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Aqueous Zn-ion hybrid supercapacitors (AZHSCs) combining the advantages of high‐energy batteries and high‐power supercapacitors see a bright future, but they still suffer from the poor capacity of carbonic cathodes. Herein, a functionalized porous carbon cloth (denoted as FPCC) electrode is demonstrated based on commercial carbon cloth (denoted as CC) tuning by structural and surface engineering. The constructed exfoliated porous carbon layer and the negatively charged functionalized interface not only increase the electrical double layer capacitance but also favor the chemical adsorption of Zn2+ to obtain additional pseudocapacitance. Consequently, the FPCC electrode delivers a high capacity of 0.16 mAh cm−2 at 4 mA cm−2, which is 923.8 times higher than CC, and a long cycle life (85.0% capacity retention after 30 000 cycles). More importantly, the Zn//FPCC AZHSC possesses an impressive energy density (3.3 mWh cm−3) and power density (240 mW cm−3), superior to many advanced batteries and supercapacitors. The quasi-solid-state device is also assembled as a demo. This modification strategy may provide new opportunities for high-performance AZHSCs. [ABSTRACT FROM AUTHOR]

Vanadium pentoxide (V2O5) has shown great potential as the electrode for aqueous ammonium ion batteries (AAIBs) owing to its good electrochemical reversibility and high theoretical capacity. However, the electrochemical performance of V2O5 is seriously limited by the weak NH4+ adsorption capability and insufficient active sites of vanadium oxide originated from the unsuitable 3d orbital electron state. Herein, the strategy of a 3d orbital electron tunning and crystal engineering is used to increase the ammonium ion storage capacity of V2O5 electrode. The experimental results show that the modified 3d orbital state of V4+ ( t 2 g 1 ) can effectively increase the active sites of V2O5. Therefore, the as-prepared N-VO exhibits a high specific capacity of 249.3 mA h g−1 at 1.0 A g−1 and 69.5 mA h g−1 at 10.0 A g−1, superior to other reported anode material for AAIBs. Noticeably, the prepared resultant quasi-solid-state ammonium ion battery can display considerable cycling stability with capacity retention of 87.9% after a long cycle life of 10 000 cycles at 1 A g−1 and impressive mechanical flexibility with no capacity decay after cycling at different bending angles. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) are widely used in portable energy storage. The capacity of commercial graphite is difficult to improve due to the stoichiometry limit of LiC6 of graphite, thus new anodes need to be developed to meet the demand of high-energy–density LIB. The growing interest in graphitized carbon nitride (g-C3N4) stems from its structural resemblance to graphite and its capacity to offer abundant adsorption and intercalation sites. However, g-C3N4, as a semiconductor, has a low lithium transfer rate due to its poor conductivity and high diffusion resistance. Improving the electron transport rate of g-C3N4 and reducing the adsorption energy barrier of Li+ in g-C3N4 are the keys to improving the electrochemical performances of g-C3N4. In this study, lignin and melamine were homogeneously mixed using the spray drying method, followed by the preparation of covalently bonded C3N4/LC material through a one-step carbonization process. The uniform dispersion of g-C3N4 in amorphous carbon can improve the conductivity and reduce the diffusion energy barrier of Li+. As a result, the C3N4/LC-x anode has better electrochemical behavior, including higher reversible capacity, better rate performance, and cycle stability. Highlights: • The covalently bonded C3N4/LC-x material was prepared through a one-step carbonization method. • The uniform dispersion of g-C3N4 in amorphous carbon could improve the electronic conductivity and reduce the diffusion energy barrier of Li+ ions. • C3N4/LC-2 showed high reversible capacity, ideal rate performance, and cycle life. [ABSTRACT FROM AUTHOR]

Objective. In this study, we utilized gonadotropin-releasing hormone analogue-modified indocyanine green (GnRHa-ICG) to improve the accuracy of intraoperative recognition and resection of endometriotic lesions. Methods. Gonadotropin-releasing hormone receptor (GnRHR) expression was detected in endometriosis tissues and cell lines via immunohistochemistry and western blotting. The in vitro binding capacities of GnRHa, GnRHa-ICG, and ICG were determined using fluorescence microscopy and flow cytometry. In vivo imaging was performed in mouse models of endometriosis using a near-infrared fluorescence (NIRF) imaging system and fluorescence navigation system. The ex vivo binding capacity was determined using confocal fluorescence microscopy. Results. GnRHa-ICG exhibited a significantly stronger binding capacity to endometriotic cells and tissues than ICG. In mice with endometriosis, GnRHa-ICG specifically imaged endometriotic tissues (EMTs) after intraperitoneal administration, whereas ICG exhibited signals in the intestine. GnRHa-ICG showed the highest fluorescence signals in the EMTs at 2 h and a good signal-to-noise ratio at 48 h postadministration. Compared with traditional surgery under white light, targeted NIRF imaging-guided surgery completely resected endometriotic lesions with a sensitivity of 97.3% and specificity of 77.8%. No obvious toxicity was observed in routine blood tests, serum biochemicals, or histopathology in mice. Conclusions. GnRHa-ICG specifically recognized and localized endometriotic lesions and guided complete resection of lesions with high accuracy. [ABSTRACT FROM AUTHOR]

Background: Accurate segmentation of brain glioma is a critical prerequisite for clinical diagnosis, surgical planning and treatment evaluation. In current clinical workflow, physicians typically perform delineation of brain tumor subregions slice‐by‐slice, which is more susceptible to variabilities in raters and also time‐consuming. Besides, even though convolutional neural networks (CNNs) are driving progress, the performance of standard models still have some room for further improvement. Purpose: To deal with these issues, this paper proposes an attention‐guided multi‐scale context aggregation network (AMCA‐Net) for the accurate segmentation of brain glioma in the magnetic resonance imaging (MRI) images with multi‐modalities. Methods: AMCA‐Net extracts the multi‐scale features from the MRI images and fuses the extracted discriminative features via a self‐attention mechanism for brain glioma segmentation. The extraction is performed via a series of down‐sampling, convolution layers, and the global context information guidance (GCIG) modules are developed to fuse the features extracted for contextual features. At the end of the down‐sampling, a multi‐scale fusion (MSF) module is designed to exploit and combine all the extracted multi‐scale features. Each of the GCIG and MSF modules contain a channel attention (CA) module that can adaptively calibrate feature responses and emphasize the most relevant features. Finally, multiple predictions with different resolutions are fused through different weightings given by a multi‐resolution adaptation (MRA) module instead of the use of averaging or max‐pooling to improve the final segmentation results. Results: Datasets used in this paper are publicly accessible, that is, the Multimodal Brain Tumor Segmentation Challenges 2018 (BraTS2018) and 2019 (BraTS2019). BraTS2018 contains 285 patient cases and BraTS2019 contains 335 cases. Simulations show that the AMCA‐Net has better or comparable performance against that of the other state‐of‐the‐art models. In terms of the Dice score and Hausdorff 95 for the BraTS2018 dataset, 90.4% and 10.2 mm for the whole tumor region (WT), 83.9% and 7.4 mm for the tumor core region (TC), 80.2% and 4.3 mm for the enhancing tumor region (ET), whereas the Dice score and Hausdorff 95 for the BraTS2019 dataset, 91.0% and 10.7 mm for the WT, 84.2% and 8.4 mm for the TC, 80.1% and 4.8 mm for the ET. Conclusions: The proposed AMCA‐Net performs comparably well in comparison to several state‐of‐the‐art neural net models in identifying the areas involving the peritumoral edema, enhancing tumor, and necrotic and non‐enhancing tumor core of brain glioma, which has great potential for clinical practice. In future research, we will further explore the feasibility of applying AMCA‐Net to other similar segmentation tasks. [ABSTRACT FROM AUTHOR]

The conventional fractionation of lignocellulose into cellulose, hemicellulose, and lignin was generally carried out at high temperatures and under highly acidic conditions owing to the rigid matrix of lignocellulose and crystallinity of cellulose, causing the degradation of carbohydrates and condensation of lignin, thus hindering their value-added utilization. In this study, a stepwise fractionation method was proposed in which microwave-assisted fractionation of hemicellulose under mild conditions contributed to 81.9% of xylose yield; then, the cellulose part solid was rapidly dissolved in LiBr molten salt hydrate (LiBr-MSH) solution, followed by a filtration process to isolate lignin solid. It was found that the removal of hemicellulose provided more pores for the next dissolution of cellulose, and LiBr-MSH caused the cleavage of the hydrogen bonds of cellulose. Consequently, the cellulose part solid was rapidly dissolved in LiBr-MSH. The results showed that under the optimal fractionation conditions, 78.0% yield of glucan oligomers and 89.7% purity of noncondensed lignin were obtained from cellulose fractionation and filtration, respectively. A high β-O-4 bond content close to 87.3% of the theoretical maximum yield was detected in the obtained lignin, indicating that most β-O-4 bond structure of lignin was well preserved. Additionally, the isolated lignin can be selectively depolymerized into aromatic monomers with a high yield of 20.4%, which is 74.2% of the theoretical maximum yield. [ABSTRACT FROM AUTHOR]