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Constructing nitrided aluminum metal skeleton to improve the reversibility and cyclicity of Si film electrode

Progress in Natural Science:Materials International 2023年第6期33卷 789-796页

作者： Jiali Tong Tingfeng Guan Qilin Tong Yan Cheng Zhaozhe Yu Guangxi Key Laboratory of Manufacturing Systems and Advanced Manufacturing Technology Guilin University of Electronic Technology

Silicon is a promising anode material for lithium-ion batteries（LIBs） due to its high theoretical capacity, low discharge potential, environmental friendliness and cost-effectiveness. However, its low conductivity, high volume variation and poor cycling performance hamper its practical application. Herein, we prepare Si-Al（Al N）composite film anodes containing a nitrided aluminum metal conductive skeleton. The Al（Al N） creates a multiphase interface, buffers the volume expansion of the electrode, and also plays a role in conducting electricity and supporting the electrode structure. As a result, the Si-Al（Al N） electrode exhibits excellent performance with a high initial coulombic efficiency of 86 %, an outstanding cycling stability of roughly 1955 m Ah g-1after 200 cycles at420 m A g-1and retains 916 m Ah g-1after 300 cycles even at 4200 m A g-1（the capacity retention rate of 83 %）.This work provides an effective method for adjusting anode performance with greater freedom.

关键词： Silicon film anode Nitrided aluminum metal conducting skeleton Cycling stability Lithium-ion battery

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High-energy-density lithium manganese iron phosphate for lithium-ion batteries:Progresses,challenges,and prospects

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Journal of Energy Chemistry 2025年第1期100卷 1-17页

作者： Bokun Zhang Xiaoyun Wang Shuai Wang Yan Li Libo Chen Handong Jiao Zhijing Yu Jiguo Tu State Key Laboratory of Advanced Metallurgy University of Science and Technology BeijingBeijing 100083China Research Institute for Intelligent Wearable Systems The Hong Kong Polytechnic University Hong Kong 9977China Institute of Advanced Structure Technology Beijing Institute of TechnologyBeijing 100081China

The soaring demand for smart portable electronics and electric vehicles is propelling the advancements in high-energy–density lithium-ion *** manganese iron phosphate(LiMn_(x)Fe_(1-x)PO_(4))has garnered significant attention as a promising positive electrode material for lithium-ion batteries due to its advantages of low cost,high safety,long cycle life,high voltage,good high-temperature performance,and high energy *** LiMn_(x)Fe_(1-x)PO_(4)has made significant breakthroughs in the past few decades,there are still facing great challenges in poor electronic conductivity and Li-ion diffusion,manganese dissolution affecting battery cycling performance,as well as low tap *** review systematically summarizes the reaction mechanisms,various synthesis methods,and electrochemical properties of LiMn_(x)Fe_(1-x)PO_(4)to analyze reaction processes accurately and guide material ***,the main challenges currently faced are concluded,and the corresponding various modification strategies are discussed to enhance the reaction kinetics and electrochemical performance of LiMn_(x)Fe_(1-x)PO_(4),including multi-scale particle regulation,heteroatom doping,surface coating,as well as microscopic morphology ***,in view of the current research challenges faced by intrinsic reaction processes,kinetics,and energy storage applications,the promising research directions are *** importantly,it is expected to provide key insights into the development of high-performance and stable LiMn_(x)Fe_(1-x)PO_(4)materials,to achieve practical energy storage requirements.

关键词： Lithiummanganese iron phosphate High energydensity Lithium-ionbatteries Reactionmechanism Tap density

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A Survey on Graph Neural Network Acceleration: A Hardware Perspective

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Chinese Journal of Electronics 2024年第3期33卷 601-622页

作者： Shi CHEN Jingyu LIU Li SHEN School of Computer National University of Defense Technology Key Laboratory of Advanced Microprocessor Chips and Systems

Graph neural networks(GNNs) have emerged as powerful approaches to learn knowledge about graphs and *** rapid employment of GNNs poses requirements for processing *** to incompatibility of general platforms,dedicated hardware devices and platforms are developed to efficiently accelerate training and inference of *** conduct a survey on hardware acceleration for *** first include and introduce recent advances of the domain,and then provide a methodology of categorization to classify existing works into three ***,we discuss optimization techniques adopted at different *** finally we propose suggestions on future directions to facilitate further works.

关键词： Graph neural networks Deep learning acceleration Domain-specific architecture Hardware accelerator

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A comprehensive survey on graph neural network accelerators

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Frontiers of Computer Science 2025年第2期19卷 11-29页

作者： Jingyu LIU Shi CHEN Li SHEN School of Computer National University of Defense TechnologyChangsha 410073China Key Laboratory of Advanced Microprocessor Chips and Systems Changsha 410073China

Deep learning has gained superior accuracy on Euclidean structure data in neural *** a result,nonEuclidean structure data,such as graph data,has more sophisticated structural information,which can be applied in neural networks as well to address more complex and practical ***,actual graph data obeys a power-law distribution,so the adjacent matrix of a graph is random and *** processing accelerator(GPA)is designed to handle the problems ***,graph computing only processes 1-dimensional *** graph neural networks(GNNs),graph data is ***,GNNs include the execution processes of both traditional graph processing and neural network,which have irregular memory access and regular computation,*** obtain more information in graph data and require better model generalization ability,the layers of GNN are deeper,so the overhead of memory access and computation is *** present,GNN accelerators are designed to deal with this *** this paper,we conduct a systematic survey regarding the design and implementation of GNN ***,we review the challenges faced by GNN accelerators,and existing related works in detail to process ***,we evaluate previous works and propose future directions in this booming field.

关键词： graph neural network accelerators graph convolutional networks design space exploration deep learning domain-specific architecture

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Automatic Control of Magnetic Helical Microrobots Docking with Target Objects in Liquid Environments

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Journal of Bionic Engineering 2025年第2期22卷 574-584页

作者： Fu Zhao Haoran Rong Lefeng Wang State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin150080China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin150080China

Bio-inspired magnetic helical microrobots have great potential for biomedical and micromanipulation applications. Precise interaction with objects in liquid environments is an important prerequisite and challenge for helical microrobots to perform various tasks. In this study, an automatic control method is proposed to realize the axial docking of helical microrobots with arbitrarily placed cylindrical objects in liquid environments. The docking process is divided into ascent, approach, alignment, and insertion stages. First, a 3D docking path is planned according to the positions and orientations of the microrobot and the target object. Second, a steering-based 3D path-following controller guides the helical microrobot to rise away from the container bottom and approach the target along the path. Third, based on path design with gravity compensation and steering output limits, alignment of position and orientation can be accomplished simultaneously. Finally, the helical microrobot completes the docking under the rotating magnetic field along the target orientation. Experiments verified the automatic docking of the helical microrobot with static targets, including connecting with micro-shafts and inserting into micro-tubes. The object grasping of a reconfigurable helical microrobot aided by 3D automatic docking was also demonstrated. This method enables precise docking of helical microrobots with objects, which might be used for capture and sampling, in vivo navigation control, and functional assembly of microrobots.

关键词： Magnetic actuation Helical microrobots Visual servo Motion control Docking

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Towards secure and resilient unmanned aerial vehicles swarm network based on blockchain

IET Blockchain

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IET Blockchain 2024年第S1期4卷 483-493页

作者： Zhou, Xin Yang, Lin Linru, M.A. He, Huasen Academy of Military Science Institute of Systems Engineering Beijing Beijing China University of Science and Technology Institute of Advanced Technology Hefei Hefei China

Blockchain, as an emerging technology, has found diverse applications in solving trust issues in multi-party cooperation. The field of Unmanned Aerial Vehicle (UAV) swarm networks is no exception, as UAVs are susceptible to attacks from malicious counterparts due to limited computing and storage resources. In this paper, the authors present an adaptive networking mechanism for UAVs based on blockchain technology, which incorporates three key technologies. Utilizing Global Unique Identifier (GUID) and block storage, UAVs can achieve rapid identity authentication and efficient swarm switching. By evaluating node participation in block consensus, malicious nodes within the UAV swarm can be accurately identified. Additionally, routing learning based on node security attributes and link quality ensures secure and efficient data transmission. Simulation test results demonstrate the practicality and applicability of the proposed blockchain-based adaptive networking mechanism for UAV swarm networks. © 2023 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： Unmanned aerial vehicles (UAV)

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A novel triple periodic minimal surface-like plate lattice and its data-driven optimization method for superior mechanical properties

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Applied Mathematics and Mechanics(English Edition) 2024年第2期45卷 217-238页

作者： Yanda WANG Yanping LIAN Zhidong WANG Chunpeng WANG Daining FANG State Key Laboratory for Turbulence and Complex Systems&Center for Applied Physics and Technology College of EngineeringPeking UniversityBeijing 100871China Institute of Advanced Structure Technology Beijing Institute of TechnologyBeijing 100081China

Lattice structures can be designed to achieve unique mechanical properties and have attracted increasing attention for applications in high-end industrial equipment,along with the advances in additive manufacturing(AM)*** this work,a novel design of plate lattice structures described by a parametric model is proposed to enrich the design space of plate lattice structures with high connectivity suitable for AM *** parametric model takes the basic unit of the triple periodic minimal surface(TPMS)lattice as a skeleton and adopts a set of generation parameters to determine the plate lattice structure with different topologies,which takes the advantages of both plate lattices for superior specific mechanical properties and TPMS lattices for high connectivity,and therefore is referred to as a TPMS-like plate lattice(TLPL).Furthermore,a data-driven shape optimization method is proposed to optimize the TLPL structure for maximum mechanical properties with or without the isotropic *** this method,the genetic algorithm for the optimization is utilized for global search capability,and an artificial neural network(ANN)model for individual fitness estimation is integrated for high efficiency.A set of optimized TLPLs at different relative densities are experimentally validated by the selective laser melting(SLM)fabricated *** is confirmed that the optimized TLPLs could achieve elastic isotropy and have superior stiffness over other isotropic lattice structures.

关键词： lattice structure triple periodic minimal surface(TPMS) plate lattice structural optimization machine learning

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Thrust vector control of Hall thrusters using asymmetrical gas injection

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Journal of Electric Propulsion 2025年第1期4卷 1-26页

作者： Lev, Dan Katz Franco, Daniel Zimmerman, Raul Appel, Leonid Georgia Institute of Technology AtlantaGA30318 United States Rafael Advanced Defense Systems Ltd Haifa3102102 Israel

We present an experimental validation of thrust vector control in a Hall thruster by using asymmetrical gas injection into the thruster discharge channel. We detail the design and operation method of a gas distributor divided into three compartments, each fed by a dedicated mass flow controller. Low power Hall thruster (273 W) was operated, and its plume diverted using uneven gas injection via the mass flow controllers. Axial and lateral thrust were measured to assess thrust vector magnitude and direction. We show that the visual plume deflection is approximately in line with the thrust vector. We show that the direction of thrust could be manipulated in the range of -11° to + 7.3° in the cathode plane, when the positive angles are on the cathode side. Additionally, the thrust and specific impulse are reduced by up to 20% when total mass flow rate is preserved while the efficiency may drop to less than half of the nominal (straight) thrust direction. The thrust and specific impulse are reduced by up to 45% when maintaining a constant anode power while the efficiency may drop to less than half. We also showed that for a constant total mass flow rate, anode power may increase by as much as 80% due to an increase in discharge current at deflection angles greater than + 5°, corresponding to gas starvation in the discharge channel closest to the cathode. On the other hand, large discharge current oscillations, Id,rms/Id > 50%, were observed at deflection angles larger than -10°, when most gas is injected in the discharge channel closest to the cathode. This study validates the possibility to control the thrust vector, to a limited degree, using asymmetrical gas injection into the discharge channel. © The Author(s) 2025.

关键词： Hall thrusters

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On-orbit mission overview of the low power hall thruster propulsion system aboard venμs satellite

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Journal of Electric Propulsion 2024年第1期3卷 15页

作者： Katz Franco, Daniel Shoor, Boaz Davidson, Amoz Zimmerman, Raul Appel, Leonid Rinski, Vladislav Berkman, Sapir Baron, Dror Amit Shapira, Yonatan Malka, Michael Nezer, Tom Shmidt, Eran Herscovitz, Jacob Lev, Dan Advanced Defense Systems Ltd Haifa3102102 Israel Georgia Institute of Technology AtlantaGA30318 United States

Venμs is a satellite launched in 2017, for super-spectral Earth imaging and Electric Propulsion System (EPS) demonstration. In this paper we overview EPS design and operation throughout all five mission phases, from open/closed-loop orbit control, through orbit descent (720 → 410 km), orbit maintenance under high drag environment, to orbit raising (410 → 560 km). The EPS consisted of two throttleable Hall thrusters, PPU, Propellant Management Assembly (PMA), and a 9 L propellant tank carrying 16 kg of Xenon. Both thrusters operated in the 300–550 W power range, generated a combined total impulse of 158.1 kN-sec and consumed all propellant. Two methods are described to compute the remaining propellant mass – "Bookkeeping" and "PTV" methods. The advantages and disadvantages of each method are discussed in light of the Venµs mission. Thruster performance was measured on-orbit using the "Orbit Determination" method and compared to laboratory experiments conducted on the ground with identical thrusters. The measured performance on-orbit was found to agree within the error bars with the performance measured on the ground. Lastly, we present several repeating events in which the propulsion system suffered from sudden beam-outs or ignition difficulties. We present the methods used to construct a solution and implement it. © The Author(s) 2024.

关键词： Hall thrusters

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Decomposition-based learning in drone-assisted wireless-powered mobile edge computing networks

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Digital Communications and Networks 2024年第6期10卷 1769-1781页

作者： Xiaoyi Zhou Liang Huang Tong Ye Weiqiang Sun State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong UniversityShanghai 200240China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310058China

This paper investigates the multi-Unmanned Aerial Vehicle(UAV)-assisted wireless-powered Mobile Edge Computing(MEC)system,where UAVs provide computation and powering services to mobile *** aim to maximize the number of completed computation tasks by jointly optimizing the offloading decisions of all terminals and the trajectory planning of all *** action space of the system is extremely large and grows exponentially with the number of *** this case,single-agent learning will require an overlarge neural network,resulting in insufficient ***,the offloading decisions and trajectory planning are two subproblems performed by different executants,providing an opportunity for *** thus adopt the idea of decomposition and propose a 2-Tiered Multi-agent Soft Actor-Critic(2T-MSAC)algorithm,decomposing a single neural network into multiple small-scale *** the first tier,a single agent is used for offloading decisions,and an online pretrained model based on imitation learning is specially designed to accelerate the training process of this *** the second tier,UAVs utilize multiple agents to plan their *** agent exerts its influence on the parameter update of other agents through actions and rewards,thereby achieving joint *** results demonstrate that the proposed algorithm can be applied to scenarios with various location distributions of terminals,outperforming existing benchmarks that perform well only in specific *** particular,2T-MSAC increases the number of completed tasks by 45.5%in the scenario with uneven terminal ***,the pretrained model based on imitation learning reduces the convergence time of 2T-MSAC by 58.2%.

关键词： Mobile-edge computing Multi-agent reinforcement learning Offloading decision Trajectory planning Unmanned aerial vehicle Wireless power transfer

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