Decorating ketjen black with ultra-small Mo_(2)C nanoparticles to enhance polysulfides chemisorption and redox kinetics for lithium-sulfur batteries

The low sulfur utilization and fast capacity fading resulting from the sluggish redox reaction and abominable polysulfides shuttle greatly hinder the practical applications of lithium-sulfur(Li-S) batteries.Herein, we develop a facile "in-situ growth" method to decorate ultra-small Mo2 C nanoparticles(USMo2 C) on the surface of Ketjen Black(KB) to functionalize the commercial polypropylene(PP) separators,which can accelerate the redox kinetics of lithium polysulfides conversion and effectively increase the utilization of sulfur for Li-S batteries. Importantly, the US-Mo2 C nanoparticles have abundant sites for chemical adsorption towards polysulfides and the conductive carbon networks of KB have cross-linked pore channels, which can promote electron transport and provide physical barrier and volume expansion space for polysulfides. Due to the combined effects of the US-Mo2 C and KB, Li-S cells employing the multifunctional PP separators modified with KB/US-Mo2 C composite(KB/US-Mo2 C@PP) exhibit a high specific capacity(1212.8 mAh g^(-1) at 0.2 C), and maintain a reversible capacity of 1053.3 m Ah g^(-1) after 100 cycles.More importantly, the KB/US-Mo2 C@PP cells with higher sulfur mass loading of 4.9 mg cm^(-2) have superb areal capacity of 2.3 mAh cm^(-2). This work offers a novel and promising perspective for high-performance Li-S batteries from both the shuttle effect and the complex polysulfides conversion.

Multi-Classification of Polyps in Colonoscopy Images Based on an Improved Deep Convolutional Neural Network

Achieving accurate classification of colorectal polyps during colonoscopy can avoid unnecessary endoscopic biopsy or resection.This study aimed to develop a deep learning model that can automatically classify colorectal polyps histologically on white-light and narrow-band imaging(NBI)colonoscopy images based on World Health Organization(WHO)and Workgroup serrAted polypS and Polyposis(WASP)classification criteria for colorectal polyps.White-light and NBI colonoscopy images of colorectal polyps exhibiting pathological results were firstly collected and classified into four categories:conventional adenoma,hyperplastic polyp,sessile serrated adenoma/polyp(SSAP)and normal,among which conventional adenoma could be further divided into three sub-categories of tubular adenoma,villous adenoma and villioustublar adenoma,subsequently the images were re-classified into six categories.In this paper,we proposed a novel convolutional neural network termed Polyp-DedNet for the four-and six-category classification tasks of colorectal polyps.Based on the existing classification network ResNet50,Polyp-DedNet adopted dilated convolution to retain more high-dimensional spatial information and an Efficient Channel Attention(ECA)module to improve the classification performance further.To eliminate gridding artifacts caused by dilated convolutions,traditional convolutional layers were used instead of the max pooling layer,and two convolutional layers with progressively decreasing dilation were added at the end of the network.Due to the inevitable imbalance of medical image data,a regularization method DropBlock and a Class-Balanced(CB)Loss were performed to prevent network overfitting.Furthermore,the 5-fold cross-validation was adopted to estimate the performance of Polyp-DedNet for the multi-classification task of colorectal polyps.Mean accuracies of the proposed Polyp-DedNet for the four-and six-category classifications of colorectal polyps were 89.91%±0.92%and 85.13%±1.10%,respectively.The metrics of precision,recall and F1-score were also improved by 1%∼2%compared to the baseline ResNet50.The proposed Polyp-DedNet presented state-of-the-art performance for colorectal polyp classifying on white-light and NBI colonoscopy images,highlighting its considerable potential as an AI-assistant system for accurate colorectal polyp diagnosis in colonoscopy.

MPFracNet:A Deep Learning Algorithm for Metacarpophalangeal Fracture Detection with Varied Difficulties

Due to small size and high occult,metacarpophalangeal fracturediagnosis displays a low accuracy in terms of fracture detection and locationin X-ray images.To efficiently detect metacarpophalangeal fractures on Xrayimages as the second opinion for radiologists,we proposed a novel onestageneural network namedMPFracNet based onRetinaNet.InMPFracNet,a deformable bottleneck block(DBB)was integrated into the bottleneckto better adapt to the geometric variation of the fractures.Furthermore,an integrated feature fusion module(IFFM)was employed to obtain morein-depth semantic and shallow detail features.Specifically,Focal Loss andBalanced L1 Loss were introduced to respectively attenuate the imbalancebetween positive and negative classes and the imbalance between detectionand location tasks.We assessed the proposed model on the test set andachieved an AP of 80.4%for the metacarpophalangeal fracture detection.To estimate the detection performance for fractures with different difficulties,the proposed model was tested on the subsets of metacarpal,phalangeal andtiny fracture test sets and achieved APs of 82.7%,78.5%and 74.9%,respectively.Our proposed framework has state-of-the-art performance for detectingmetacarpophalangeal fractures,which has a strong potential application valuein practical clinical environments.

Borate-Modified,Flame-Retardant Paper Packaging Materials for Archive Conservation

Paper packaging materials like cardboards are widely used to protect archives which are a major kind of cultural relics.Unfortunately,paper is a combustible material,and thus exploring environment-friendly flame retardant for paper-based archive packaging material plays an important role.Herein,boric acid,borax and disodium octaborate are used to modify the craft paper-based packaging materials for archive conservation to improve fire safety.The modified craft paper exhibits much higher flame retardancy than the pristine one dose based on vertical burning tests,without much influence on mechanical properties such as tensile strength and elongation at break.Thermogravimetric analysis(TGA),scanning electron microscope(SEM),and X-ray photoelectron spectroscopy(XPS)reveal that porous glass structure is formed during the combustion,because thermal decomposition of boric acid,borax and disodium octaborate will produce porous glassy matter as B_(2)O_(3).The porous glass covers the paper surface as an insulating layer which retards the further pyrolysis and combustion,resulting in a denser carbon layer.Our study provides a robust way to reduce the fire hazard of the archive packaging material by applying environment-friendly boron-based fire retardants.

猪esr基因反转录转座子插入多态性及其与大白猪生产性能的关联性分析

雌激素受体(Estrogen receptor,esr)介导雌激素影响相关基因表达,从而调控哺乳动物的生长和繁殖机能。为了探讨esr基因的反转录转座子多态性对猪生长性能的影响,文中应用比较基因组学和生物信息学方法,预测猪esr基因的反转录转座子插入位点,采用PCR方法验证不同品种猪中插入多态性,并将该基因型与大白猪性能进行关联分析。结果显示,esr1和esr2基因验证后得到4个反转录转座子多态性位点,分别是位于esr1基因内含子2的esr1-SINE-RIP1、位于内含子5的esr1-LINE-RIP2和esr1-SINE-RIP3,以及位于esr2基因内含子1的esr2-LINE-RIP。其中esr1-SINE-RIP1的287 bp SINE插入对大白猪的活体背膘厚和100 kg体重背膘厚有显著影响(P<0.05),纯合有插入(SINE^(+/+))的活体背膘厚和100kg体重背膘厚显著高于杂合有插入(SINE^(+/-))和无插入(SINE^(-/-))型。这表明esr1-SINE-RIP1位点可作为分子标记辅助选育大白猪的背膘厚性状。

Large eddy simulation of room fire spread using a medium scale compartment made of medium density fibreboard (MDF) panels

At present,there is a shortage of experimental and simulation studies on fire spread in medium-and large-scale compartments while the existing models of the fire spread are limited for typical engineering applications.This paper proposes a new model for large-scale fire spread on medium density fibreboard(MDF)panels.Validating the model with single burning item(SBI)experiments,it is found that the numerical simulation closely predicts the experimental heat release rate(HRR)with some error near the peak.The predicted heat flux and distance of lateral flame spread are consistent with the experiments and an existing model.The effects of kinetic properties and heat of combustion are identified through a sensitivity analysis.The decrease of activation energy and increase of pre-exponential factor make the MDF easier to pyrolyze and the increase of heat of combustion enhances the flame temperature and thus provide more heat feedback to the sample surface.The low activation energy(71.9 kJ/mol)and high heat of combustion(46.5 MJ/kg)of the model ensure the occurrence of flame spread.Furthermore,the model was validated using medium-scale compartment fire experiments and the results showed that the model can accurately predict the HRR after flashover(the error is within 7%).While the burner is ignited,the predictions of in-compartment gas temperature and heat flux are more accurate.However,when the burner is extinguished,the modelled in-compartment gas temperature is lower than the experimental values,resulting in a lower heat flux prediction.The model leads to easier flame spread;therefore,the modelled flame spreads faster in the compartment compared to the experiment,and thus the HRR increases more rapidly.