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Oxidation Resistance of Form-stable Hightemperature Phase Change Thermal Energy Storage Materials Doped by Impregnated Graphite

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Journal of wuhan University of Technology(Materials Science) 2025年第1期40卷 1-12页

作者： LI Baorang DAI Jianhuan ZHANG wei LIU Xiangchen yang Liu School of Energy Power and Mechanical Engineering North China Electric Power UniversityBeijing 102206China School of Ener-gy and Environment Southeast UniversityNanjing 210096China

we adopted the solution impregnation route with aluminum dihydrogen phosphate solution as liquid medium for effective surface modification on graphite *** mass ratio of graphite to Al(H_(2)PO_(4))_(3) changed from 0.5:1 to 4:1,and the impregnation time changed from 1 to 7 *** typical composite phase change thermal storage materials doped with the as-treated graphite were fabricated using form-stable *** investigate the oxidation and anti-oxidation behavior of the impregnated graphite at high temperatures,the samples were put into a muffle furnace for a cyclic heat *** on SEM,EDS,DSC techniques,analyses on the impregnated technique suggested an optimized processing conditions of a 3 h impregnation time with the ratio of graphite:Al(H_(2)PO_(4))_(3) as 1:3 for graphite impregnation *** investigations on high-temperature phase change heat storage materials doped by the treated graphite suggested excellent oxidation resistance and thermal cycling performance.

关键词： phase change materials graphite impregnation method oxidation,sintering thermal analysis

Ideal Bi‑Based Hybrid Anode Material for Ultrafast Charging of Sodium‑Ion Batteries at Extremely Low Temperatures

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Nano-Micro Letters 2025年第3期17卷 152-167页

作者： Jie Bai Jian Hui Jia Yu wang Chun Cheng yang Qing Jiang Key Laboratory of Automobile Materials(Jilin University) Ministry of EducationSchool of Materials Science and EngineeringJilin UniversityChangchun 130022People’s Republic of China

Sodium-ion batteries have emerged as competitive substitutes for low-temperature applications due to severe capacity loss and safety concerns of lithium-ion batteries at−20°C or ***,the key capability of ultrafast charging at ultralow temperature for SIBs is rarely ***,a hybrid of Bi nanoparticles embedded in carbon nanorods is demonstrated as an ideal material to address this issue,which is synthesized via a high temperature shock *** a hybrid shows an unprecedented rate performance(237.9 mAh g^(−1)at 2 A g^(−1))at−60℃,outperforming all reported SIB anode *** with a Na_(3)V_(2)(PO_(4))_(3)cathode,the energy density of the full cell can reach to 181.9 wh kg^(−1)at−40°*** on this work,a novel strategy of high-rate activation is proposed to enhance performances of Bi-based materials in cryogenic conditions by creating new active sites for interfacial reaction under large current.

关键词： Bi nanoparticles High temperature shock High-rate activation Ultrafast charging Low-temperature sodium-ion batteries

Highly conductive and stable iodine doped argyrodite electrolyte for all-solid-state lithium batteries

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Journal of Energy Chemistry 2025年第1期100卷 50-58页

作者： Gaozhan Liu Jing Zhang Jing yang Jinghui Chen Xingyue Xiao Xiayin Yao Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences

Lithium argyrodites with high ionic conductivity and low cost are considered as one of the most prospective solid electrolytes for all-solid-state lithium ***,the poor chemical stability and compatibility with lithium metal limit their ***,Li5.4PS4.4Cl1.4I0.2solid electrolyte with high ionic conductivity of 11.49 m S cm-1and improved chemical stability is synthesized by iodine *** ultra-thin Li5.4PS4.4Cl1.4I0.2membrane with thickness of 10μm can be obtained by wet coating process,exhibiting a high ionic conductivity of 2.09 mS cm-1and low areal resistance of 1.17Ω***,iodine doping could in-situ form LiI at the lithium/electrolyte interface and improve the critical current density of Li5.4PS4.4Cl1.6from 0.8 to 1.35 mA *** resultant LiCoO2/Li5.4PS4.4Cl1.4I0.2/Li battery shows excellent cycling stability at 1 C,with a reversible specific capacity of 110.1 mA h g-1and a retention of 80.5% after 1000 *** addition,the assembled LiCoO2/Li5.4PS4.4Cl1.4I0.2membrane/Li pouch cell delivers an initial discharge capacity of 110.4 mA h g-1and 80.5% capacity retention after 100 cycles.

关键词： Solid-State Batteries

Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

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Computers, Materials & Continua 2025年第1期82卷 543-560页

作者： Hengyang Liu yang Yuan Pengcheng Ren Chengyun Song Fen Luo School of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China College of Artificial Intelligence Chongqing Technology and Business UniversityChongqing400067China

Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution ***,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than *** design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these *** be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled *** introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in *** the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local *** this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation *** two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation *** only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

关键词： Semi-supervised medical image segmentation contrastive learning stochastic augmented

Revealing and predicting the long-term biomechanical response of orthokeratology by developing a patient-specific computational model

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 156-168页

作者： Yifeng Li Zhuoran yang Ziming Yan Huibin Shi Zhanli Liu Kaijie wang Department of Engineering Mechanics School of Aerospace Engineering Tsinghua University Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology & Visual Sciences Key Lab

Orthokeratology(OK) is widely used for effective myopia correction and control. However, the incomplete understanding of its biomechanical mechanisms makes OK lens fitting rely heavily on clinician judgment, complicating accurate predictions of treatment outcomes. In this paper, we performed clinical experiments and numerical analysis to study corneal deformation modes and long-term changes in central corneal thickness. Clinical experiments were conducted on 194 Chinese myopic patients under OK treatment for 3 months. Based on the experimental data, a patient-specific computational biomechanical model for OK was established and validated. Specifically, the anisotropic mechanical properties of the cornea were incorporated into the model to describe the significant difference between its shear modulus(29.5 kPa) and tensile modulus(768.4 kPa). Additionally, a viscohyperelastic material model with a prolonged corneal relaxation time of 5.6 h was developed to capture the long-term deformation response. The results show that corneal thickness reduction in OK is primarily due to out-of-plane shear deformation,influenced by the cornea's low shear resistance. Modeling the extended corneal relaxation time is crucial for predicting long-term biomechanical responses. The computational model effectively captures long-term changes in central corneal thickness, potentially improving OK lens fitting accuracy.

关键词： orthokeratology computational biomechanical model long-term viscoelasticity myopia correction

The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 86-96页

作者： Qianyun Yun wen-Biao Han Yi-yang Guo He wang Minghui Du Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Shanghai Astronomical Observatory Chinese Academy of Sciences School of Physics and Astronomy Shanghai Jiao Tong University School of Astronomy and Space Science University of Chinese Academy of Sciences Taiji Laboratory for Gravitational wave Universe (Beijing/Hangzhou) University of Chinese Academy of Sciences Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Province Key Laboratory of Quantum Theory and Applications of MoE Lanzhou University Institute of Theoretical Physics & Research Center of Gravitation Lanzhou University International Centre for Theoretical Physics Asia-Pacific (ICTP-AP) University of Chinese Academy of Sciences (UCAS) Center for Gravitational wave Experiment National Microgravity Laboratory Institute of Mechanics Chinese Academy of Sciences

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals(EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a2-layer convolutional neural network(CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate(TPR) of 96.9% at a 1% false positive rate(FPR) for signal-to-noise ratio(SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole(SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter *** model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation.

关键词： gravitational wave EMRIs deep-learing

The Internet of Things under Federated Learning:A Review of the Latest Advances and Applications

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Computers, Materials & Continua 2025年第1期82卷 1-39页

作者： Jinlong wang Zhenyu Liu Xingtao yang Min Li Zhihan Lyu School of Information and Control Engineering Qingdao University of TechnologyQingdao260043China Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving Anhui Jianzhu UniversityHefei230000China Department of Game Design Uppsala UniversityUppsala75310Sweden

with the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application *** the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious *** learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data *** review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy *** this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy ***,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning *** also list various application scenarios of FL on the ***,we propose several open research challenges and possible solutions.

关键词： Federated learning Internet of Things sensors machine learning privacy security

Smart Gas Sensors:Recent Developments and Future Prospective

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Nano-Micro Letters 2025年第3期17卷 55-86页

作者： Boyang Zong Shufang wu Yuehong yang Qiuju Li Tian Tao Shun Mao College of Environmental Science and Engineering Biomedical Multidisciplinary Innovation Research InstituteShanghai East HospitalState Key Laboratory of Pollution Control and Resource ReuseTongji University1239 Siping RoadShanghai 200092People’s Republic of China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092People’s Republic of China Microbiome Medicine Center Department of Laboratory MedicineZhujiang HospitalSouthern Medical UniversityGuangzhou 510280People’s Republic of China

Gas sensor is an indispensable part of modern society withwide applications in environmental monitoring,healthcare,food industry,public safety,*** the development of sensor technology,wireless communication,smart monitoring terminal,cloud storage/computing technology,and artificial intelligence,smart gas sensors represent the future of gassensing due to their merits of real-time multifunctional monitoring,earlywarning function,and intelligent and automated *** electronicand optoelectronic gas sensors have been developed for high-performancesmart gas *** the development of smart terminals and the maturityof integrated technology,flexible and wearable gas sensors play an increasingrole in gas *** review highlights recent advances of smart gassensors in diverse *** structural components and fundamentalprinciples of electronic and optoelectronic gas sensors are described,andflexible and wearable gas sensor devices are ***,sensorarray with artificial intelligence algorithms and smart gas sensors in“Internet of Things”paradigm are ***,the challengesand perspectives of smart gas sensors are discussed regarding the future need of gas sensors for smart city and healthy living.

关键词： Smart gas sensor Electronic sensor Optoelectronic sensor Flexible and wearable sensor Artificial intelligence

A novel method for clustering cellular data to improve classification

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Neural Regeneration Research 2025年第9期20卷 2697-2705页

作者： Diek w.wheeler Giorgio A.Ascoli Center for Neural Informatics Structures&PlasticityKrasnow Institute for Advanced Study Bioengineering Department Volgenau School of Engineering George Mason University FairfaxVAUSA

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are *** recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within *** we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical *** general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical *** demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

关键词： cellular data clustering dendrogram data classification Levene's one-tailed statistical test unsupervised hierarchical clustering

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