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https://www.selleckchem.com/products/pkm2-inhibitor-compound-3k.html The spread of COVID-19 has led to an explosive increase in the number of waste polypropylene face masks worldwide, landfill and incineration of which will cause serious pollution and resource waste. This study aims to develop a new method for the safe and high-added value reuse of materials for polypropylene face masks based on carbonization of porous polymer. The waste masks were first sulfonated in an autoclave, then used as carbon source and turned into a dense hollow fiber porous structure after a one-step heat treatment. This porous structure has a high specific capacitance, namely 328.9 F g-1 at a current density of 1 A g-1. Besides, the assembled solid-state capacitor possesses a good energy density of 10.4 W h kg-1 at a power density of 600 W kg-1, and excellent cycling stability with a capacitance retention rate of 81.1% after 3000 cycles. These findings indicate that the novel carbonization technology in this study can not only be used to obtain high-performance supercapacitor electrode materials but also provide a new idea for the recycling and utilization of wastes such as medical devices.Salient object detection is a hot spot of current computer vision. The emergence of the convolutional neural network (CNN) greatly improves the existing detection methods. In this paper, we present 3MNet, which is based on the CNN, to make the utmost of various features of the image and utilize the contour detection task of the salient object to explicitly model the features of multi-level structures, multiple tasks and multiple channels, so as to obtain the final saliency map of the fusion of these features. Specifically, we first utilize contour detection task for auxiliary detection and then utilize use multi-layer network structure to extract multi-scale image information. Finally, we introduce a unique module into the network to model the channel information of the image. Our network has produced

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