Approaching Ultimate Synthesis Reaction Rate of Ni-Rich Layered Cathodes for Lithium-Ion Batteries

Nickel-rich layered oxide LiNi_(x)Co_(y)MnzO_(2)(NCM,x+y+z=1)is the most promising cathode material for high-energy lithium-ion batteries.However,conventional synthesis methods are limited by the slow heating rate,sluggish reaction dynamics,high energy consumption,and long reaction time.To overcome these chal-lenges,we first employed a high-temperature shock(HTS)strategy for fast synthesis of the NCM,and the approaching ultimate reaction rate of solid phase transition is deeply investigated for the first time.In the HTS process,ultrafast average reaction rate of phase transition from Ni_(0.6)Co_(0.2)Mn_(0.2)(OH)_(2) to Li-containing oxides is 66.7(%s^(-1)),that is,taking only 1.5 s.An ultrahigh heating rate leads to fast reaction kinetics,which induces the rapid phase transition of NCM cathodes.The HTS-synthesized nickel-rich layered oxides perform good cycling performances(94%for NCM523,94%for NCM622,and 80%for NCM811 after 200 cycles at 4.3 V).These findings might also assist to pave the way for preparing effectively Ni-rich layered oxides for lithium-ion batteries.

基于同步辐射光源X射线散射对聚乙醇酸挤出棒材退火条件的优化

文中用同步辐射光源对聚乙醇酸(PGA)挤出棒材的切片进行广角X射线散射(WAXS)和小角X射线散射(SAXS)表征,研究聚乙醇酸棒材在挤出成型后优选的退火条件,以尽可能消除内应力,改善棒材后续的机械加工性。WAXS结果表明,高温更有利于微观应变的减小,但较高温时晶粒尺寸会随退火时间的延长而减小。SAXS结果表明,高温更有利于取向程度的减小,退火时间对取向程度的影响明显弱于退火温度。综合各数据建立目标函数,并利用响应面分析法进行分析。结果表明,在实验范围内,PGA棒材的优选退火条件为200℃退火2h。

Analysis and prediction of sputtering yield using combined hierarchical clustering analysis and artificial neural network algorithms

Sputtering is a crucial technology in fields such as electric propulsion, materials processing and semiconductors. Modeling of sputtering is significant for improving thruster design and designing material processing control algorithms. In this study we use the hierarchical clustering analysis algorithm to perform cluster analysis on 17 descriptors related to sputtering. These descriptors are divided into four fundamental groups, with representative descriptors being the mass of the incident ion, the formation energy of the incident ion, the mass of the target and the formation energy of the target. We further discuss the possible physical processes and significance involved in the classification process, including cascade collisions, energy transfer and other processes. Finally, based on the analysis of the above descriptors, several neural network models are constructed for the regression of sputtering threshold E_(th), maximum sputtering energy E_(max) and maximum sputtering yield SY_(max). In the regression model based on 267 samples, the four descriptor attributes showed higher accuracy than the 17 descriptors(R^(2) evaluation) in the same neural network structure, with the 5×5 neural network structure achieving the highest accuracy, having an R^(2) of 0.92. Additionally, simple sputtering test data also demonstrated the generalization ability of the 5×5 neural network model, the error in maximum sputtering yield being less than 5%.

EGFR外显子20插入突变型NSCLC治疗的研究进展

肺癌已成为对人类健康威胁最大的肿瘤之一,死亡率位居肿瘤死因之首,非小细胞肺癌(non-small cell lung cancer, NSCLC)占肺癌的80%-85%,晚期NSCLC的治疗以化疗为主,但5年的生存率较低。表皮生长因子受体(epidermal growth factor receptor, EGFR)基因突变是肺癌中最常见的驱动基因变异,而EGFR外显子20插入(EGFR exon 20 insertions,EGFRex20ins)突变型属于其中一种罕见突变,在所有EGFR突变中占4%-10%,在NSCLC中约占1.8%。近年来,以EGFR酪氨酸激酶抑制剂(EGFR-tyrosine kinase inhibitors, EGFR-TKIs)为代表的靶向治疗已经成为晚期NSCLC患者一个重要的治疗选择,然而EGFR ex20ins突变型的NSCLC患者对大部分EGFR-TKIs治疗并不敏感。目前,部分针对EGFR ex20ins突变的靶向药物取得了明显疗效,部分药物仍在临床研究中。本文将针对EGFR ex20ins突变的各种治疗方法及其疗效进行阐述。

A thorough study on genetic algorithms in feedback-based wavefront shaping

Feedback-based wavefront shaping focuses light through scattering media by employing phase optimization algorithms.Genetic algorithms(GAs),inspired by the process of natural selection,are well suited for phase optimization in wavelfront shaping problems.In 2012,Conkey et al.first introduced a GA into feedback-based wavefront shaping to find the optimum phase map.Since then,due to its siuperior performance in noisy environment,the GA has been widely adopted by lots of implementations.However,there have been limited studies discussing and optimizing the detailed procedures of the GA.To fill this blank,in this study,we performed a thorough study on the performance of the GA for focusing light through scattering media.Using numerical tools,we evaluated certain procedures that can be potentially improved and provided guidance on how to choose certain parameters appropriately.This study is beneficial in improving the performance of wavefront shaping systems with GAs.

African swine fever virus MGF505-3R inhibits cGAS-STING-mediated IFN-βpathway activation by degrading TBK1

African swine fever virus(ASFV)is an important pathogen causing acute infectious disease in domestic pigs and wild boars that seriously endangers the global swine industry.As ASFV is structurally complex and encodes a large number of functional proteins,no effective vaccine has been developed to date.Thus,dissecting the mechanisms of immune escape induced by ASFV proteins is crucial.A previous study showed that the ASFV-encoded protein is an important factor in host immunity.In this study,we identified a negative regulator,MGF505-3R,that significantly downregulated cGAS/STING-and poly(dG:dC)-mediated IFN-βand interferon stimulation response element(ISRE)reporter activity and suppressed IFNB1 and IFIT2 mRNA levels.In addition,TBK1,IRF3 and IκBαphosphorylation levels were also inhibited.Mechanistically,MGF505-3R interacted with cGAS/TBK1/IRF3 and targeted TBK1 for degradation,thereby disrupting the cGAS-STING-mediated IFN-βsignaling pathway,which appears to be highly correlated with autophagy.Knockdown MGF505-3R expression enhanced IFN-βand IL-1βproduction.Taken together,our study revealed a negative regulatory mechanism involving the MGF505-3R-cGAS-STING axis and provided insights into an evasion strategy employed by ASFV that involves autophagy and innate signaling pathways.

INTS-MFS:A novel method to predict microRNA-disease associations by integrating network topology similarity and microRNA function similarity

Identifying associations between microRNAs(miRNAs)and diseases is very important to understand the occurrence and development of human diseases.However,these existing methods suffer from the following limitation:first,some disease-related miRNAs are obtained from the miRNA functional similarity networks consisting of heterogeneous data sources,i.e.,disease similarity,protein interaction network,gene expression.Second,little approaches infer disease-related miRNAs depending on the network topological features without the functional similarity of miRNAs.In this paper,we develop a novel model of Integrating Network Topology Similarity and MicroRNA Function Similarity(INTS-MFS).The integrated miRNA similarities are calculated based on miRNA functional similarity and network topological characteristics.INTS-MFS obtained AUC of 0.872 based on five-fold cross-validation and was applied to three common human diseases in case studies.As a results,30 out of top 30 predicted Prostatic Neoplasm-related miRNAs were included in the two databases of dbDEMC and PhenomiR2.0.29 out of top 30 predicted Lung Neoplasm-related miRNAs and Breast Neoplasm-related miRNAs were included in dbDEMC,PhenomiR2.0 and experimental reports.Moreover,INTS-MFS found unknown association with hsa-mir-371a in breast cancer and lung cancer,which have not been reported.It provides biologists new clues for diagnosing breast and lung cancer.

MDA-TOEPGA:A novel method to identify miRNA-disease association based on two-objective evolutionary programming genetic algorithm

The association between miRNA and disease has attracted more and more attention.Until now,existing methods for identifying miRNA related disease mainly rely on top-ranked association model,which may not provide a full landscape of association between miRNA and disease.Hence there is strong need of new computational method to identify the associations from miRNA group view.In this paper,we proposed a framework,MDA-TOEPGA,to identify miRNAdisease association based on two-objective evolutionary programming genetic algorithm,which identifies latent miRNAdisease associations from the view of functional module.To understand the miRNA functional module in diseases,the case study is presented.We have been compared MDA-TOEPGA with several state-of-the-art functional module algorithm.Experimental results showed that our method cannot only outperform classical algorithms,such as K-means,IK-means,MCODE,HC-PIN,and ClusterONE,but can also achieve an ideal overall performance in terms of a composite score consisting of f1,Sensitivity,and Accuracy.Altogether,our study showed that MDA-TOEPGA is a promising method to investigate miRNA-disease association from the landscapes of functional module.

Boosting cycling stability by regulating surface oxygen vacancies of LNMO by rapid calcination

Spinel LiNi_(0.5-x)Mn_(1.5+x)O_(4)(LNMO)has attracted intensive interest for lithium-ion battery due to its high voltage and high energy density.However,severe capacity fade attributed to unstable surface structure has hampered its commercialization.Oxygen vacancies(OVs)tend to occur in the surface of the material and lead to surface structure reconstruction,which deteriorates the battery performance during electrochemical cycling.Here,we utilize high-temperature-shock(HTS)method to synthesize LNMO materials with fewer surface OVs.Rapid calcination drives lower surface OVs concentration,reducing the content of Mn^(3+)and surface reconstruction layers,which is beneficial to obtain a stable crystal structure.The LNMO material synthesized by HTS method delivers an initial capacity of 127 mAh·g^(-1) at 0.1 C and capacity retention of 81.6%after 300 cycles at 1 C,and exhibits excellent performance at low temperature.

Recycle spent graphite to defect-engineered,high-power graphite anode

Graphite is a dominant anode material for lithium-ion batteries(LIBs)due to its outstanding electrochemical performance.However,slow lithium ion(Li+)kinetics of graphite anode restricts its further application.Herein,we report that high-temperature shock(HTS)can drive spent graphite(SG)into defect-rich recycled graphite(DRG)which is ideal for high-rate anode.The DRG exhibits the charging specific capacity of 323 mAh/g at a high current density of 2 C,which outperforms commercial graphite(CG,120 mAh/g).The eminent electrochemical performance of DRG can be attributed to the recovery of layered structure and partial remaining defects of SG during ultrafast heating and cooling process,which can effectively reduce total strain energy,accelerate the phase transition in thermodynamics and improve the Li+diffusion.This study provides a facile strategy to guide the re-graphitization of SG and design high performance battery electrode materials by defect engineering from the atomic level.

Discovery of tip induced unconventional superconductivity on Weyl semimetal

Weyl fermion is a massless Dirac fermion with definite chirality,which has been long pursued since 1929.Though it has not been observed as a fundamental particle in nature,Weyl fermion can be realized as low-energy excitation around Weyl point in Weyl semimetal,which possesses Weyl fermion cones in the bulk and nontrivial Fermi arc states on the surface. As a firstly discovered Weyl semimetal,Ta As crystal possesses 12 pairs of Weyl points in the momentum space,which are topologically protected against small perturbations. Here,we report for the first time the tip induced superconductivity on Ta As crystal by point contact spectroscopy. The zero bias conductance peak as well as a conductance plateau with double conductance peaks and sharp double dips are observed in the point contact spectra simultaneously,indicating unconventional superconductivity. Our further theoretical study suggests that the induced superconductivity may have nontrivial topology. The present work opens a new route in investigating the novel superconducting states based on Weyl materials.

Generalizing the Gerchberg–Saxton algorithm for retrieving complex optical transmission matrices

The Gerchberg–Saxton(GS)algorithm,which retrieves phase information from the measured intensities on two related planes(the source plane and the target plane),has been widely adopted in a variety of applications when holographic methods are challenging to be implemented.In this work,we showed that the GS algorithm can be generalized to retrieve the unknown propagating function that connects these two planes.As a proof-of-concept,we employed the generalized GS(GGS)algorithm to retrieve the optical transmission matrix(TM)of a complex medium through the measured intensity distributions on the target plane.Numerical studies indicate that the GGS algorithm can efficiently retrieve the optical TM while maintaining accuracy.With the same training data set,the computational time cost by the GGS algorithm is orders of magnitude less than that consumed by other non-holographic methods reported in the literature.Besides numerical investigations,we also experimentally demonstrated retrieving the optical TMs of a stack of ground glasses and a 1-m-long multimode fiber using the GGS algorithm.The accuracy of the retrieved TM was evaluated by synthesizing high-quality single foci and multiple foci on the target plane through these complex media.These results indicate that the GGS algorithm can handle a large TM with high efficiency,showing great promise in a variety of applications in optics.

Investigation of point-contact Andreev reflection on magnetic Weyl semimetal Co3Sn2S2

Magnetic Weyl semimetals(WSMs)with broken time-reversal symmetry(TRS)hosting topological band structures are expected to provide an ideal platform for investigating topological superconductivity and spintronics.However,the experimental verification of magnetic WSMs is very challenging.Very recently,the kagome magnet Co3Sn2S2 was confirmed to be a magnetic WSM by both angle-resolved photoemission spectroscopy and scanning tunneling spectroscopy and consequently has become the focus of great attention.This paper reports a point-contact Andreev-reflection spectroscopy(PCARS)investigation on the(001)surface and the side surface of the Co3Sn2S2 single crystals,respectively.The measurements from the sample’s(001)and side surfaces provide experimental evidence for transport spin polarization in the Co3Sn2S2 magnetic WSM.Furthermore,the superconducting proximity effect in the Co3Sn2S2 single crystal is successfully detected.The point-contact spectra(PCS)along the in-plane direction cannot be well fitted by theoretical models based on s-wave pairing,indicating that possible triplet p-wave superconductivity may be triggered at the interface,which paves the way for the future exploration of the topological superconductivity and Majorana states in broken TRS WSMs.

Two-stage matrix-assisted glare suppression at a large scale

Scattering-induced glares hinder the detection of weak objects in various scenarios.Recent advances in wavefront shaping show one can not only enhance intensities through constructive interference but also suppress glares within a targeted region via destructive interference.However,due to the lack of a physical model and mathematical guidance,existing approaches have generally adopted a feedback-based scheme,which requires timeconsuming hardware iteration.Moreover,glare suppression with up to tens of speckles was demonstrated by controlling thousands of independent elements.Here,we reported the development of a method named twostage matrix-assisted glare suppression(TAGS),which is capable of suppressing glares at a large scale without triggering time-consuming hardware iteration.By using the TAGS,we experimentally darkened an area containing 100 speckles by controlling only 100 independent elements,achieving an average intensity of only 0.11 of the original value.It is also noticeable that the TAGS is computationally efficient,which only takes 0.35 s to retrieve the matrix and 0.11 s to synthesize the wavefront.With the same number of independent controls,further demonstrations on suppressing larger scales up to 256 speckles were also reported.We envision that the superior performance of the TAGS at a large scale can be beneficial to a variety of demanding imaging tasks under a scattering environment.

Vapors in the ambient-A complication in tribological studies or an engineering solution of tribological problems?

Tribology involves not only two-body contacts of two solid materials-a substrate and a counter-surface;it often involves three-body contacts whether the third body is intentionally introduced or inevitably added during the sliding or rubbing.The intentionally added third body could be lubricant oil or engineered nano-material used to mitigate the friction and wear of the sliding contact.The inevitably added third body could be wear debris created from the substrate or the counter surface during sliding.Even in the absence of any solid third-body between the sliding surfaces,molecular adsorption of water or organic vapors from the surrounding environment can dramatically alter the friction and wear behavior of solid surfaces tested in the absence of lubricant oils.This review article covers the last case:the effects of molecular adsorption on sliding solid surfaces both inevitably occurring due to the ambient test and intentionally introduced as a solution for engineering problems.We will review how adsorbed molecules can change the course of wear and friction,as well as the mechanical and chemical behavior,of a wide range of materials under sliding conditions.

Erratum to: Vapors in the ambient-A complication in tribological studies or an engineering solution of tribological problems?

Erratum to Friction 3(2):85-114(2015)DOI 10.1007/s40544-015-0083-5 The original version of this article contained the incorrect caption of Fig.7 on page 93,instead of Fig.7 Line profiles of the wear tracks on different glasses under different humidity conditions:(a)soda-lime silicate glass;(b)fused quartz;(c)BF33;(d)AF45;(e)sodium alumino-silicate;(f)K-exchanged alumino-silicate.(a)and(b)are reproduced with permission from Ref.[110].Copyright John Wiley and Sons,2013.(c)and(d)are reproduced with permission from Ref.[124].Copyright John Wiley and Sons,2014.(e)and(f)are reproduced with permission from Ref.[125].Copyright Springer,2014.

MTMO： an efficient network-centric algorithm for subtree counting and enumeration

Background： The frequency of small subtrees in biological, social, and other types of networks could shed light into the structure, function, and evolution of such networks. However, counting all possible subtrees of a prescribed size can be computationally expensive because of their potentially large number even in small, sparse networks. Moreover, most of the existing algorithms for subtree counting belong to the subtree-centric approaches, which search for a specific single subtree type at a time, potentially taking more time by searching again on the same network. Methods： In this paper, we propose a network-centric algorithm （MTMO） to efficiently count k-size subtrees. Our algorithm is based on the enumeration of all connected sets of k-1 edges, incorporates a labeled rooted tree data structure in the enumeration process to reduce the number of isomorphism tests required, and uses an array-based indexing scheme to simplify the subtree counting method. Results： The experiments on three representative undirected complex networks show that our algorithm is roughly an order of magnitude faster than existing subtree-centric approaches and base network-centric algorithm which does not use rooted tree, allowing for counting larger subtrees in larger networks than previously possible. We also show major differences between unicellular and multicellular organisms. In addition, our algorithm is applied to find network motifs based on pattern growth approach. Conclusions： A network-centric algorithm which allows for a This enables us to count larger motif in larger networks than faster counting of non-induced subtrees is proposed previously.

Machine Learning for Selecting Important Clinical Markers of Imaging Subgroups of Cerebral Small Vessel Disease Based on a Common Data Model

Differences in the imaging subgroups of cerebral small vessel disease(CSVD)need to be further explored.First,we use propensity score matching to obtain balanced datasets.Then random forest(RF)is adopted to classify the subgroups compared with support vector machine(SVM)and extreme gradient boosting(XGBoost),and to select the features.The top 10 important features are included in the stepwise logistic regression,and the odds ratio(OR)and 95%confidence interval(CI)are obtained.There are 41290 adult inpatient records diagnosed with CSVD.Accuracy and area under curve(AUC)of RF are close to 0.7,which performs best in classification compared to SVM and XGBoost.OR and 95%CI of hematocrit for white matter lesions(WMLs),lacunes,microbleeds,atrophy,and enlarged perivascular space(EPVS)are 0.9875(0.9857−0.9893),0.9728(0.9705−0.9752),0.9782(0.9740−0.9824),1.0093(1.0081−1.0106),and 0.9716(0.9597−0.9832).OR and 95%CI of red cell distribution width for WMLs,lacunes,atrophy,and EPVS are 0.9600(0.9538−0.9662),0.9630(0.9559−0.9702),1.0751(1.0686−1.0817),and 0.9304(0.8864−0.9755).OR and 95%CI of platelet distribution width for WMLs,lacunes,and microbleeds are 1.1796(1.1636−1.1958),1.1663(1.1476−1.1853),and 1.0416(1.0152−1.0687).This study proposes a new analytical framework to select important clinical markers for CSVD with machine learning based on a common data model,which has low cost,fast speed,large sample size,and continuous data sources.