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High-temperature-tolerant flexible supercapacitors: Gel polymer electrolytes and electrode materials

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Journal of Energy Chemistry 2025年第1期100卷 426-457页

作者： Chong Peng Xinyi Huang Mingwei Zhao Shuling Liao Quanhong Yang Nianjun Yang Siyu yu School of Chemistry & Chemical Engineering Southwest University Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology and Collaborative Innovation Center of Chemical Science and Engineering (Tianjin) Tianjin University Department of Chemistry & IMO-IMOMEC Hasselt University

The development of flexible supercapacitors(FSCs) capable of operating at high temperatures is crucial for expanding the application areas and operating conditions of supercapacitors. Gel polymer electrolytes and electrode materials stand as two key components that significantly impact the efficacy of hightemperature-tolerant FSCs(HT-FSCs). They should not only exhibit high electrochemical performance and excellent flexibility, but also withstand intense thermal stress. Considerable efforts have been devoted to enhancing their thermal stability while maintaining high electrochemical and mechanical performance. In this review, the fundamentals of HT-FSCs are outlined. A comprehensive overview of state-of-the-art progress and achievements in HT-FSCs, with a focus on thermally stable gel polymer electrolytes and electrode materials is provided. Finally, challenges and future perspectives regarding HT-FSCs are discussed, alongside strategies for elevating operational temperatures and *** review offers both theoretical foundations and practical guidelines for designing and manufacturing HT-FSCs, further promoting their widespread adoption across diverse fields.

关键词： Polymer membrane electrodes

Personalized federated few-shot node classification

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Science China(Information Sciences) 2025年第1期68卷 204-218页

作者： yunfeng ZHAO Xintong HE Guoxian yu Jun WANG Yongqing ZHENG Carlotta DOMENICONI School of Software Shandong University Joint SDU-NTU Centre for Artificial Intelligence Research Shandong University Department of Mathematics National University of Singapore Department of Computer Science George Mason University

Personalized federated learning(PFL) aims to train customized models for individual clients in a decentralized setting, with the account of non-independent and identically distributed data across clients. However, most PFL methods adopt uniform classification layers for diverse clients and give rise to error-prone predictions, due to the task heterogeneity notably prominent in decentralized graph data scenarios. Although some PFL solutions setup client-specific classification layers for each client and optimize them only locally, they are corrupted with limited local training data. We propose an innovative solution called federated parameter decoupling and node augmentation(Fed PANo) to address these problems and to achieve personalized federated few-shot node classification, which is a prevalent and challenging but unexplored topic. Specifically, Fed PANo first separates the local model into the GNN and classifier to handle unique client-specific task variations. The GNN is trained through federated learning to capture shared knowledge of graph nodes across clients, while the classifier is custom-designed and trained individually for each client. Additionally, a generic classifier shared among clients is adopted to encourage the GNN's grasp of shared information. Then Fed PANo further proposes the node generator along with its local and collaborative training strategies to deal with the node scarcity of clients. Extensive experimental results on benchmark datasets confirm that Fed PANo outperforms eight competitive baselines across different settings.

关键词： personalized federated learning few-shot learning node classification task heterogeneity parameter decoupling node augmentation

Vector meson's spin alignments in high energy reactions

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 5-20页

作者： Jin-Hui Chen Zuo-Tang Liang yu-Gang Ma Xin-Li Sheng Qun Wang Key Laboratory of Nuclear Physics and Ion-beam Application (MoE) Institute of Modern PhysicsFudan University Institute of Frontier and Interdisciplinary Science Key Laboratory of Particle Physics and Particle Irradiation (MOE)Shandong University INFN-Firenze Department of Modern Physics University of Science and Technology of China School of Mechanics and Physics Anhui University of Science and Technology

The global spin alignment of vector mesons has been observed by the STAR collaboration at the Relativistic Heavy Ion Collider （RHIC） at Brookhaven National Laboratory （BNL）.It provides a unique opportunity to probe the correlation between the polarized quark and antiquark in the strongly coupled quark-gluon plasma （sQGP） produced in relativistic heavy ion collisions,opening a new window to explore the properties of s *** addition,spin alignments of vector mesons have also been observed in other high-energy particle collisions such as e+e-annihilations at high energies where hadron production is dominated by quark fragmentation *** results obtained are quite different from those obtained in heavy ion collisions where quark coalescence/combination mechanism dominates suggesting strong dependence on hadronization *** comprehensive studies in different hadronization processes are *** this article,we present a brief review of theoretical and experimental advances in the study of vector meson’s spin alignments in a variety of high-energy particle collisions,with emphasis on hadronization mechanisms.

关键词： vector meson spin alignment global polarization relativistic heavy ion collisions fragmentation

Fluorinated poly(p-triphenylene isatin) anion exchange membranes based on hydrophilic hydroxyl side chain modulation for fuel cells

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Journal of Energy Chemistry 2025年第1期100卷 702-709页

作者： Yiman Gu Yanchao Zhang Zhanyu Li Yijia lei Baozeng Sun Xiaoyu yu Zhe Wang College of Chemistry and Life Science Changchun University of Technology College of Chemical Engineering Changchun University of Technology School of Materials Science and Engineering Changchun University of Technology Key Laboratory of Advanced Functional Polymer Membrane Materials of Jilin Province

The development of alkaline fuel cells is moving forward at an accelerated pace,and the application of ether-free bonded polymers to anion exchange membranes （AEMs） has been widely ***,the question of the“trade-off”between AEM ionic conductivity and dimensional stability remains *** strategy of inducing microphase separation to improve the performance of AEM has attracted much attention recently,but the design of optimal molecular structures is still being ***,this work introduced different ratios of 3-bromo-1,1,1-trifluoroacetone （x=40,50,and 60） into the main chain of poly（p-terphenylene isatin）.Because fluorinated groups have excellent hydrophobicity,hydrophilic hydroxyl-containing side chains are introduced to jointly adjust the formation of phase separation *** results show that PTI-PTF50-NOH AEM with the appropriate fluorinated group ratio has the best ionic conductivity and alkali stability under the combined effect of *** has an ionic conductivity of 133.83 mS cm-1at 80°*** addition,the OH-conductivity remains at 89%of the initial value at 80°C and 3 M KOH for 1056 h of *** cell polarization curve based on PTI-PTF50-NOH shows a power density of 734.76 mW cm-2at a current density of 1807.7 mA cm-2.

关键词： Potassium hydroxide

Recent Advances in Artificial Sensory Neurons:Biological Fundamentals,Devices,Applications,and Challenges

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Nano-Micro letters 2025年第3期17卷 168-216页

作者： Shuai Zhong Lirou Su Mingkun Xu Desmond Loke Bin yu Yishu Zhang Rong Zhao Guangdong Institute of Intelligence Science and Technology HengqinZhuhai 519031People’s Republic of China College of Integrated Circuits Zhejiang UniversityHangzhou 3112000People’s Republic of China ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou 310027People’s Republic of China Department of Science Mathematics and TechnologySingapore University of Technology and DesignSingapore 487372Singapore Department of Precision Instruments Tsinghua UniversityBeijing 100084People’s Republic of China Center for Brain‑Inspired Computing Research Tsinghua UniversityBeijing 100084People’s Republic of China IDG/McGovern Institute for Brain Research Tsinghua UniversityBeijing 100084People’s Republic of China

Spike-based neural networks,which use spikes or action potentialsto represent information,have gained a lot of attention because of their high energyefficiency and low power *** fully leverage its advantages,convertingthe external analog signals to spikes is an essential *** including analog-to-digital converters or ring oscillators,and sensorssuffer from high power and area *** efforts are devoted to constructingartificial sensory neurons based on emerging devices inspired by the biologicalsensory *** can simultaneously perform sensing and spike conversion,overcoming the deficiencies of traditional sensory *** review summarizesand benchmarks the recent progress of artificial sensory *** starts with thepresentation of various mechanisms of biological signal transduction,followed bythe systematic introduction of the emerging devices employed for artificial ***,the implementations with different perceptual capabilitiesare briefly outlined and the key metrics and potential applications are also ***,we highlight the challenges and perspectives for the future development of artificial sensory neurons.

关键词： Artificial intelligence Emerging devices Artificial sensory neurons Spiking neural networks Neuromorphic sensing

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Reversible Li2C2O4 product regulation in Li–CO2 batteries using single-atom alloy engineering

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Science China Chemistry 2025年

作者： Xinxin Zhang yu Wang Yafei Li Jiangsu Key Laboratory of New Power Batteries Jiangsu Collaborative Innovation Centre of Biomedical Functional Materials School of Chemistry and Materials Science Nanjing Normal University

The efficiency of lithium-carbon dioxide （Li–CO2） batteries is limited by the sluggish generation/decomposition kinetics of the discharge products （i.e.,Li2CO3and carbon） despite the use of state-of-the-art noble metal （NM） ***2C2O4has emerged as a promising alternative product;however,the thermodynamic instability of Li2C2O4causes it to generally appear as an intermediate in Li2CO3pathways rather than a product during ***,using comprehensive first-principles calculations,we report tailoring the discharge products of Li–CO2batteries to Li2C2O4via single-atom alloy （SAA） engineering,in which NM-doped copper SAAs （NM1Cu） are constructed for *** NM1Cu catalysts display a free-atom-like narrow d-band state and unique charge distribution over the solute NM1atom and the surrounding matrix Cu *** an optimized electronic structure is substantial in Ru1Cu and Ir1Cu,which is conducive to the co-activation and coupling of *** lithiation of*CO2intermediate that breaks the charge symmetry of the central C species can further reduce the kinetic barriers of the C–C *** advantages promote Li2C2O4generation on Ru1Cu and Ir1Cu while suppress its subsequent ***,they exhibit low theoretical overpotentials and enable facile delithiation under normal charging *** work proposes a universal principle of manipulating electronic structures to achieve Li2C2O4product,which provides practical guidance for the development of Li–CO2batteries.

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Engine Misfire Fault Detection Based on the Channel Attention Convolutional Model

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Computers, Materials & Continua 2025年第1期82卷 843-862页

作者： Feifei yu Yongxian Huang Guoyan Chen Xiaoqing Yang Canyi Du Yongkang Gong School of Mechatronic Engineering Guangdong Polytechnic Normal UniversityGuangzhou510665China School of Automotive and Transportation Engineering Guangdong Polytechnic Normal UniversityGuangzhou510665China

To accurately diagnosemisfire faults in automotive engines,we propose a Channel Attention Convolutional Model,specifically the Squeeze-and-Excitation Networks(SENET),for classifying engine vibration signals and precisely pinpointing misfire *** the experiment,we established a total of 11 distinct states,encompassing the engine’s normal state,single-cylinder misfire faults,and dual-cylinder misfire faults for different *** collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,*** collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and prevent overlap between the two *** results revealed that,with a vibration acceleration sequence of 1000 time steps(approximately 50 ms)as input,the SENET model achieved a misfire fault detection accuracy of 99.8%.For comparison,we also trained and tested several commonly used models,including Long Short-Term Memory(LSTM),Transformer,and Multi-Scale Residual Networks(MSRESNET),yielding accuracy rates of 84%,79%,and 95%,*** underscores the superior accuracy of the SENET model in detecting engine misfire faults compared to other ***,the F1 scores for each type of recognition in the SENET model surpassed 0.98,outperforming the baseline *** analysis indicated that the misclassified samples in the LSTM and Transformer models’predictions were primarily due to intra-class misidentifications between single-cylinder and dual-cylinder misfire *** delve deeper,we conducted a visual analysis of the features extracted by the LSTM and SENET models using T-distributed Stochastic Neighbor Embedding(T-SNE)*** findings revealed that,in the LSTMmodel,data points of the same type tended to cluster together with significant ***,in the SENET model,data points of various types were more widely and evenly dispersed,demonstra

关键词： Channel attention SENET model engine misfire fault fault detection

GFRF R-CNN:Object Detection Algorithm for Transmission Lines

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Computers, Materials & Continua 2025年第1期82卷 1439-1458页

作者： Xunguang Yan Wenrui Wang Fanglin Lu Hongyong Fan Bo Wu Jianfeng yu Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai201210China University of Chinese Academy of Sciences Beijing100049China Jingwei Textile Machinery Co. Ltd.Beijing100176China

To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related *** increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution *** study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission *** algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine *** Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and *** incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s *** results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster *** highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.

关键词： Faster R-CNN transmission line object detection GIOU GFR

Evolutionary Particle Swarm Optimization Algorithm Based on Collective Prediction for Deployment of Base Stations

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Computers, Materials & Continua 2025年第1期82卷 345-369页

作者： Jiaying Shen Donglin Zhu yujia Liu leyi Wang Jialing Hu Zhaolong Ouyang Changjun zhou Taiyong Li School of Computer Science and Technology Zhejiang Normal UniversityJinhua321004China School of Future Technologies Jiangxi Institute of Applied Science and TechnologyNanchang330000China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu611130China

The wireless signals emitted by base stations serve as a vital link connecting people in today’s society and have been occupying an increasingly important role in real *** development of the Internet of Things(IoT)relies on the support of base stations,which provide a solid foundation for achieving a more intelligent way of *** a specific area,achieving higher signal coverage with fewer base stations has become an urgent ***,this article focuses on the effective coverage area of base station signals and proposes a novel Evolutionary Particle Swarm Optimization(EPSO)algorithm based on collective prediction,referred to herein as *** a new strategy called neighbor-based evolution prediction(NEP)addresses the issue of premature convergence often encountered by *** also employs a strengthening evolution(SE)strategy to enhance the algorithm’s global search capability and efficiency,ensuring enhanced robustness and a faster convergence speed when solving complex optimization *** better adapt to the actual communication needs of base stations,this article conducts simulation experiments by changing the number of base *** experimental results demonstrate thatunder the conditionof 50 ormore base stations,ECPPSOconsistently achieves the best coverage rate exceeding 95%,peaking at 99.4400%when the number of base stations reaches *** results validate the optimization capability of the ECPPSO algorithm,proving its feasibility and *** ablative experiments and comparisons with other algorithms highlight the advantages of ECPPSO.

关键词： Particle swarm optimization effective coverage area global optimization base station deployment