Facile and scalable synthesis of Si@void@C embedded in interconnected three-dimensional porous carbon architecture for high performance lithium ion batteries

Si‐based materials possess huge potential as an excellent anode material for Li‐ion batteries. However, how to realize scalable synthesis of Si‐based anode with a long cycling life and high‐performance is still a critical challenge. Here a water‐in‐oil microemulsion process followed by UV illumination, calcination, and hydrothermal method to produce yolk‐shell Si@void@C embedded in interconnected 3D porous carbon network architecture using silicon nanoparticles is reported. As a result, the sample Si@void@C/C‐2 electrode has achieved a reversible capacity of 1160 mA h g−1 at 0.2 A g−1 after 300 cycles and a stable long cycling life of 480 mA h g−1 at 1 A g−1 after 1000 cycles. A full battery with the synthesized anode shows a capacity of 128 mA h g−1 at 0.2 A g−1 as well as good cycling stability after 1100th cycles. Such excellent electrochemical performance is ascribed to its unique structure, the yolk‐shell void space, highly robust carbon shells and interconnected porous carbon nets that can improve the conductivity of the electrode, buffer the volume expansion, and also suppress Si nanoparticles stress variation. This water‐in oil system makes it possible for mass production of environmentally friendly synthesis of core–shell structure

Certifying randomness in quantum state collapse

The unpredictable process of state collapse caused by quantum measurements makes the generation of quantum randomness possible. In this paper, we explore the quantitive connection between the randomness generation and the state collapse and provide a randomness verification protocol under the assumptions: (I) independence between the source and the measurement devices and (II) the L\"{u}ders' rule for collapsing state. Without involving heavy mathematical machinery, the amount of genereted quantum randomness can be directly estimated with the disturbance effect originating from the state collapse. In the protocol, we can employ general measurements that are not fully trusted. Equipped with trusted projection measurements, we can further optimize the randomness generation performance. Our protocol also shows a high efficiency and yields a higher randomness generation rate than the one based on uncertainty relation. We expect our results to provide new insights for understanding and generating quantum randomnes

Automatic landslide identification by Dual Graph Convolutional Network and GoogLeNet model-a case study for Xinjiang province, China

Landslides are a natural disaster that exists widely in the world and poses a great threat to human life and property, so it is of great importance to identify and locate landslides. Traditional manual interpretation can effectively identify landslides, but its efficiency is very low for large interpreted areas. In this sense, a landslide recognition method based on the Dual Graph Convolutional Network (DGCNet) is proposed to identify the landslide in remote sensing images quickly and accurately. The remote sensing image (regional remote sensing image) of the northern mountainous area of Tuergen Township, Xinyuan County, Xinjiang Province, was obtained by GeoEye-1 (spatial resolution: 0.5 m). Then, the DGCNet is used to train the labeled images, which finally shows good accuracy of landslide recognition. To show the difference with the traditional convolutional network model, this paper adopts a convolution neural network algorithm named GoogLeNet for image recognition to carry out a comparative analysis, the remote sensing satellite images (single terrain image) of Xinyuan County, Xinjiang Province is used as the data set, and the prediction accuracy is 81.25%. Compared with the GoogLeNet model, the DGCNet model has a larger identification range, which provides a new method for landslide recognition of large-scale regional remote sensing images, but the performance of DGCNet is highly dependent on the quality and characteristics of the input image. If the input data quality is poor or the image structure is unclear, the model’s performance may decline

Epidemiology and immunoprotection of nephropathogenic avian infectious bronchitis virus in southern China

<p>Abstract</p> <p>Background</p> <p>In last three years, 96 suspected poultry farms from different provinces in China were diagnosed for avian infectious bronchitis (IB) survey. Finally, 221 IBV strains were confirmed by dwarf embryo test and RT-PCR assay. By virus recovery trials, 187 of the isolates caused the birds died or distressed from nephritis, which was accordant with the clinical record.</p> <p>Results</p> <p>Based on epidemiology analysis of recent field isolates of nephropathogenic IB in vaccinated farms in China, YL6 strain were used for vaccination and evaluated by antibody titer and challenge tests. The immunoprotection test indicated that the practical application of vaccine based on the recent field strains could finely facilitate controlling the nephropathogenic IB.</p> <p>Conclusions</p> <p>Our study was aim at setting a guide for safeguard against nephropathogenic IBV-caused disease in China.</p

Type I interferons suppress viral replication but contribute to T cell depletion and dysfunction during chronic HIV-1 infection

The direct link between sustained type I interferon (IFN-I) signaling and HIV-1-induced immunopathogenesis during chronic infection remains unclear. Here we report studies using a monoclonal antibody to block IFN-α/β receptor 1 (IFNAR1) signaling during persistent HIV-1 infection in humanized mice (hu-mice). We discovered that, during chronic HIV-1 infection, IFNAR blockade increased viral replication, which was correlated with elevated T cell activation. Thus, IFN-Is suppress HIV-1 replication during the chronic phase but are not essential for HIV-1-induced aberrant immune activation. Surprisingly, IFNAR blockade rescued both total human T cell and HIV-specific T cell numbers despite elevated HIV-1 replication and immune activation. We showed that IFNAR blockade reduced HIV-1-induced apoptosis of CD4+ T cells. Importantly, IFNAR blockade also rescued the function of human T cells, including HIV-1-specific CD8+ and CD4+ T cells. We conclude that during persistent HIV-1 infection, IFN-Is suppress HIV-1 replication, but contribute to depletion and dysfunction of T cells

Biomass carbon stocks and their changes in northern China's grasslands during 1982-2006

Grassland covers approximately one-third of the area of China and plays an important role in the global terrestrial carbon (C) cycle. However, little is known about biomass C stocks and dynamics in these grasslands. During 2001-2005, we conducted five consecutive field sampling campaigns to investigate above-and below-ground biomass for northern China's grasslands. Using measurements obtained from 341 sampling sites, together with a NDVI (normalized difference vegetation index) time series dataset over 1982-2006, we examined changes in biomass C stock during the past 25 years. Our results showed that biomass C stock in northern China's grasslands was estimated at 557.5 Tg C (1 Tg=10(12) g), with a mean density of 39.5 g C m(-2) for above-ground biomass and 244.6 g C m(-2) for below-ground biomass. An increasing rate of 0.2 Tg C yr(-1) has been observed over the past 25 years, but grassland biomass has not experienced a significant change since the late 1980s. Seasonal rainfall (January-July) was the dominant factor driving temporal dynamics in biomass C stock; however, the responses of grassland biomass to climate variables differed among various grassland types. Biomass in arid grasslands (i.e., desert steppe and typical steppe) was significantly associated with precipitation, while biomass in humid grasslands (i.e., alpine meadow) was positively correlated with mean January-July temperatures. These results suggest that different grassland ecosystems in China may show diverse responses to future climate changes