Nucleocapsid protein (N protein) is the most abundant protein in SARS-CoV2 and is highly conserved, and there are no homologous proteins in the human body, making it an ideal biomarker for the early diagnosis of SARS-CoV2. However, early detection of clinical specimens for SARS-CoV2 remains a challenge due to false-negative results with viral RNA and host antibodies based testing. In this manuscript, a microfluidic chip with femtoliter-sized wells was fabricated for the sensitive digital detection of N protein. Briefly, β-galactosidase (β-Gal)-linked antibody/N protein/aptamer immunocomplexes were formed on magnetic beads (MBs). Afterwards, the MBs and β-Gal substrate fluorescein-di-β- d -galactopyranoside (FDG) were injected into the chip together. Each well of the chip would only hold one MB as confined by the diameter of the wells. The MBs in the wells were sealed by fluorocarbon oil, which confines the fluorescent (FL) product generated from the reaction between β-Gal and FDG in the individual femtoliter-sized well and creates a locally high concentration of the FL product. The FL images of the wells were acquired using a conventional inverted FL microscope. The number of FL wells with MBs (FL wells number) and the number of wells with MBs (MBs wells number) were counted, respectively. The percentage of FL wells was calculated by dividing (FL wells number) by (MBs wells number). The higher the percentage of FL wells, the higher the N protein concentration. The detection limit of this digital method for N protein was 33.28 pg/mL, which was 300 times lower than traditional double-antibody sandwich based enzyme-linked immunosorbent assay (ELISA).

The isolation of black phosphorus (BP) and extraordinary performance of the BP field-effect transistor have led to BP offering remarkable properties in the two-dimensional (2D) family. Along with BP, other group VA element materials have been demonstrated to possess superior electronic and optical properties. However, numerous challenges remain to be overcome in their practical applications. Heterostructures play a vital role in modern semiconductors, and 2D group VA materials provide the opportunity to fabricate novel heterostructures that are combined by van der Waals forces. Previous theoretical and experimental studies have indicated that constructing a heterostructure is a promising strategy to conquer the obstacles and boost the development of 2D group VA materials. In this paper, we summarize the recent progress in 2D group VA material-based heterostructures. Firstly, the crystal structures and fundamental electrical properties of 2D group VA materials are introduced. Thereafter, various heterostructures based on group VA materials are discussed. Finally, conclusions and the outlook on emerging group VA heterostructures are presented.

Under the tested laser parameters, Er:YAG laser irradiation causes lower mechanical values and reduction of organic components in subsurface dentin, which has deleterious effects on resin adhesion to this area.

Light-emitting diode (LED) chips have disordered arrangement and defects with characteristics of low contrast, for which traditional segmentation methods cannot classify surface defects effectively. In this paper, a chip segmentation method based on position pre-estimation and a modified Normalized Correlation Coefficient (NCC) matching algorithm, as well as feature enhancement methods are proposed. The position pre-estimation method is used to avoid the interference introduced by the disordered chip arrangement and the large missing area. By modifying the NCC algorithm, matching speed is improved by eight times compared to traditional NCC while matching result is not affected by brightness change. Furthermore, feature enhancement schemes with higher speed and accuracy were designed to identify low-contrast defects. The experimental results showed that the average accuracy reached 99.54%, improved by 0.66% compared to the state-of-the-art method while the inspection missing rate was 0.03%. In addition, the detection time of a single chip was approximately 1.098 ms, which meets the requirements of online detection, and the smallest defect that could be detected was 2 µm. In summary, the methods proposed in this study meet the requirements of industrial online detection regardless of accuracy, efficiency, or extensibility.