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Cathode materials with conversion mechanisms for aqueous zinc-ion batteries (AZIBs) have shown a great potential as next-generation energy storage materials due to their high discharge capacity and high energy density. However, improving their cycling stability has been the biggest challenge plaguing researchers. In this study, CuO microspheres were prepared using a simple hydrothermal reaction, and the morphology and crystallinity of the samples were modulated by controlling the hydrothermal reaction time. The as-synthesized materials were used as cathode materials for AZIBs. The electrochemical experiments showed that the CuO-4h sample, undergoing a hydrothermal reaction for 4 h, had the longest lifecycle and the best rate of capability. A discharge capacity of 131.7 mAh g−1 was still available after 700 cycles at a current density of 500 mA g−1. At a high current density of 1.5 A g−1, the maintained capacity of the cell is 85.4 mA h g−1. The structural evolutions and valence changes in the CuO-4h cathode material were carefully explored by using ex situ XRD and ex situ XPS. CuO was reduced to Cu2O and Cu after the initial discharge, and Cu was oxidized to Cu2O instead of CuO during subsequent charging processes. We believe that these findings could introduce a novel approach to exploring high-performance cathode materials for AZIBs.

Prussian blue analogs (PBAs) are widely used as electrode materials for secondary batteries because of their cheapness, ease of synthesis, and unique structural properties. Nevertheless, the unsatisfactory capacity and cyclic stability of PBAs are seriously preventing their practical applications. Here, vanadium hexacyanoferrate (VHCF) is successfully prepared and used as a cathode for aqueous zinc-ion batteries (AZIBs). When using 3 M Zn(CF3SO3)2 as the electrolyte, a high capacity of ~230 mA h g−1 and a high voltage of ~1.2 V can be achieved. The XRD result and XPS analysis indicate that the outstanding Zn2+ storage capability is due to the presence of dual electrochemical redox centers in VHCF (Fe2+ ⇋ Fe3+ and V5+ ⇋ V4+ ⇋ V3+). However, the battery shows a short cycle life (7.1% remaining capacity after 1000 cycles) due to the dissolution of VHCF. To elongate the cycle life of the battery, a high-concentration hybrid electrolyte is used to reduce the activity of water molecules. The improved battery exhibits an impressive capacity of 235.8 mA h g−1 and good capacity retention (92.9%) after 1000 cycles.

Polysaccharide from marine shellfish has various bioactivities. In this study, the effects of polysaccharide from Patinopecten yessoensis skirt (PS) on boosting immune response in mice were evaluated, and the potential mechanisms were explored. The results showed that PS administration effectively increased the serum IgG and IgM levels, implying that PS had immune response-boosting properties. Moreover, PS administration could modulate the composition of the gut microbiota, and significantly improve short-chain fatty acids (SCFAs) metabolism, especially butyrate metabolism. Of note, the expression of the Tlr2, Tlr7, MyD88, Tnfa, and Il1b genes in toll-like receptor (TLR) signaling pathway was significantly increased. In summary, PS could boost immune response by modulating the gut microbiota and SCFAs metabolism correlating with the activation of the TLR signaling pathway. Therefore, PS can be developed as a special ingredient for functional product.

Abstract Background Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited. Results In this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset. Conclusions The framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.