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, ...
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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.
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 a...
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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.
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 ...
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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.
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...
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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.
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 ...
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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.
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 suscepti...
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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...
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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.
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...
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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 ...
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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 o...
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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%.
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