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Hierarchically Porous FeCoNiCrAl/CNF Nanocomposites for Enhanced Electromagnetic Wave Absorption

ACS Applied Nano Materials 2025年第20期8卷 10242-10247页

作者： Ma, Yuanyuan Chen, Guoqin Wei, Zengyan Feng, Yuming Wang, Chunyu Wang, Pingping Zhong, Bo Wu, Gaohui School of Materials Science and Engineering Harbin Institute of Technology Harbin150080 China Weihai264209 China Weihai264209 China

Lightweight, versatile, and efficient electromagnetic wave absorption materials (EWAMs) are needed to counteract the adverse electromagnetic pollution caused by electromagnetic radiation leakage from electronic devices and to eliminate electromagnetic interference. The hybrids of high entropy systems/carbon materials with multifarious loss mechanisms exhibit a tendency to become the focus of the next generation of EWAMs. Here, we fabricate hierarchical porous FeCoNiCrAl/CNF nanocomposites by a one-step microwave instantaneous heating method. Its remarkable microwave absorption performance benefits from the unique nanosize effect of FeCoNiCrAl high-entropy alloy (HEA) nanoparticles in the composition and the multicomponent synergy and hierarchical pore structure. These results provide guidance for the design of advanced HEA-based EWAMs. © 2025 American Chemical Society.

关键词： Electromagnetic pulse

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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

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Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi directional optimization iot devices swarm deep learning

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Progress of crystalline carbon nitride in synthesis,atomic structure characterization and photocatalysis

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Nano Research 2025年第1期18卷 455-476页

作者： Yueyang Tan Zhongyan Zhang Yayun Pu Zongzhao Sun Limin Huang School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemistry Southern University of Science and TechnologyShenzhen 518055China School of Traffic and Environment Shenzhen Institute of Information TechnologyShenzhen 518172China College of Chemistry and Chemical Engineering Linyi UniversityLinyi 276000China School of Optoelectronic Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Crystalline carbon nitride(CCN)has emerged as a highly promising semiconductor photocatalyst with unique properties,such as enhanced charge migration rate,reduced carrier recombination probability,narrow band gap and improved light-harvesting efficiency,which are suitable for a wide range of applications in solar-to-chemical conversion,energy storage,therapeutic and environmental pollution *** the past few years,there has been an increasing number of reviews on CCN ***,most of these reviews mainly focus on synthesis methods,modification and applications,with less emphasis on the relationship between structures and properties,as well as on indepth exploration of the crystalline *** electronic instability of CCN presents challenges for conventional characterization techniques to directly and thoroughly investigate the relationship between its intrinsic atom structure and photocatalytic *** mini-review not only highlights the progress in CCN-based photocatalysts,with a focus on molten-salt synthesis(including solid-salt-induced crystallization),but also emphasizes the atomic structure characterization by specifically introducing the differential phase contrast(DPC)scanning transmission electron microscopy(STEM)technique,which is essential for enhancing our understanding of the crystal structure and photocatalytic mechanisms of ***,the review outlines the photocatalytic performance and puts forward potential challenges on CCN *** review will provide a clearer understanding of the relationship in developing precise customization strategies for CCN materials and ultimately explain the regularity and specificity of the enhanced performance in targeted photocatalytic systems.

关键词： crystalline carbon nitride synthesis characterization photocatalysis

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Entity and Relationship Extraction with High-quality Spans and Enhanced Marker Strategies

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Machine Intelligence Research 2025年第2期22卷 289-303页

作者： Qibin Li Nianmin Yao Nai Zhou School of Computer Science Dalian University of TechnologyDalian 116024China Ningbo Institute of Dalian University of Technology Ningbo 116026China

Entity and relation extraction is a critical task in information *** approaches have emphasized obtaining improved span ***,existing work suffers from two major ***,there is an overabundance of low-quality candidate spans,which hinders the effective extraction of information from high-quality candidate ***,the information encoded by existing marker strategies is often too simple to fully capture the nuances of the span,resulting in the loss of potentially valuable *** address these issues,we propose an enhancing entity and relation extraction with high-quality spans and enhanced marker(HSEM)strategies,it assigns adaptive weights to different spans in order to make the model more focused on high quality ***,the HSEM model enriches marker representation to incorporate more span information and enhance entity ***,we design a span scoring framework that assesses span quality based on the fusion of internal information and focuses the model on training high-quality samples to improve *** results on six benchmark datasets demonstrate that our model achieves state-of-the-art results after discriminating span quality.

关键词： High-quality spans enhanced marker span scoring framework span representations entity and relation extraction

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Mode transitions in Hall effect thrusters by Varying duty ratio under pulsating operation

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Advances in Space Research 2025年第10期75卷 7466-7472页

作者： Ma, Haochen Zheng, He Liang, Tiance Li, Jiapeng Wei, Liqiu Han, Liang Li, Hong Ding, Yongjie School of Energy Science and Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China Key Laboratory of Aerospace Plasma Propulsion Ministry of Industry and Information Technology Harbin150001 China Beijing Institute of Control Engineering China Aerospace Science and Technology Corporation Beijing100089 China

During the operation of a Hall effect thruster, small changes in discharge voltage, mass flow, or magnetic field cause significant changes in the discharge mode of the thruster. Keeping other working conditions unchanged, the discharge mode of the Hall effect thruster can be triggered by changing the duty ratio. The experimental results show that at different frequencies, a small change in the duty ratio caused a significant increase in the discharge current and discharge oscillation amplitude. When the duty ratio changed from 70 % to 68 % at 90 kHz, the discharge mode changed from high- to low-oscillation mode. The discharge current oscillation decreased from 1.03 to 0.66, and the anode efficiency increased from 24.5 % to 25.8 %. When the duty ratio was changed from 64 % to 58 % at a pulse frequency of 70 kHz, the discharge current oscillation decreased from 1.26 to 0.85, and the anode efficiency increased from 23.5 % to 24.8 %. In the low-oscillation mode, the discharge current and anode voltage of the thruster were at the same frequency, and the discharge oscillation of the thruster was significantly reduced. Furthermore, the Faraday probe measurement results showed that the ion current density distribution of the thruster was more focused. The match between the supply of the working medium and the ionization rate improved the degree of ionization, leading to an improvement in thrust performance. Maintaining the Hall effect thruster in low-oscillation mode is of great significance for improving the reliability of the thruster. © 2025 COSPAR

关键词： Hall thrusters

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HDD-Net: Haar Dual Domain Network for Ring Artifacts Correction

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 399-409页

作者： Wu, Xuelong Wang, Junsheng Zhao, Qingjie Beijing Institute of Technology School of Mechanical Engineering Beijing100081 China Beijing Institute of Technology Advanced Research Institute for Multidisciplinary Science School of Materials Science and Engineering Beijing100081 China Beijing Institute of Technology School of Computer Science Beijing100081 China

Ring artifacts are common artifacts in X-ray Computed Tomography (XCT) scans and have a significant impact on subsequent feature/phase extractions due to the small grayscale gradients in XCT volume data of bulk materials. This paper proposes the Haar Dual Domain Network for correcting ring artifacts. By utilizing the Haar wavelet decomposition on images containing ring artifacts in both the image and projection domains, the ring artifacts are preliminarily separated, facilitating their removal by neural networks while preserving microstructure features such as low-contrast phase boundaries. By constructing a feature fusion network, the information from both 2D slices and 3D projection volume data has been fully integrated to eliminate ring artifacts while preserving the edges of every feature. The effectiveness of the Haar wavelet transform and fusion network has been validated by ablation experiments, proving the application of HDD-Net to large volume of XCT data. © 2015 IEEE.

关键词： Wavelet decomposition

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Developing a novel Mg2Zn3Li1Gd alloy sheet with high room-temperature formability by introducing an elliptical texture distribution

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Journal of Magnesium and Alloys 2025年第2期13卷 877-893页

作者： Guangjie Xue Junjie He Yang Chen Zijian Zhang Kun Yang Yilun Zhou Huijun Hao Weiqi Wang Yong Mao Qinghang Wang Materials Genome Institute School of Materials and EnergyYunnan UniversityKunming 650504China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001HeilongjiangChina School of Mechanical Engineering Yangzhou UniversityYangzhou 225127China

In recent years,modification of texture distribution has been considered a valid approach to improve the room-temperature(RT)formability of magnesium(Mg)*** this study,a novel Mgsingle bond2Znsingle bond3Lisingle bond1Gd alloy sheet with weak elliptical-texture was fabricated by cold rolling and subsequent annealing,and it showed an excellent Erichsen(IE)value near 7.1 *** quasi-in-situ electron backscatter diffraction(EBSD)and transmission electron microscopy(TEM)analysis indicate that considerable basal and pyramidal dislocations can be activated in the cold rolling *** annealing,these dislocations can induce nucleation and then cause preferential misorientation relationships around〈uvt0〉concerning the nuclei and parent grains,which can facilitate the formation of elliptical ***,the particle-stimulated nucleation(PSN)mechanism and the co-segregation of Zn and Gd at grain boundaries(GB)further weak texture ***,the mechanical properties of the Mgsingle bond2Znsingle bond3Lisingle bond1Gd alloy sheet are significantly improved.

关键词： Mg-2Zn-3Li-1Gd Alloying Formability Plastic deformation

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1.4-kV Irradiation-Hardened -Ga2O3 Heterojunction Barrier Schottky Diode Under 107 ions/cm2 Fluence and 82.1 MeV cm2/mg LET Environments

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 813-816页

作者： Sun, Na Zhang, Zhengliang Zhou, Feng Wang, Tianqi Ren, Fang-Fang Gu, Shulin Lu, Hai Zhang, Rong Ye, Jiandong Nanjing University School of Electronic Science and Engineering Nanjing210093 China Harbin Institute of Technology Harbin150001 China

Single event burnout (SEB) caused by heavy ion irradiation in space environments poses a significant threat to aerospace power electronic devices. This work demonstrates irradiation-hardened -Ga2O3 heterojunction barrier Schottky (HJBS) diodes with exceptional SEB capability. The device design incorporates micron-scale deep trenches filled by p-type nickel oxide (NiO) and high-k BaTiO3 field-plate (FP) edge termination. This architecture efficiently extracts radiation-induced positive charges (holes) during single-event irradiation through the trenched-embedded Ni/p-NiO with low Ohmic contact resistance, significantly alleviating charge aggregation while minimizing non-uniform field distributions through strategically engineered charge drainage pathways. As a result, the HJBS device achieves a SEB voltage exceeding 1.4 kV and a SEB degradation rate of only 9.6%. This is the first demonstration of kilovolt-class radiation-hardened diodes, and its performance metrics are the best reported among SiC, GaN, Ga2O3 and Si power diodes to date. This work underscores the great potential of Ga2O3 power diodes for irradiation power applications. © 1980-2012 IEEE.

关键词： Nickel oxide

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A facile data-driven battery capacity estimation framework for on-road plug-in hybrid electric vehicle

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Progress in Natural science:Materials International 2025年第1期35卷 166-176页

作者： Jianlu Li Yanming Chen Tongxing Lei Jianguo Liu Guizheng Liu Zhaoyang Deng Xuebiao Wu Zhiyu Ding Yinghe Zhang Junwei Wu Yanan Chen Guangzhou Automobile Group Co. Ltd. School of Materials Science and Engineering Harbin Institute of Technology School of Materials and Environmental Engineering Shenzhen Polytechnic University School of Science Shenzhen Key Laboratory of Flexible Printed Electronics Technology Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application Harbin Institute of Technology School of Materials Science and Engineering Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education Tianjin Key Laboratory of Composite and Functional Materials Tianjin University

A data-driven framework with strong generalization capabilities is proposed to effectively extract features and easily access battery capacity. This framework can make highly accurate predictions for the battery capacities of plug-in electric vehicles. The feature extraction process is entirely based on statistics, which are always available and can be generalized to various types of battery data. An improved ampere-hour integral method can easily access battery capacity with just short-charging segments lasting 500 s. Several machine-learning models are trained to verify the framework's effectiveness, with the best model achieving a test error of 0.84 % based on leave-one-out validation. SHAP values are used to provide a reasonable interpretation of the relationships between the constructed features and model outputs. The proposed framework offers advantages such as reduced computational resources, wide generalization, and high prediction accuracy, showing great potential for battery management.

关键词： Battery capacity estimation Data-driven framework Lithium-ion battery Machine learning Plug-in hybrid electric vehicle

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Realizing the potential of Internet of Things (IoT) in Industrial applications

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Discover Internet of Things 2025年第1期5卷 1-16页

作者： Rathi, Bharat Thapaswi, S. Kambhampati, Meghana Jain, Vineet Akshay, P. Pandey, Trilok Nath Pradhan, Sunil Kumar School of Electronics Engineering Vellore Institute of Technology Chennai600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India

The Internet of Things (IoT), which enables seamless connectivity and effective data exchange between physical items and digital systems, has completely changed the way we interact with our surroundings. This study evaluates IoT applications in healthcare, transportation, agriculture, and smart cities, highlighting advancements, opportunities, and challenges. Using a systematic literature review and case study analysis, it identifies essential IoT components, including sensors, communication protocols, data analytics, and security methods. Key findings demonstrate IoT's potential to enhance healthcare through real-time monitoring and cost reduction, optimize transportation systems for resource efficiency, improve agricultural productivity via precision farming, and enable sustainable urban development in smart cities. However, challenges like data privacy, interoperability, and scalability remain critical barriers to widespread adoption. This study underscores the need for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to address these challenges and unlock IoT's full potential. It provides actionable insights and future directions for realizing a connected, efficient, and sustainable IoT-driven future. © The Author(s) 2025.

关键词： Precision agriculture

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