To manipulate on-chip mid-IR signals, it is pivotal to construct a waveguide with subwavelength energy confinement. However, a deep subwavelength optical waveguide always suffers from high signal crosstalk, resulting in an inevitable coupling loss of multi-channel communication. To solve this problem, in this paper, a mid-IR hybrid waveguide which is composed of a graphene/hexagonal boron nitride (hBN) structure and a dielectric waveguide, is designed to realize a strongly enhanced light-matter interaction, accompanied by a low crosstalk transmission. The surface-phonon-plasmon-polariton mode generated by the graphene-hBN is coupled to a nanowire dielectric mode to form a hybrid guiding mode. Benefiting from this hybrid mode, the results show that it is possible to minimize the crosstalk of two parallel waveguides by reducing the width of the graphene-hexagonal hBN structure even if the waveguide separation length is at the nanoscale, thereby enabling low crosstalk optical transmission. Our designed approach opens the door for possible uses in nanophotonic devices such as amplitude equalizers, mode multiplexers, and wavelength-selective switches in optical communication systems.
In this study, we theoretically propose a surface plasmon resonance (SPR) biosensor composed of a plasmonic gold film, double negative (DNG) metamaterial, graphene-MoS2−COOH Van der Waals heterostructures and gold nanoparticles (Au NPs). We use a novel scheme of Goos-Hänchen (GH) shift to study the biosensing performances of our proposed plasmonic biosensor. The calculation results show that, both an extreme low reflectivity of 8.52×10-10 and significantly enhanced GH sensitivity of 2.1530×107 μm/RIU can be obtained, corresponding to the optimal configuration: 32 nm Au film/120 nm metamaterial/4-layer graphene/4-layer MoS2−COOH. In addition, there is a theoretically excellent linear response between the concentration of target analytes (SARS-CoV-2 and S protein) and the change in differential GH shift. Our proposed biosensor promises to be a useful tool for performing the novel coronavirus detection.
In recent years, lung cancer has become one of the most lethal factors to human beings. Clinical data show that the probability of lung nodules developed into lung cancer is about 30%. Due to the lack of obvious symptoms, around 70% of lung cancer patients in China are in advanced stage of lung cancer when firstly diagnosed. Therefore, early identification of lung nodules is of great significance for early diagnosis and therapy. Currently, artificial intelligence has been widely used to generate predictive model of lung nodules by learning algorithms adapted to image characteristics, leading to improved accuracy and higher sensitivity of diagnosis of early lung cancer. In this work, Luna16 (lung nodule analysis 2016, containing a total of 888 low-dose chest Computed Tomography (CT) thin-slice plain scan lesions) were selected as the data set, providing a total of 1018 CT slices with the most representative shape of lung nodules in this analysis. Next, this project was performed on Baidu AI Studio platform, applying both U-Net and PSP Net to train a model of rapid detection of lung nodules. The training process generated a model providing a rapid and accurate identification of lung nodules larger than 3 mm in diameter. Results showed that the accuracy of U-Net was higher than that of PSP Net, indicating a high potential in further clinical diagnosis in lung cancer.
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