Objective: CT provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study is to develop a deep learning (DL) based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. Methods: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each 8 9

Ultrathin MoS2 nanosheets uniformly embedded into a N,O-codoped carbon matrix possess outstanding cyclability and rate performances as a lithium/sodium ion battery anode.

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Vertical graphene growth on subnanoscopically and homogeneously dispersed SiOx and carbon composite microspheres shows fast and stable lithium ion storage behavior.

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Free‐standing and foldable electrodes with high energy density and long lifespan have recently elicited attention on the development of lithium‐ion batteries (LIBs) for flexible electronic devices. However, both low energy density and slow kinetics in cycling impede their practical applications. In this work, a free‐standing and binder‐free N, O‐codoped 3D vertical graphene carbon nanofibers electrode with ultra‐high silicon content (VGAs@Si@CNFs) is developed via electrospinning, subsequent thermal treatment, and chemical vapor deposition processes. The as‐prepared VGAs@Si@CNFs electrode exhibits excellent conductivity and flexibility because of the high graphitized carbon nanofiber network and abundant vertical graphene arrays. Such 3D all‐carbon architecture can be fabulous for providing a conductive and mechanically robust network, further improving the kinetics and restraining the volume expansion of Si NPs, especially with an ultra‐high Si content (>90 wt%). As a result, the VGAs@Si@CNFs composite demonstrates a superior specific capacity (3619.5 mAh g−1 at 0.05 A g−1), ultralong lifespan, and outstanding rate capability (1093.1 mAh g−1 after 1500 cycles at 8 A g−1) as a free‐standing anode for LIBs. It is believed that this work offers an exciting method for developing free‐standing and high‐energy‐density electrodes for other energy storage devices.

Metallic zinc anodes of aqueous zinc ion batteries suffer from severe dendrite and side reaction issues, resulting in poor cycling stability, especially at high rates and capacities. Herein, we develop two three-dimensional hierarchical graphene matrices consisting of nitrogen-doped graphene nanofibers clusters anchored on vertical graphene arrays of modified multichannel carbon. The graphene matrix with radial direction carbon channels possesses high surface area and porosity, which effectively minimizes the surface local current density, manipulates the Zn2+ ions concentration gradient, and homogenizes the electric field distribution to regulate Zn deposition. As a result, the engineered matrices achieve a superior coulombic efficiency of 99.67% over 3000 cycles at 120 mA cm−2, the symmetric cells with the composite zinc anode demonstrates 2600 h dendrite-free cycles at 80 mA cm−2 and 80 mAh cm−2. The as-designed full cell exhibits an inspiring capacity of 16.91 mAh cm−2. The Zn capacitor matched with activated carbon shows a superior long-term cycle performance of 20000 cycles at 40 mA cm−2. This strategy of constructing a 3D hierarchical structure for Zn anodes may open up a new avenue for metal anodes operating under high rates and capacities.