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Two-dimensional ultrathin nanosheets over mackinawite FeS for efficient electrochemical N_(2) reduction

International Journal of Minerals,Metallurgy and Materials 2025年第4期32卷 936-943页

作者： Jing Zhang Yingying Guo Haiyang Li Jing Guo Rui Zheng Shuai Niu Fang Wang School of Chemistry and Materials Science Key Laboratory of Magnetic Molecules and Magnetic Information Materials(Ministry of Education)Shanxi Normal UniversityTaiyuan 031000China College of Chemistry and Chemical Engineering Taiyuan University of TechnologyTaiyuan 030024China School of Physics and Information Engineering Shanxi Normal UniversityTaiyuan 031000China Chemistry and Environmental Science College Key Laboratory of Advanced Materials Chemistry and Devices of Higher Education Institutions in Inner Mongolia Autonomous RegionInner Mongolia Normal UniversityHohhot 010022China

Electrocatalytic N_(2)reduction reaction (NRR) has been considered as a promising and alternative strategy for the synthesis of NH_(3),which will contribute to the goal of carbon neutrality and ***,this process often suffers from the barrier for N_(2)activation and competitive reactions,resulting in poor NH_(3)yield and low Faraday efficiency (FE).Here,we report a two-dimensiona(2D) ultrathin FeS nanosheets with high conductivity through a facile and scalable method under mild *** synthesized FeS catalysts can be used as the work electrode in the electrochemical NRR cell with N_(2)-saturated Na_(2)SO_(4)*** a catalyst shows a NH_(3)yield of 9.0μg·h^(-1)·mg^(-1)(corresponding to 1.47×10^(-4)μmol·s^(-1)·cm^(-2)) and a high FE of 12.4%,which significantly outperformed the other most NRR *** high catalytic performance of FeS can be attributed to the 2D mackinawite structure,which provides a new insight to explore low-cost and high-performance Fe-based electrocatalysts,as well as accelerates the practical application of the NRR.

关键词： electrocatalysis electrocatalytic nitrogen reduction reaction FeS nanosheets faraday efficiency

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Keio University Department of Information and Computer Science Kanagawa223852 Japan Tohoku University International Research Institute of Disaster Science Sendai Japan

As Synthetic Aperture Radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this paper proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network (AMCW-NN). Firstly, employing Multi-Scale Superpixel Reconstructed Difference Image (MSRDI) effectively enhances the edge information of the images. Secondly, a twostage Weighted Center Constrained Fuzzy C-Means (WCCFCM) clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Thirdly, a Convolutional Squeeze-and-Excitation (SE) Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model (DDPM) is introduced into the CSWNN to expand pseudo-labeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2%-9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Synthesis of interfacial electric field-enhanced CdS/CdxZn1-xS/ZnO ternary heterojunction by lye dissolution etching mechanism for photocatalytic H2 production and CO2 reduction

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材料科学技术（英文版） 2025年第1期204卷 152-165页

作者： Qi Li Shengchao Yang Yufan Huang Yuwei Liang Chunling Hu Min Wang Zhiyong Liu Yanlong Tai Jichang Liu Yongsheng Li School of Chemistry and Chemical Engineering Shihezi University/Key Laboratory of Green Process for Chemical Engineering/Key Laboratory for Chemical Materials of Xinjiang Uygur Autonomous Region/Engineering Center for Chemical Materials of Xinjiang BingtuanShihezi UniversityShihezi 832003China Shanghai Institute of Ceramics Chinese Academy of SciencesShanghai 200050China School of Chemistry and Chemical Engineering Shihezi University/Key Laboratory of Green Process for Chemical Engineering/Key Laboratory for Chemical Materials of Xinjiang Uygur Autonomous Region/Engineering Center for Chemical Materials of Xinjiang BingtuanShihezi UniversityShihezi 832003China Key Laboratory of Human-Machine Intelligence-Synergy Systems of Chinese Academy of Sciences(CAS) Shenzhen Institutes of Advanced TechnologyShenzhen 518055China School of Chemistry and Chemical Engineering Shihezi University/Key Laboratory of Green Process for Chemical Engineering/Key Laboratory for Chemical Materials of Xinjiang Uygur Autonomous Region/Engineering Center for Chemical Materials of Xinjiang BingtuanShihezi UniversityShihezi 832003China Lab of Low-Dimensional Materials Chemistry Key Laboratory for Ultrafine Materials of Ministry of Education School of Materials Science and Engineering East China University of Science and Technology ShanghaiShanghai 200237China

The difficulty in fabricating a multifaceted composite heterojunction system based on CdxZn1-xS limits the enhancement of photocatalytic *** the present scrutiny,novel ZnO/CdxZn1-xS/CdS com-posite heterojunctions are successfully prepared by the alkaline dissolution etching *** internal electric field at the interface of Ⅰ-type and Z-scheme heterojunction improved the effective charge *** ZC 8 sample exhibits excellent photocatalytic performance and the H2 production efficiency is 15.67 mmol g-1 h-1 with good stability up to 82.9％in 24-hour *** performance of CH4 and CO capacity in the CO2RR process is 3.47 μmol g-1 h-1 and 23.5 μmol g-1 h-1,*** photogener-ated accelerated charge transport is then examined in detail by in situ X-ray photoelectron spectroscopy(ISXPS)and density functional theory(DFT)*** work presents a new idea for the synthe-sis of CdxZni-xS solid-solution-based materials and provides a solid reference for the detailed mechanism regarding the electric field at the heterojunction interface.

关键词： Photocatalysis Interface electric field Composite heterostructure Photocatalytic mechanism CdxZn1-xS solid-solution

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Artificial sensory neurons and their applications

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Journal of Semiconductors 2025年第1期46卷 108-128页

作者： Jiale Shao Hongwei Ying Peihong Cheng Lingxiang Hu Xianhua Wei Zongxiao Li Huanming Lu Zhizhen Ye Fei Zhuge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315211China State Key Laboratory of Environment-friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly *** limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a *** computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI *** neurons and synapses are the two core components of neuromorphic computing *** perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of *** work reviews recent advances in artificial sensory neurons and their ***,biological sensory neurons are briefly ***,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working ***,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and ***,challenges faced by artificial sensory neurons at both device and system levels are summarized.

关键词： artificial sensory neurons artificial perception systems neuromorphic computing artificial intelligence

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Port-controllable routing of orbital angular momentum modes using a rotatable diffractive neural network

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science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 158-167页

作者： Junmin Liu Jiafu Chen Qingji Zeng Zemin Liang Xinping Wu Xin Zhao Jiangnan Xiao Huapeng Ye Ze Dong Dianyuan Fan Shuqing Chen Institute of Microscale Optoelectronics Shenzhen University College of New Materials and New Energies Shenzhen Technology University Terahertz Technology Innovation Research Institute Shanghai Key Laboratory of Modern Optical System Terahertz Science Cooperative Innovation Center School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays South China Academy of Advanced Optoelectronics South China Normal University School of Information and Electronics Beijing Institute of Technology

Orbital angular momentum(OAM) modes provide an additional orthogonal physical dimension, offering transformative potential for enhancing optical communication capacity. Despite significant progress in mode multiplexing, the development of robust communication networks faces persistent challenges, particularly in effectively routing and controlling these multiplexed channels among network nodes. To tackle these dilemmas, we propose a rotatable diffractive neural network(R-DNN) strategy and demonstrate its capability for port-controllable OAM mode routing. By leveraging the correlation between the orthogonal evolution of OAM modes in free space and phase modulations during propagation, the R-DNN precisely shapes the spatial evolution of mode fields through multiple rotatable phase layers, enabling efficient routing to specific output ports. This approach exploits the interaction of secondary wavelets with the relative states of the rotatable layers, allowing on-demand control of mode evolution paths and enhancing routing flexibility. As a proof of concept, we developed a tri-functional router that successfully directs three OAM modes to individually controllable output ports. This router achieves an average intermode crosstalk of less than-16.4 dB across three functional states, one-dimensional, two-dimensional, and cross-connected switching, while supporting the routing of 5.85 Tbit/s quadrature phase-shift keying signals. These results highlight the R-DNN's effectiveness in achieving precise and controllable OAM mode manipulation, paving the way for advanced applications in mode-multiplexed communication networks and beyond.

关键词： orbital angular momentum mode mode routing diffractive neural network

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High-beam-quality single-frequency 10-mJ nanosecond fiber and a solid-state hybrid laser front-end system at a 1-kHz repetition rate

Optics Continuum

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Optics Continuum 2025年第4期4卷 915-923页

作者： Kong, Zhiwei Cheng, Tianxiang Zhao, Zhigang Liu, Zhaojun School of Information Science and Engineering Shandong University Qingdao266237 China Key Laboratory of Laser and Infrared System Ministry of Education Shandong University Qingdao266237 China Shandong Provincial Key Laboratory of Laser Technology and Application Shandong University Qingdao266237 China

A compact single-frequency nanosecond fiber & solid-state hybrid laser system at 1030 nm is presented. The seed source, a continuous wave distributed feedback (DFB) laser diode, was first externally modulated with a repetition rate of 1.5 MHz and a pulse duration of 10 ns, whose repetition rate was further lowered to 1 kHz by a pulse picker. Afterwards, three-stage end-pumped Yb:YAG crystal amplifiers were employed for energy scaling. Finally, an average power of 12 W was obtained with a repetition rate of 1 kHz and a linewidth of ∼180 MHz, corresponding to approximately four times the Fourier transform limit (FTL). The beam quality factor (M2) was measured to be 1.18 and 1.20 for the horizontal and vertical directions, respectively. In the future, this high-beam quality front-end laser will be used as the seed source for nanosecond disk laser amplifiers. © 2025 Optica Publishing Group.

关键词： Pulse repetition rate

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Enhanced piezoelectric properties and thermal stability of BiFeO3-BaTiO3-based ceramics with local structural heterogeneity

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Ceramics International 2025年第14期51卷 18587-18596页

作者： Fang, Juan Mao, Suwen Jiang, Minhong Zhou, Changrong Yu, Hongbao Rao, Guanghui Zhao, Jingtai Han, Jin Hou, Long Sun, Kang Cheng, Shuai Guangxi Key Laboratory of Information Materials and Property for New Energy Materials Guilin University of Electronic Technology Guangxi Guilin541004 China School of Material Science and Engineering Guilin University of Electronic Technology Guangxi Guilin541004 China College of Material Science and Engineering Kunming University of Science and Technology Kunming650093 China School of Materials Science and Engineering Shanghai University Shanghai200444 China

Pb-free piezoceramics have been extensively studied as alternatives to commercial Pb-based ceramics because of environmental concerns. However, their intrinsic thermal degradation of piezoelectric properties limits their industrial applications. Herein, by designing a sandwich-structured BaTi0.93Sn0.07O3/0.75BiFeO3-0.25BaTiO3-0.1 wt%MnO2/BaTi0.93Sn0.07O3 (SWS-BFBT), we achieve significant improvements in piezoelectric properties and depolarization temperature (Td) by forming a heterogeneity on the surface of BFBT ceramics. The wide temperature range of phase boundaries and enhanced relaxor behavior resulting from the reduced domain size and the increased number of polar nanoregions (PNRs) are attained in the diffusion layer of the SWS-BFBT ceramic, thereby enhancing piezoelectricity and thermal stability with the synergistic effect of polar pinning by more defects collectively. Compared to the Td of BFBT ceramic below 300 °C, we have designed SWS-BFBT ceramics with a heterogeneity, which improves Td to 423 °C and piezoelectric constant (d33) to 137 pC/N. Furthermore, their d33 can still maintain above a value of 111 pC/N at temperatures below 423 °C. We believe our finding will contribute significantly to the design of high-temperature lead-free piezoelectric materials. © 2025

关键词： Piezoelectricity

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Differentially Private Federated Stochastic Primal-Dual Learning for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17034-17050页

作者： Li, Yiwei Wang, Shuai Chang, Tsung-Hui Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

Federated learning (FL) has the potential to empower Internet-of-Vehicles (IoV) networks by enabling smart vehicles (SVs) to participate in the learning process under the orchestration of a vehicular service provider while keeping data locally. In this paper, we propose a novel federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP) to ensure robust privacy protection for FL based IoV (FL-IoV) systems. The FedSPD-DP algorithm leverages multiple steps of local stochastic gradient descent (SGD) and partial client participation (PCP) to improve communication efficiency while incorporating differential privacy (DP) to ensure privacy protection. Our theoretical analysis explores the impact of these strategies on learning performance. Specifically, we demonstrate that the data sampling strategy and PCP enhance data privacy, while a larger number of local SGD steps may increase privacy leakage, revealing a non-trivial tradeoff between communication efficiency and privacy protection. Extensive experiments on real-world data validate the effectiveness of the proposed algorithm, showing superior performance compared to state-of-the-art FL algorithms, and confirming the analytical results and properties. © 2014 IEEE.

关键词： Federated learning

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Flexible, Super-stable, and Multifunctional Capacitive Pressure Sensors with Tilted Micropillar Array by Inclined Laser Ablation and Demolding

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IEEE Sensors Journal 2025年第6期25卷 9367-9374页

作者： Xu, Tianlong Li, Kaixin Zhou, Yihao Zhang, Dawei Sheng, Bin College of Optical Electrical and Computer Engineering Unversity of Shanghai for Science and Technology Shanghai 200093 China. Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instruments and System Shanghai 200093 China

Flexible capacitive pressure sensors have garnered considerable interest across diverse applications, including medical monitoring, electronic skin, and robotic tactile systems, owing to their straightforward fabrication process, low energy consumption, and robust stability. In this paper, we introduce a new inclined laser ablation (ILA) combined with demolding technique for the fabrication of flexible capacitive sensors featuring a microstructured dielectric layer composed of a tilted micropillar array (TMA). The design parameters of TMA were systematically optimized, including the tilt angle of the micropillars, array gap, aspect ratio, and bottom film thickness. The resulting capacitive pressure sensor, equipped with 75° tilted micropillar arrays (aspect ratio of 5:1), demonstrates exceptional sensitivity (1.12 kPa-1) within the low-pressure range (0-2 kPa), rapid response and recovery times, a low pressure detection limit of 0.25 Pa, and transient response stability factor as high as 243.9. The proposed capacitive pressure sensor was validated for the detection and recognition of human motions when attached to various body parts. Additionally, the sensors also demonstrated significant potential for diverse applications, including tactile sensing, and Morse code communication. © 2025 IEEE.

关键词： Aspect ratio

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