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Promoting effect of d-band center and in-situ precipitation strategy on sulfur evolution reaction

Journal of Energy Chemistry 2025年第5期104卷 740-748页

作者： Jun Pu Yun Tan Jiasen Yin Zihan Shen Shoujie Liu Kai Zhang Lei Zhou Yagang Yao Key Laboratory of Functional Molecular Solids Ministry of Education Anhui Provincial Engineering Laboratory for New-Energy Vehicle Battery Energy-Storage Materials College of Chemistry and Materials Science Anhui Normal University National Laboratory of Solid State Microstructures College of Engineering and Applied Sciences Jiangsu Key Laboratory of Artificial Functional Materials Collaborative Innovation Center of Advanced Microstructures Nanjing University State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences School of Materials Science and Engineering Anhui University School of Energy and Power Engineering Jiangsu University Department of Electrical Engineering Eindhoven University of Technology

The slow conversion of polyphase in lithium-sulfur （Li-S） batteries not only intensifies the shuttle effect of lithium polysulfides （LiPSs）,but also causes the continuous accumulation of inactive sulfur species,resulting in rapid capacity attenuation and sluggish dynamic ***,the promoting effect of atomic interface stress on sulfur reaction was investigated via CoFe-CoFe2O4heterogeneous nanosheets with a cavity *** strain force induced by the in-situ precipitation of Co Fe bimetallic alloy in oxide matrix increased the d-band center,which was conducive to the interaction between catalyst and Li *** sulfur cathode based on this two-dimensional （2D） nanosheet design showed an extremely high capacity of 751 mAh g-1at 4 *** with a sulfur loading of 5.55 mg cm-2,its area capacity was still as high as 7.15 mAh ***,the enhanced stability greatly improved the practical potential of Li-S batteries.

关键词： 2D nanosheets D-band center In-situ precipitation Li-S batteries Strain effect

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Effect of Sn addition on electrochemical activity of ribbons of Mg-Al-Ca anode material for rechargeable magnesium batteries fabricated by rapid solidification

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Keikinzoku/Journal of Japan Institute of Light Metals 2025年第1期75卷 9-13页

作者： Fukuta, Yoshitaka Kikura, Kensei Tsukeda, Tadayoshi Aida, Tetsuo Tabata, Hironobu Kurihara, Hideki Suzuki, Mayumi Program of Materials Science and Engineering Studies in Science and Engineering Graduate School of Science and Engineering University of Toyama 3190 Gofuku Toyama-shi Toyama930-8555 Japan Graduate School of Science and Engineering University of Toyama Toyama-shi Toyama Japan Faculty of Sustainable Design Academic Assembly University of Toyama Toyama-shi Toyama Japan CHUETSU METAL WORKS CO. LTD. Nakaniikawagun Toyama Japan Saitama Industrial technology Center Kawaguchi-shi Saitama Japan Department of Mechanical Systems Engineering Faculty of Engineering Toyama Prefectural University Imizu-shi Toyama Japan

Magnesium-based batteries are potential candidates for next-generation rechargeable batteries due to the divalent nature of magnesium cations and the natural abundance of magnesium resources. In this study, the electrochemical activity of Mg-6mass%Al-3mass%Ca anodes combined with Sn was investigated. Thin ribbons of the anode specimens were prepared by a single-roll rapid solidification method. The results showed that the electrochemical activity increased upon adding 1 mass% Sn, while it decreased for 3 mass% Sn. Based on the results of an X-ray diffraction analysis, the addition of Sn led to the formation of Mg2Sn and Ca2Sn phases, which increased the lattice distortion. Scanning electron microscopy observations indicated that these secondary phases were homogeneously distributed for 1 mass% Sn addition, while they were coarsely distributed for 3 mass% Sn. © 2025 The Japan Institute of Light Metals.

关键词： X ray diffraction analysis

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A distributed algorithm with network-independent step-size and event-triggered mechanism for economic dispatch problem

A distributed algorithm with network-independent step-size a...

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作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey School of Electrical Engineering Dalian University of Technology Dalian China

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.

关键词： Cost functions

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Innovative dual-mode device integrating capacitive desalination and solar vapor generation for high-efficiency seawater desalination

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Journal of Energy Chemistry 2025年第1期100卷 171-179页

作者： Jiacheng Wang Zhaoyu Chen Ruduan Yuan Jiaxin Luo Ben Zhang Keju Ji Meng Li Juanxiu Xiao Kuan Sun National Innovation Center for Industry-Education Integration of Energy Storage Technology MOE Key Laboratory of Low-grade Energy Utilization Technologies and SystemsCollege of Energy&Power EngineeringChongqing UniversityChongqing 400044China Xuefeng Mountain Energy Equipment Safety National Observation and Research Station CQU-NUS Renewable Energy Materials&Devices Joint LaboratoryChongqing UniversityChongqing 400044China Southwest Technology and Engineering Research Institute Chongqing 400039China Jiangsu Provincial Key Laboratory of Bionic Functional Materials College of Mechanical and Electrical EngineeringNanjing University of Aeronautics and AstronauticsNanjing 210016JiangsuChina State Key Laboratory of Marine Resources Utilization in South China Sea Collaborative Innovation Center of Marine Science and TechnologySchool of Marine Science and EngineeringHainan UniversityHaikou 570228HainanChina

Solar-driven interface evaporation with high solar-to-steam conversion efficiency has shown great potential in seawater ***,due to the influence of latent heat and condensation efficiency,the water yield from solar-driven interface evaporation remains insufficient,posing a significant challenge that requires *** this work,we designed a dual-mode high-flux seawater desalination device that combines solar-driven interface evaporation and capacitive *** utilizing coupled desalination materials exhibiting both photothermal conversion and capacitance activity,the device demonstrated photothermal evaporation rates of 1.41 and 0.97 kg m^(-2)h^(-1)for condensate water yield under one-sun ***,the device exhibited a salt adsorption capacity of up to48 mg g^(-1)and a salt adsorption rate of 2.1 mg g^(-1)*** addition,the salt adsorption capacity increased by approximately 32%under one-sun ***,photo-enhanced capacitive desalination performance was explored through numerical simulations and theoretical *** calculations and characterizations confirmed that the defect energy levels formed by the introduction of sulfur vacancies can effectively widen the light absorption range,improve photothermal conversion performance,and stimulate more photoelectrons to participate in capacitive ***,the electron distribution state of molybdenum disulfide with sulfur vacancies and surface defect sites contributes to ion/electron transport at the solid-liquid *** work provides a novel pathway for integrating solar vapor generation with other low-energy desalination technologies.

关键词： Capacitive deionization Solar vapor generation Molybdenum disulfide Defect engineering

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Accelerating Deep Neural Networks with a Low-Cost Lossless Compression

Accelerating Deep Neural Networks with a Low-Cost Lossless C...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seongwook Byeon, Gwangeun Lee, Seungeui Bae, Youngseok Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Advanced R&D Center Hanwha Systems Co. Ltd. Korea Republic of Agency for Defense Development Emerging Science and Technology Directorate Korea Republic of

ISBN: (纸本)9798331510756

Since deep learning inference involves a significant amount of computations, there have been a lot of efforts to accelerate the inference process by eliminating ineffectual compu-tations. As a solution to this problem, several sparsity-aware deep learning accelerators have been proposed. These accelerators perform the inference on compressed data (filters and feature maps) and dynamically remove the ineffectual multiplications with zero operands. For efficiently handling the compressed data on the accelerators, a simple and effective compression scheme, which encodes the whole data with its nonzero values and their positions, is necessary. However, prior approaches have several shortcomings, such as low compression efficiency and high complexity in compression and decompression hardware. This paper proposes a low-cost compression scheme that can be applied to sparsity-aware deep-learning accelerators. The compression scheme synergistically combines two low-cost compression schemes: Zero-Value Compression (ZVC) and Base-Delta-Immediate Compression (BDI). Our experimental evaluation shows that the proposed compression scheme achieves an average compression ratio of 2.26, resulting in 2.33x performance improvement, © 2025 IEEE.

关键词： Data handling

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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Multi-modal Multitask Learning Model for Simultaneous Classification of Two Epilepsy Biomarkers 27th

Multi-modal Multitask Learning Model for Simultaneous Classi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Broti, Nawara Mahmood Sawada, Masaki Takayama, Yutaro Iijima, Keiya Iwasaki, Masaki Ono, Yumie Electrical Engineering Program Graduate School of Science and Technology Meiji University Kanagawa Kawasaki Japan Department of Neurosurgery Yokohama City University Hospital Kanagawa Yokohama Japan Department of Neurosurgery National Center Hospital National Center of Neurology and Psychiatry Tokyo Kodaira Japan Department of Electronics and Bioinformatics School of Science and Technology Meiji University Kanagawa Kawasaki Japan

ISBN: (纸本)9783031781971

Accurate estimation of the epileptogenic zone is critical for surgical treatment of drug-resistant epilepsy. While epilepsy biomarkers detection for epileptogenic zone localization has traditionally relied on expert clinician knowledge, the use of deep learning in this context has recently gained appeal for objective diagnosis and reducing clinician burden. However, previously proposed classifiers for electrocorticogram data focused on only single biomarkers among many types, requiring separate models and large datasets for each. To minimize clinicians’ workload and patients’ sufferings, innovative methods to classify multiple biomarkers using small training datasets and minimal computation time is required. This research marks the first implementation of multi-modal multitask learning in epilepsy biomarker classification, proposing a model to automatically classify two biomarkers—high-frequency oscillations (HFO) and interictal epileptiform discharges (IED)—from separate datasets simultaneously. We validated the proposed model on 1500 annotated HFO and IED candidate signals each, obtained from 5 patients. Our proposed model can perform both HFO and IED classification simultaneously, with cross-validation accuracies of 90.99% and 93.99%;F1 scores were reported as 0.80 and 0.82, respectively. Our leave-one-patient-out experiments achieved 78.03% and 81.19% accuracy for HFO and IED classification, respectively. Compared to existing state-of-the-art classification architectures, our model is shown to be efficient and robust, yet simple and lightweight. The usefulness of sharing parameters between the two classifiers was confirmed in this practical clinical setting of epileptic biomarker detection, potentially contributing to minimizing the number of annotated datasets and realizing robust, accurate, and simultaneous identification of two biomarkers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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High-Lift Prediction Workshop 5: High-Order Discretization Technology Focus Group Summary

High-Lift Prediction Workshop 5: High-Order Discretization T...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Galbraith, Marshall C. Ruiz-Gironés, Eloi Roca, Xevi Wang, Z.J. Holst, Kevin R. Skaperdas, Vangelis Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States Applications in Science and Engineering Barcelona Supercomputing Center - BSC Barcelona Spain Department of Aerospace Engineering University of Kansas LawrenceKS66045 United States Electrical Engineering and Computer Science University of Tennessee KnoxvilleTN37996 United States Customer Service BETA CAE Systems Thessaloniki Greece

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper summarizes the High-Order Technical Focus Group (HO-TFG) submissions forthe 5th AIAA High-Lift Prediction Workshop (HLPW-5). The goal of the workshop was to assess the state-of-the-art in mesh generation and computational fluid dynamics software for predicting peak lift of transport aircraft with flaps and slats deployed. Numerous geometric build ups of the Common Research Model High-Lift (CRM-HL) variant and Reynolds number considerations served as the primary focus of the workshop, and a simple wing-body variant of the CRM-HL was used as a verification test case. The HO-TFG was tasked with generating high-order curved meshes for these complex geometries, and computing high-order solutions using both Reynolds Averaged Navier-Stokes (RANS) and scale resolving Large Eddy Simulation(LES) formulations. While participation in the HO-TFG was limited, the effort produced curved meshes on highly-complex geometric configurations and computational results from two participants. The LES results demonstrate the potential for producing results comparable to 2nd-order accurate methods, but with fewer degrees of freedom. The RANS submission for this workshop does not demonstrate a benefit over 2nd-order schemes. However, the authors will argue a lack of understanding for optimal mesh distributions for high-order methods is the cause for the limited benefits observed for the RANS results. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Reynolds number

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Deep learning framework for wind speed prediction in Saudi Arabia

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Neural Computing and Applications 2025年第5期37卷 3685-3701页

作者： Alabdulhadi, Arwa Ahmed Rehman, Shafiqur Ali, Amjad Shafiullah, Md Physics Department College of Science and Humanities Imam Abdulrahman Bin Faisal University Jubail3065 Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Control & Instrumentation Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Increased utilization of conventional energy sources severely impacts the environment by increasing the global temperature and contributing to global warming. Sustainable energy sources could contribute to handling the increment while also providing cheap, clean, and abundant energy. Wind energy is considered one of the most reliable sources of sustainable energy due to its abundance and availability during the day and night compared to other renewable resources. However, effective forecasting of such intermittent resources is considered a key challenge for power system operators. This paper develops a novel deep learning framework for forecasting the wind speed in Dhahran City, Saudi Arabia, using two years of data acquired at different heights from the light detection and ranging device, an active remote sensing wind monitoring system. It also segments the data and removes wrong measurements through data pre-processing. The article identifies the best configuration for deep learning models, such as long short-term memory, through a systematic approach. The presented results confirm the efficacy of the developed models against the selected statistical performance measures. The developed model performs better with large data volumes than with lower volumes. Finally, the comparative analysis with the literature-reported results provides confidence in the competency of the proposed model in predicting wind speed for various periods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Clean energy

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Molecular architecture of mammalian pyruvate dehydrogenase complex

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Protein & Cell 2025年第1期16卷 72-78页

作者： Maofei Chen Yutong Song Sensen Zhang Yitang Zhang Xudong Chen Minghui Zhang Meng Han Xin Gao Sai Li Maojun Yang Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological StructureSchool of Life SciencesTsinghua UniversityBeijing 100084China Ministry of Education Key Laboratory of Protein Science Tsinghua UniversityBeijing 100084China State Key Laboratory of Membrane Biology Tsinghua UniversityBeijing 100084China Department of Biochemistry and Molecular Biology School of Basic Medical SciencesShenzhen University Health Science CenterShenzhen 518060China Protein Research Technology Center Protein Chemistry and Omics PlatformSchool of Life SciencesTsinghua UniversityBeijing 100084China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Cryo-EM Facility Center Southern University of Science and TechnologyShenzhen 518055China Beijing Life Science Academy Beijing 102209China

Dear Editor,Pyruvate dehydrogenase complex(PDHc) is a large multienzyme assembly(Mr = 4–10 million Daltons) consisting of three essential components: pyruvate dehydrogenase(E1p), dihydrolipoyl transacetylase(E2p), and dihydrolipoyl dehydrogenase(E3). These three enzymes perform distinct functions sequentially to catalyze the oxidative decarboxylation of pyruvate with formation of nicotinamide adenine dinucleotide(NADH) and acetyl-coenzyme A(Patel and Roche, 1990).

关键词： sequentially oxidative Dalton

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