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Oscillation of Dzyaloshinskii–Moriya interaction driven by weak electric fields

Chinese Physics B 2024年第2期33卷 487-491页

作者：陈润泽曹安妮王馨苒柳洋杨洪新赵巍胜 Fert Beijing Institute MIIT Key Laboratory of SpintronicsSchool of Integrated Circuit Science and EngineeringBeihang UniversityBeijing 100191China Hefei Innovation Research Institute Beihang UniversityHefei 230013China Beihang-Goertek Joint Microelectronics Institute Qingdao Research InstituteBeihang UniversityQingdao 266000China National Laboratory of Solid State Microstructures School of PhysicsCollaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China Beijing Microelectronics Technology Institute Beijing 100076China

Dzyaloshinskii–Moriya interaction(DMI) is under extensive investigation considering its crucial status in chiral magnetic orders, such as Néel-type domain wall(DW) and skyrmions. It has been reported that the interfacial DMI originating from Rashba spin–orbit coupling(SOC) can be linearly tuned with strong external electric fields. In this work, we experimentally demonstrate that the strength of DMI exhibits rapid fluctuations, ranging from 10% to 30% of its original value, as a function of applied electric fields in Pt/Co/MgO heterostructures within the small field regime(< 10-2V/nm). Brillouin light scattering(BLS) experiments have been performed to measure DMI, and first-principles calculations show agreement with this observation, which can be explained by the variation in orbital hybridization at the Co/MgO interface in response to the weak electric fields. Our results on voltage control of DMI(VCDMI) suggest that research related to the voltage control of magnetic anisotropy for spin–orbit torque or the motion control of skyrmions might also have to consider the role of the external electric field on DMI as small voltages are generally used for the magnetoresistance detection.

关键词： Dzyaloshinskii-Moriya interaction weak electric field control effect Rashba spin-orbit coupling interfacial orbital hybridization

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Self‑Generated Buried Submicrocavities for High‑Performance Near‑Infrared Perovskite Light‑Emitting Diode

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Nano-Micro Letters 2023年第8期15卷 355-367页

作者： Jiong Li Chenghao Duan Qianpeng Zhang Chang Chen Qiaoyun Wen Minchao Qin Christopher C.S.Chan Shibing Zou Jianwu Wei Zuo Xiao Chuantian Zuo Xinhui Lu Kam Sing Wong Zhiyong Fan keyou Yan School of Environment and Energy State Key Lab of Luminescent Materials and DevicesGuangdong Provincial Key Laboratory of Solid Wastes Pollution Control and RecyclingSouth China University of TechnologyGuangzhou 510000People’s Republic of China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongPeople’s Republic of China Department of Physics The Chinese University of Hong KongShatinHong KongPeople’s Republic of China Department of Physics The Hong Kong University of Science and TechnologyClear Water BayKowloon 999077Hong KongPeople’s Republic of China School of Chemistry and Chemical Engineering Guangxi UniversityNanning 530004People’s Republic of China Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing 100190People’s Republic of China

ABSTRACT Embedding submicrocavities is an effective approach to improve the light out-coupling efficiency(LOCE)for planar perovskite light-emitting diodes(PeLEDs).In this work,we employ phenethylammonium iodide(PEAI)to trigger the Ostwald ripening for the downward recrystallization of perovskite,resulting in spontaneous formation of buried submicrocavities as light output *** simulation suggests the buried submicrocavities can improve the LOCE from 26.8 to 36.2%for near-infrared ***,PeLED yields peak external quantum efficiency(EQE)increasing from 17.3%at current density of 114 mA cm^(−2)to 25.5%at current density of 109 mA cm^(−2)and a radiance increasing from 109 to 487 W sr^(−1)m^(−2)with low *** turn-on voltage decreased from 1.25 to 1.15 V at 0.1 W sr^(−1)m^(−2).Besides,downward recrystallization process slightly reduces the trap density from 8.90×10^(15)to 7.27×10^(15)cm^(−3).This work provides a self-assembly method to integrate buried output coupler for boosting the performance of PeLEDs.

关键词： Perovskite light-emitting diodes Downward recrystallization Buried submicrocavities Light out-coupling efficiency Radiative recombination

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Advances in nuclear detection and readout techniques

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Nuclear science and Techniques 2023年第12期34卷 281-358页

作者： Rui He Xiao‑Yang Niu Yi Wang Hong‑Wei Liang Hong‑Bang Liu Ye Tian Hong‑Lin Zhang Chao‑Jie Zou Zhi‑Yi Liu Yun‑Long Zhang Hai‑Bo Yang Ju Huang Hong‑Kai Wang Wei‑Jia Han Bei Cao Gang Chen Cong Dai Li‑Min Duan Rui‑Rui Fan Fang‑Fa Fu Jian‑Hua Guo Dong Han Wei Jiang Xian‑Qin Li Xin Li Zhuo‑Dai Li Yu‑Tie Liang Shun Liao De‑Xu Lin Cheng‑Ming Liu Guo‑Rui Liu Jun‑Tao Liu Ze Long Meng‑Chen Niu Hao Qiu Hu Ran Xiang‑Ming Sun Bo‑Tan Wang Jia Wang Jin‑Xiang Wang Qi‑Lin Wang Yong‑Sheng Wang Xiao‑Chuan Xia Hao‑Qing Xie He‑Run Yang Hong Yin Hong Yuan Chun‑Hui Zhang Rui‑Guang Zhao Ran Zheng Cheng‑Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China Key Laboratory of Particle and Radiation Imaging Department of Engineering PhysicsTsinghua UniversityBeijing 100084China School of Integrated Circuits Dalian University of TechnologyDalian 116020China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Radiation Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China Electronic Engineering School Heilongjiang UniversityHarbin 150080China Spallation Neutron Source Science Center Dongguan 523803China Micro‑electronic Department Harbin Institute of TechnologyHarbin 150001China Purple Mountain Observatory Chinese Academy of SciencesNanjing 210034China Liaoning Academy of Materials Shenyang 110167China Istituto Nazionale di Fisica Nucleare Sezione di PerugiaPerugiaItaly School of Mathematics and Physics Chengdu University of TechnologyChengdu 620035China PLAC Key Laboratory of Quark and Lepton Physics(MOE)Central China Normal UniversityWuhan 430079China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China State Key Lab of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China

“A Craftsman Must Sharpen His Tools to Do His Job,”said *** detection and readout techniques are the foundation of particle physics,nuclear physics,and particle astrophysics to reveal the nature of the ***,they are being increasingly used in other disciplines like nuclear power generation,life sciences,environmental sciences,medical sciences,*** article reviews the short history,recent development,and trend of nuclear detection and readout techniques,covering Semiconductor Detector,Gaseous Detector,Scintillation Detector,Cherenkov Detector,Transition Radiation Detector,and Readout *** explaining the principle and using examples,we hope to help the interested reader underst and this research field and bring exciting information to the community.

关键词： Silicon detector Wide bandgap detector Time projection chamber(TPC) Multigap resistive plate chamber detector(MRPC) Scintillation detector Calorimeters Cherenkov detector Transition radiation detector Artificial intelligence Trigger-less readout

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Tauroursodeoxycholic acid(TUDCA)improves intestinal barrier function associated with TGR5-MLCK pathway and the alteration of serum metabolites and gut bacteria in weaned piglets

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Journal of Animal science and Biotechnology 2022年第6期13卷 1917-1931页

作者： Min Song Fenglin Zhang Yiming Fu Xin Yi Shengchun Feng Zhichang Liu Dun Deng Qiang Yang Miao Yu Canjun Zhu Xiaotong Zhu Lina Wang Ping Gao Gang Shu Xianyong Ma Qingyan Jiang Songbo Wang Guangdong Provincial Key Laboratory of Animal Nutrition Control ALLT ECH-SCAU Animal Nutrition Control Research Allianceand National Engineering Research Center for Breeding Swine IndustryCollege of Animal ScienceSouth China Agricultural UniversityGuangzhou 510642P.R.China Institute of Animal Science Guangdong Academy of Agricultural SciencesState Key Laboratory of Livestock and Poultry BreedingKey Laboratory of Animal Nutrition and Feed Science in South ChinaMinistry of Agriculture and Rural AffairsGuangdong Key Laboratory of Animal Breeding and NutritionGuangdong Engineering Technology Research Center of Animal Meat Quality and Safety Control and EvaluationGuangzhou 510640P.R.China Maoming Branch Guangdong Laboratory for Lingnan Modern Agricultural Science and TechnologyMaoming 525000P.R.China

Background:Tauroursodeoxycholic acid(TUDCA),a hydrophilic bile acid,is the main medicinal component of bear bile and is commonly used to treat a variety of hepatobiliary ***,TUDCA has been shown to modulate the intestinal barrier function and alleviate DSS-induced colitis in ***,the effect of TUDCA on the intestinal barrier of weaned piglets remains largely ***:The weaned piglets and porcine IPEC-J2 intestinal epithelial cells were used to investigate the effects of TUDCA on intestinal barrier function in weaned piglets and explore the possible underlying *** vivo,72 healthy weaned piglets were randomly allocated into 2 groups according to their gender and body weight,and piglets were fed the basal diet with 0(control,CON)and 200 mg/kg TUDCA for 30 d,*** female and three male piglets reflecting the average bodyweight were slaughtered in each group and samples were *** vitro,IPEC-J2 cells were subjected to 100μmol/L TUDCA to explore the possible underlying ***:Our results demonstrated that dietary TUDCA supplementation significantly reduced the diarrhea incidence of weaned piglets,possibly attributing to the TUDCA-enhanced intestinal barrier function and *** addition,TUDCA supplementation altered serum metabolites and the relative abundance of certain gut bacteria,which might contribute to the improved intestinal barrier ***,the in-vitro results showed that TUDCA improved the ***-induced epithelial barrier impairment of IPEC-J2 cells and increased Takeda G-coupled protein receptor 5(TGR5)protein ***,knockdown of TGR5 and inhibition of myosin light chain kinase(MLCK)pathway abolished the TUDCA-improved epithelial barrier impairment in ***-treated IPEC-J2 cells,indicating the involvement of TGR5-MLCK in this ***:These findings showed that TUDCA improved intestinal barrier function associated with TGR5-MLCK pathway and the al

关键词： Gut bacteria Intestinal barrier function Serum metabolites Tauroursodeoxycholic acid(TUDCA) TGR5-MLCK pathway Weaned piglets

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基于氧化钒的动态超表面

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science China Materials 2023年第1期66卷 284-290页

作者：李月谢建良邓龙江彭波 National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of ChinaChengdu 611731China Key Laboratory of Multi Spectral Absorbing Materials and Structures of Ministry of Education University of Electronic Science and Technology of ChinaChengdu 611731China

动态调控是现代光学器件中必不可少的特性,在激光雷达系统、显示器件以及数码相机等设备中具有重要意义.与传统的体光学器件相比,动态超表面为实现小型化和集成化智能光学系统提供了一个极具吸引力的解决方案.相变材料具有高的折射率对... 详细信息

动态调控是现代光学器件中必不可少的特性,在激光雷达系统、显示器件以及数码相机等设备中具有重要意义.与传统的体光学器件相比,动态超表面为实现小型化和集成化智能光学系统提供了一个极具吸引力的解决方案.相变材料具有高的折射率对比度和易于操控的特性,是设计动态超表面的理想材料.本文通过操控氧化钒的相变,在近红外区域设计实现了可调谐的超构透镜和可切换图像编码的超表面器件.通过控制氧化钒的相变,能够改变超透镜的聚焦强度,实现强度对比约为12倍的开关聚焦效果.此外,利用氧化钒的相变还设计实现了任意两个不同数字图案的可切换成像.本工作为实现动态成像和光学加密系统的可调谐超表面奠定了基础.

关键词：光学加密光学器件激光雷达系统氧化钒动态调控图像编码动态成像显示器件

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TICNet: Three-Branch Real-Time Semantic Segmentation Network with Intensive Compensation of Railway Track Scenes 5

TICNet: Three-Branch Real-Time Semantic Segmentation Network...

引用

5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Bai, Yiwen Yang, Lu Zhang, Lei Song, Yajing School of Transportation Science and Engineering Beihang University Beijing102206 China School of Mechanical Engineering Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechanical System Design and Intelligent Control Tianjin China National Demonstration Center for Experimental Mechanical and Electrical Engineering Tianjin300384 China Traffic Control Technology Co. Ltd. Beijing China

ISBN: (纸本)9798331530334

With the rapid development of railway traffic system, real-time semantic segmentation plays a crucial role in railway track scene monitoring. However, most of the existing methods use lightweight convolution to reduce the computational complexity, which results in reduced accuracy. To solve this problem, this paper proposes a real-time semantic segmentation network called TICNet, which is specifically used for accurate segmentation of railway track scenes. TICNet uses a novel three-branch structure designed for efficient feature extraction and accurate segmentation. Results on the RailSem19 dataset show that TICNet has a segmentation accuracy of 62.36% mIoU with an inference speed of 81.2 FPS, which is significantly better than many existing methods. © 2024 IEEE.

关键词： Semantic Segmentation

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Object Detection and Multiple Objective Optimization Manipulation Planning for Underwater Autonomous Capture in Oceanic Natural Aquatic Farm

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Journal of Field Robotics 2025年第0期

作者： Hai, Huang Tao, Jiang Xinyu, Bian Hao, Zhou Xu, Yang Gang, Wang Hongde, Qin Xinyue, Han National Key Laboratory of Science and Technology on Underwater Vehicle Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Bei Jing China

Underwater autonomous capture operations offer significant potential for reducing labor and health risks in sea organism industries. This study presents a comprehensive solution for cross-domain underwater object detection and autonomous capture. A novel unsupervised domain adaptive learning method is proposed, integrating multiscale domain adaptive modules and attention mechanisms into a Faster Region-Convolutional Neural Network framework. This approach enhances feature alignment across diverse aquatic domains without parameter tuning. Additionally, an efficient, parameterless constrained multiobjective optimization algorithm is introduced for underwater autonomous mobile capture, integrating parameterized trajectory planning with innovative features, such as adaptive mutation strategies and constraint violation tolerance. The proposed approaches are extensively validated through simulations, tank experiments, and real-world oceanic trials in the Natural Aquatic Farm of Zhangzidao Island. Results demonstrate the system's robustness in complex underwater environments with varying currents, with experimental outcomes validating the accuracy and reliability of detection and capture capabilities. This research significantly advances autonomous underwater systems' capabilities in object detection and capture tasks, addressing complex challenges in realistic organism capture applications across diverse aquatic environments. © 2025 Wiley Periodicals LLC.

关键词： Health risks

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Can Digital Intelligence and Cyber-Physical-Social Systems Achieve Global Food Security and Sustainability?

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2070-2080页

作者： Yanfen Wang Mengzhen Kang Yali Liu Juanjuan Li Kai Xue Xiujuan Wang Jianqing Du Yonglin Tian Qinghua Ni Fei-Yue Wang College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049 Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408 State Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Grassland Science Beijing Forestry UniversityBeijing 100083China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016 Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810001 Binzhou Institute of Technology Weiqiao-UCAS Science and Technology ParkBinzhou 256606China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078 State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078 Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China IEEE

Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration *** the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the

关键词： Carbon-water balance decision-support digital intelligence(DI) foundation models planning

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Research Progress of β-Ketoenamine-Linked Covalent Organic Frameworks for Photocatalytic Hydrogen Evolution

Organic Materials

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Organic Materials 2024年第2期6卷 18-32页

作者： Xue, Ping Li, Mingyuan Tang, Mi Wang, Zhengbang Wang, Chengliang School of Pharmacy Xianning Medical College Hubei University of Science and Technology Xianning 437100 China College of Chemistry and Molecular Sciences Hubei Key Laboratory of Electrochemical Power Sources Wuhan University Wuhan 430000 China Min. of Education Key Laboratory for the Green Preparation and Application of Functional Materials Hubei Key Laboratory of Polymer Materials Collab. Innov. Ctr. for Adv. Organic Chemical Materials Co-constructed by the Province and Ministry School of Materials Science and Engineering Hubei University Wuhan 430062 China School of Integrated Circuits Wuhan National Laboratory for Optoelectronics (WNLO) Huazhong University of Science and Technology Wuhan 430074 China

Ketoamide covalent organic frameworks (COFs), also named Tp-COFs, are considered to be a milestone material in the history of photocatalysts because of their excellent visible-light absorption, high crystallinity, ultra-high stability and structural diversity. In recent years, a large number of Tp-COFs and their composites have been successfully constructed based on molecular or composite engineering strategies, and exhibited splendid photocatalytic water splitting activity. In comparison with a composite strategy, the molecular engineering technique effectively avoids interface problems by designing and preparing frameworks at the molecular level. Therefore, it is necessary to timely summarize the construction of Tp-COF photocatalysts based on the molecular engineering strategy, so as to provide some theoretical basis and enlightenment for the subsequent development of high-performance Tp-COFs. Finally, the shortcomings and challenges of this technique and personal views on the further development of Tp-COFs are presented. © 2024 Georg Thieme Verlag. All rights reserved.

关键词： covalent organic frameworks hydrogen photocatalytic β-ketoenamine

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Knowledge-guided machine learning reveals pivotal drivers for gasto-particle conversion of atmospheric nitrate

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Environmental science and Ecotechnology 2024年第3期19卷 100-108页

作者： Bo Xu Haofei Yu Zongbo Shi Jinxing Liu Yuting Wei Zhongcheng Zhang Yanqi Huangfu Han Xu Yue Li Linlin Zhang Yinchang Feng Guoliang Shi State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control Tianjin Key Laboratory of Urban Transport Emission ResearchCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research(CLAER) College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of Civil Environmentaland Construction EngineeringUniversity of Central FloridaOrlandoFLUSA School of Geography Earth and Environment Sciences University of BirminghamBirminghamB152TTUK State Key Laboratory of Precision Measuring Technology and Instruments Tianjin Key Laboratory of air Pollutants Monitoring TechnologySchool of Precision Instrument and Opto-electronics EngineeringTianjin UniversityTianjin300072China Gigantic Technology(Tianjin)Co. LtdTianjin300072China College of Computer Science Nankai UniversityTianjin300350China China National Environmental Monitoring Centre Beijing100012China

Particulate nitrate,a key component of fine particles,forms through the intricate gas-to-particle conversion *** process is regulated by the gas-to-particle conversion coefficient of nitrate(ε(NO_(3)^(-))).The mechanism betweenε(NO_(3)^(-))and its drivers is highly complex and nonlinear,and can be characterized by machine learning ***,conventional machine learning often yields results that lack clear physical meaning and may even contradict established physical/chemical mechanisms due to the influence of ambient *** urgently needs an alternative approach that possesses transparent physical interpretations and provides deeper insights into the impact ofε(NO_(3)^(-)).Here we introduce a supervised machine learning approachdthe multilevel nested random forest guided by theory *** approach robustly identifies NH4 t,SO_(4)^(2-),and temperature as pivotal drivers forε(NO_(3)^(-)).Notably,substantial disparities exist between the outcomes of traditional random forest analysis and the anticipated actual ***,our approach underscores the significance of NH4 t during both daytime(30%)and nighttime(40%)periods,while appropriately downplaying the influence of some less relevant drivers in comparison to conventional random forest *** research underscores the transformative potential of integrating domain knowledge with machine learning in atmospheric studies.

关键词： Machine learning Data driven Theoretical approach Domain knowledge Guide

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