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Magnetization reversal driven by electron localization-delocalization crossover in the inverse spinel Co2VO4

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Physical Review B 2022年第9期105卷 094408-094408页

作者： Abhijit Bhat Kademane Churna Bhandari Durga Paudyal Stephen Cottrell Pinaki Das Yong Liu Yuen Yiu C. M. Naveen Kumar Konrad Siemensmeyer Andreas Hoser Diana Lucia Quintero-Castro David Vaknin Rasmus Toft-Petersen Department of Mathematics and Physics Universitetet i Stavanger 4036 Stavanger Norway Ames Laboratory Iowa State University Ames Iowa 50011 USA Department of Electrical and Computer Engineering Iowa State University Ames Iowa 50011 USA ISIS Facility STFC Rutherford Appleton Laboratory Harwell Science and Innovation Campus Chilton Didcot Oxon OX11 0QX United Kingdom Institute of Solid State Physics Vienna University of Technology Wiedner Hauptstrae 810 1040 Vienna Austria AGH University of Science and Technology Faculty of Physics and Applied Computer Science 30-059 Kraków Poland Helmholtz Zentrum Berlin für Materialien und Energie D-14109 Berlin Germany Department of Physics and Astronomy Iowa State University Ames Iowa 50011 USA Department of Physics Technical University of Denmark DK-2800 Kongens Lyngby Denmark European Spallation Source Partikelgatan 2 224 84 Lund Sweden

Neutron diffraction, magnetization, and muon spin relaxation measurements, supplemented by density functional theory (DFT) calculations are employed to unravel temperature-driven magnetization reversal in inverse spinel Co2VO4. All measurements show a second-order magnetic phase transition at TC=168K to a collinear ferrimagnetic phase. Neutron diffraction measurements reveal two antiparallel ferromagnetic (FM) sublattices, belonging to magnetic ions on two distinct crystal lattice sites, where the relative balance between the two sublattices determine the net FM moment in the unit cell. As the evolution of the ordered moment with temperature differs between the two sublattices, the net magnetic moment reaches a maximum at TNC=138K and reverses its sign at TMR=65K. The DFT results suggest that the underlying microscopic mechanism for the reversal is a delocalization of the unfilled 3d-shell electrons on one sublattice just below TC, followed by a gradual localization as the temperature is lowered. This delocalized-localized crossover is supported by muon spectroscopy results, as strong T1 relaxation observed below TC indicates fluctuating internal fields.

关键词： Density of states Ferrimagnetism Metal-insulator transition Oxides Spinels Density functional calculations Magnetization measurements Muon spin relaxation & rotation Neutron scattering

Rockburst prediction using artificial intelligence techniques:A review

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岩石力学通报（英文）

岩石力学通报（英文） 2024年第3期3卷 1-13页

作者： Yu Zhang Kongyi Fang Manchao He Dongqiao Liu Junchao Wang Zhengjia Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and ArchitectureBeijing100044China State Key Laboratory for Geomechanics and Deep Underground Engineering China University of Mining and TechnologyBeijing100083China Thomas Lord Department of Computer Science University of Southern CaliforniaLos Angeles90007CAUSA

Rockburst is a phenomenon where sudden,catastrophic failure of the rock mass occurs in underground deep regions or areas with high tectonic stress during the excavation *** disasters endanger the safety of people's lives and property,national energy security,and social interests,so it is very important to accurately predict *** rockburst prediction has not been able to find an effective prediction method,and the study of the rockburst mechanism is facing a *** the development of artificial intelligence(AI)techniques in recent years,more and more experts and scholars have begun to introduce AI techniques into the study of the rockburst *** previous research,several scholars have attempted to summarize the application of AI techniques in rockburst ***,these studies either are not specifically focused on reviews of the application of AI techniques in rockburst prediction,or they do not provide a comprehensive *** on the advantages of extensive interdisciplinary research and a deep understanding of AI techniques,this paper conducts a comprehensive review of rockburst prediction methods leveraging AI ***,pertinent definitions of rockburst and its associated hazards are ***,the applications of both traditional prediction methods and those rooted in AI techniques for rockburst prediction are summarized,with emphasis placed on the respective advantages and disadvantages of each ***,the strengths and weaknesses of prediction methods leveraging AI are summarized,alongside forecasting future research trends to address existing challenges,while simultaneously proposing directions for improvement to advance the field and meet emerging demands effectively.

关键词： Rockburst Rockburst prediction Artificial intelligence techniques

Lifetime maximization for UAV-enabled cognitive-NOMA IoT networks: Joint location, power, and decoding order optimization

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arXiv 2021年

作者： Tang, Na Tang, Hongying Le-Ngoc, Tho Li, Baoqing Yuan, Xiaobing Science and Technology on Microsystem Laboratory Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China The Science and Technology on Microsystem Laboratory Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Department of Electrical and Computer Engineering McGill University MontrealQCH3A 0G4 Canada

This paper investigates a cognitive unmanned aerial vehicle (UAV) enabled Internet of Things (IoT) network, where secondary/cognitive IoT devices upload their data to the UAV hub following a non-orthogonal multiple access (NOMA) protocol in the spectrum of the primary network. We aim to maximize the minimum lifetime of IoT devices by jointly optimizing the UAV location, transmit power, and decoding order subject to interference-power constraints in presence of the imperfect channel state information (CSI). To solve the formulated non-convex mixed-integer programming problem, we first jointly optimize the UAV location and transmit power for a given decoding order and obtain the globally optimal solution with the assistance of Lagrange duality and then obtain the best decoding order by exhaustive search, which is applicable to relatively small-scale scenarios. For large-scale scenarios, we propose a low-complexity sub-optimal algorithm by transforming the original problem into a more tractable equivalent form and applying the successive convex approximation (SCA) technique and penalty function method. Numerical results demonstrate that the proposed design significantly outperforms the benchmark schemes. © 2021, CC BY.

关键词： Unmanned aerial vehicles (UAV)

Beamforming Design and Association Scheme for Multi-RIS Multi-User mmWave Systems Through Graph Neural Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Mengbing Huang, Chongwen Alhammadi, Ahmed Renzo, Marco Di Debbah, Merouane Yuen, Chau Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates Université Paris-Saclay CNRS Laboratoire des Signaux et Systèmes CentraleSupélec 3 Rue Joliot-Curie Gif-sur-Yvette91192 France King’s College London Centre for Telecommunications Research – Department of Engineering LondonWC2R 2LS United Kingdom Khalifa University KU6G. Research Center Department of Computer and Information Engineering Abu Dhabi127788 United Arab Emirates University Paris-Saclay CentraleSupelec Gif-sur-Yvette91192 France

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multi-user millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%. © 2002-2012 IEEE.

关键词： Beamforming

TensorCircuit: a Quantum Software Framework for the NISQ Era

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arXiv 2022年

作者： Zhang, Shi-Xin Allcock, Jonathan Wan, Zhou-Quan Liu, Shuo Sun, Jiace Yu, Hao Yang, Xing-Han Qiu, Jiezhong Ye, Zhaofeng Chen, Yu-Qin Lee, Chee-Kong Zheng, Yi-Cong Jian, Shao-Kai Yao, Hong Hsieh, Chang-Yu Zhang, Shengyu Tencent Quantum Laboratory Tencent Guangdong Shenzhen518057 China Tencent Quantum Laboratory Tencent Hong Kong Institute for Advanced Study Tsinghua University Beijing100084 China Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Electrical and Computer Engineering McGill University QuebecH3A 0E9 Canada Shenzhen Middle School Guangdong Shenzhen518025 China Tencent America Palo AltoCA94306 United States Department of Physics Brandeis University WalthamMA02453 United States

TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and built on top of industry-standard machine learning frameworks, TensorCircuit supports automatic differentiation, just-in-time compilation, vectorized parallelism and hardware acceleration. These features allow TensorCircuit to simulate larger and more complex quantum circuits than existing simulators, and are especially suited to variational algorithms based on parameterized quantum circuits. TensorCircuit enables orders of magnitude speedup for various quantum simulation tasks compared to other common quantum software, and can simulate up to 600 qubits with moderate circuit depth and low-dimensional connectivity. With its time and space efficiency, flexible and extensible architecture and compact, user-friendly API, TensorCircuit has been built to facilitate the design, simulation and analysis of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era. © 2022, CC BY.

关键词： Python

Reduced Recombination via Tunable Surface Fields in Perovskite Solar Cells

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arXiv 2022年

作者： deQuilettes, Dane W. Yoo, Jason Jungwan Brenes, Roberto Kosasih, Felix Utama Laitz, Madeleine Dou, Benjia Dak Graham, Daniel J. Ho, Kevin Shin, Seong Sik Ducati, Caterina Bawendi, Moungi Bulović, Vladimir Research Laboratory of Electronics Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA United States Department of Chemistry Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA United States Division of Advanced Materials Korea Research Institute of Chemical Technology Daejeon Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Materials Science and Metallurgy University of Cambridge 27 Charles Babbage Road CambridgeCB3 0FS United Kingdom Department of Bioengineering University of Washington Box 351653 SeattleWA United States Department of Chemistry University of Washington Box 351700 SeattleWA United States

The ability to reduce energy loss at semiconductor surfaces through passivation or surface field engineering has become an essential step in the manufacturing of efficient photovoltaic (PV) and optoelectronic devices.1 Similarly, surface modification of emerging halide perovskites with quasi-2D heterostructures is now ubiquitous to achieve PV power conversion efficiencies (PCEs) > 22% and has enabled single-junction PV devices to reach 25.7%, yet a fundamental understanding to how these treatments function is still generally lacking.2 This has established a bottleneck for maximizing beneficial improvements as no concrete selection and design rules currently exist.2 Here we uncover a new type of tunable passivation strategy and mechanism found in perovskite PV devices that were the first to reach the > 25% PCE milestone,3 which is enabled by surface treating a bulk perovskite layer with hexylammonium bromide (HABr). We uncover the simultaneous formation of an iodide-rich 2D layer along with a Br halide gradient achieved through partial halide exchange that extends from defective surfaces and grain boundaries4 into the bulk layer. We demonstrate and directly visualize the tunability of both the 2D layer thickness, halide gradient, and band structure using a unique combination of depth-sensitive nanoscale characterization techniques. We show that the optimization of this interface can extend the charge carrier lifetime to values > 30 μs, which is the longest value reported for a direct bandgap semiconductor (GaAs, InP, CdTe) over the past 50 years.5 Furthermore, we show that this heterostructure is well suited for a host of optoelectronic devices where we achieve a new benchmark for perovskite/charge transport layer surface recombination velocity with values -1. Importantly, this work reveals an entirely new strategy and knob for optimizing and tuning recombination and charge transport at semiconductor interfaces and will likely establish new frontiers in achieving the

关键词： Perovskite

Protocol stack perspective for low latency and massive connectivity in future cellular networks

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arXiv 2021年

作者： Shah, Syed Waqas Haider Mian, Adnan Noor Mumtaz, Shahid Wen, Miaowen Hong, T. Kadoch, Michel Department of Electrical Engineering Information Technology University Lahore54000 Pakistan Computer Laboratory University of Cambridge 15 JJ Thomson Avenue CambridgeCB3 0FD United Kingdom Instituto de Telecommuninicoes DETI Universidade de Aveiro Aveiro4554 Portugal School of Electronic Engineering South China University of Technology Guangzhou510640 China School of Electronic and Information Engineering Beihang University Beijing China Department of Electrical Engineering Ecole de Technologie Superieure MontrealQC Canada

With the emergence of Internet-of-Things (IoT) and ever-increasing demand for the newly connected devices, there is a need for more effective storage and processing paradigms to cope with the data generated from these devices. In this study, we have discussed different paradigms for data processing and storage including Cloud, Fog, and Edge computing models and their suitability in integrating with the IoT. Moreover, a detailed discussion on low latency and massive connectivity requirements of future cellular networks in accordance with machine-type communication (MTC) is also presented. Furthermore, the need to bring IoT devices to Internet connectivity and a standardized protocol stack to regulate the data transmission between these devices is also addressed, while keeping in view the resourceconstraint nature of IoT devices. © 2021, CC BY.

关键词： Internet of things

X-ray quantification of oxygen groups on diamond surfaces for quantum applications

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Materials for Quantum technology 2023年第4期3卷 045901-045901页

作者： Dontschuk, N. Rodgers, L.V.H. Chou, J.P. Evans, D.A. O’Donnell, K.M. Johnson, H.J. Tadich, A. Schenk, A.K. Gali, A. de Leon, N.P. Stacey, A. School of Physics University of Melbourne ParkvilleVIC3010 Australia Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Physics National Changhua University of Education Changhua Taiwan Department of Physics Aberystwyth University AberystwythSY23 3BZ United Kingdom Royal Melbourne Hospital University of Melbourne ParkvilleVIC3010 Australia Australian Synchrotron 800 Blackburn Road ClaytonVIC3168 Australia School of Computing Engineering and Mathematical Sciences La Trobe University BundooraVIC3086 Australia Wigner Research Centre for Physics POB 49 BudapestH-1525 Hungary Department of Atomic Physics Budapest University of Technology and Economics Műegyetem rakpart 3. BudapestH-1111 Hungary Princeton Plasma Physics Laboratory 100 Stellarator Road PrincetonNJ08544 United States School of Science RMIT University MelbourneVIC3000 Australia

Identifying the surface chemistry of diamond materials is increasingly important for device applications, especially quantum sensors. Oxygen-related termination species are widely used because they are naturally abundant, chemically stable, and compatible with stable nitrogen vacancy centres near the diamond surface. Diamond surfaces host a mixture of oxygen-related species, and the precise chemistry and relative coverage of different species can lead to dramatically different electronic properties, with direct consequences for near-surface quantum sensors. However, it is challenging to unambiguously identify the different groups or quantify the relative surface coverage. Here we show that a combination of x-ray absorption and photoelectron spectroscopies can be used to quantitatively identify the coverage of carbonyl functional groups on the { 100 } diamond surface. Using this method we reveal an unexpectedly high fraction of carbonyl groups ( > 9%) on a wide range of sample surfaces. Furthermore, through a combination of ab initio calculations and spectroscopic studies of engineered surfaces, we reveal unexpected complexities in the x-ray spectroscopy of oxygen terminated diamond surfaces. Of particular note, we find the binding energies of carbonyl-related groups on diamond differs significantly from other organic systems, likely resulting in previous misestimation of carbonyl fractions on diamond surfaces. © 2023 The Author(s). Published by IOP Publishing Ltd.

关键词： Oxygen

Photo-assisted technologies for environmental remediation

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Nature Reviews Clean Technology

Nature Reviews Clean technology 2025年第3期1卷 1-15页

作者： Bo Weng Jiangquan Lv Ning Han Bao-Lian Su Maarten Roeffaers Johan Hofkens Yongfa Zhu Shaobin Wang Wonyong Choi State Key Laboratory of Advanced Environmental Technology Institute of Urban Environment Chinese Academy of Sciences Xiamen China University of Chinese Academy of Sciences Beijing China College of Electronics and Information Science Fujian Jiangxia University Fuzhou China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Laboratory of Living Materials at the State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan China cMACS Department of Microbial and Molecular Systems KU Leuven Leuven Belgium Department of Chemistry KU Leuven Leuven Belgium Department of Chemistry Tsinghua University Beijing China School of Chemical Engineering University of Adelaide Adelaide South Australia Australia Department of Energy Engineering Korea Institute of Energy Technology (KENTECH) Naju South Korea

Industrial processes can lead to air and water pollution, particularly from organic contaminants such as toluene and antibiotics, posing threats to human health. Photo-assisted chemical oxidation technologies leverage light energy to mineralize these contaminants. In this Review, we discuss the mechanisms and efficiencies of photo-assisted advanced oxidation processes for wastewater treatment and photothermal technologies for air purification. The integration of solar energy enhances degradation efficiency and reduces energy consumption, enabling more efficient remediation methods. We evaluate the technological aspects of photo-assisted technologies, such as photo-Fenton, photo-persulfate activation, photo-ozonation and photoelectrochemical oxidation, emphasizing their potential for practical applications. Finally, we discuss the challenges in scaling up photo-assisted technologies for specific environmental remediation needs. Photo-assisted technologies have demonstrated effectiveness in environmental remediation, although large-scale applications remain constrained by high costs. Future potential applications of photo-assisted technologies will require that technology selection be tailored to specific pollution scenarios and engineering processes optimized to minimize costs.

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