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Realizing room-temperature ferromagnetism in metal-free graphene through vacancy-assisted hydrogenation

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Chain

Chain 2025年第1期2卷 72-80页

作者： Yongjie Xu Dingyi Yang Yu Zhang Gongkai Yuan Yici Zou Zhibo Zhang Yong Wang Yizhang Wu School of Education Jiangsu Open UniversityNanjing 210036China National Laboratory of Solid-State Microstructures Collaborative Innovation Center of Advanced Microstructuresand Jiangsu Provincial Key Laboratory for NanotechnologyNanjing UniversityNanjing 210093China Wide Bandgap Semiconductor Technology Disciplines State Key Laboratory School of MicroelectronicsAcademy of Advanced Interdisciplinary ResearchXidian UniversityXi’an 710071China Department of Physics Shaanxi University of Science and TechnologyXi’an 710021China Department of Applied Physical Sciences The University of North Carolina at Chapel HillChapel HillNC 27514USA Graduate School of Arts and Sciences Georgetown UniversityWashingtonDC 20057USA Department of Computer science and Engineering Michigan State UniversityEast LansingMI 48824USA Institute of Materials Engineering Nanjing UniversityNantong 226019China

Room-temperature ferromagnetism in graphene is a crucial step toward the practical application of spintronic *** hydrogen adsorption on graphene has been shown to induce magnetic moments,the overall efficiency remains low due to the clustering of hydrogen atoms and weak magnetic *** study demonstrates a highly effective vacancy-assisted hydrogenation method to synthesize hydrogenated graphene(HG)with robust room-temperature *** introduction of vacancies inhibits hydrogen clustering,increases magnetic edge atoms,and enhances the coupling between magnetic *** a result,HG exhibits a Curie temperature of 540 K and a saturation magnetization of 0.69 emu/g at 300 *** findings provide a new approach for the efficient hydrogenation of graphene,paving the way for its applications in spintronic devices.

关键词： graphene ferromagnetic hydrogenation vacancy-assisted room-temperature

Multi-Passive/Active-IRS Enhanced Wireless Coverage: Deployment Optimization and Cost-Performance Trade-off

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arXiv 2023年

作者： Fu, Min Mei, Weidong Zhang, Rui The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Both passive and active intelligent reflecting surfaces (IRSs) can be deployed in complex environments to enhance wireless network coverage by creating multiple blockage-free cascaded line-of-sight (LoS) links. In this paper, we study a multi-passive/active-IRS (PIRS/AIRS) aided wireless network with a multi-antenna base station (BS) in a given region. First, we divide the region into multiple non-overlapping cells, each of which may contain one candidate location that can be deployed with a single PIRS or AIRS. Then, we show several trade-offs between minimizing the total IRS deployment cost and enhancing the signal-to-noise ratio (SNR) performance over all cells via direct/cascaded LoS transmission with the BS. To reconcile these trade-offs, we formulate a joint multi-PIRS/AIRS deployment problem to select an optimal subset of all candidate locations for deploying IRS and also optimize the number of passive/active reflecting elements deployed at each selected location to satisfy a given SNR target over all cells, such that the total deployment cost is minimized. However, due to the combinatorial optimization involved, the formulated problem is difficult to be solved optimally. To tackle this difficulty, we first optimize the reflecting element numbers with given PIRS/AIRS deployed locations via sequential refinement, followed by a partial enumeration to determine the PIRS/AIRS locations. Simulation results show that our proposed algorithm achieves better cost-performance trade-offs than other baseline deployment strategies. © 2023, CC BY.

关键词： Location

Nearest Neighbor-Based Contrastive Learning for Hyperspectral and LiDAR Data Classification

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arXiv 2023年

作者： Wang, Meng Gao, Feng Dong, Junyu Li, Heng-Chao Du, Qian The School of Information Science and Engineering Ocean University of China Qingdao266100 China The Sichuan Provincial Key Laboratory of Information Coding and Transmission Southwest Jiaotong University Chengdu610031 China The Department of Electrical and Computer Engineering Mississippi State University StarkvilleMS39762 United States

The joint hyperspectral image (HSI) and LiDAR data classification aims to interpret ground objects at more detailed and precise level. Although deep learning methods have shown remarkable success in the multisource data classification task, self-supervised learning has rarely been explored. It is commonly nontrivial to build a robust self-supervised learning model for multisource data classification, due to the fact that the semantic similarities of neighborhood regions are not exploited in existing contrastive learning framework. Furthermore, the heterogeneous gap induced by the inconsistent distribution of multisource data impedes the classification performance. To overcome these disadvantages, we propose a Nearest Neighbor-based Contrastive Learning Network (NNCNet), which takes full advantage of large amounts of unlabeled data to learn discriminative feature representations. Specifically, we propose a nearest neighbor-based data augmentation scheme to use enhanced semantic relationships among nearby regions. The intermodal semantic alignments can be captured more accurately. In addition, we design a bilinear attention module to exploit the second-order and even high-order feature interactions between the HSI and LiDAR data. Extensive experiments on four public datasets demonstrate the superiority of our NNCNet over state-of-the-art methods. The source codes are available at https://***/summitgao/NNCNet. © 2023, CC BY.

关键词： Image classification

An Enhanced Dual-Currency VCG Auction Mechanism for Resource Allocation in IoV: A Value of Information Perspective

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arXiv 2025年

作者： Wang, Wei Cheng, Nan Zhou, Conghao Peng, Haixia Zhou, Haibo Su, Zhou Shen, Xuemin School of Telecommunications Engineering Xidian University Xi’an China State Key Laboratory of ISN School of Telecommunications Engineering Xidian University Xi’an China School of Information and Communications Engineering Xi’an Jiaotong University Xi’an China School of Electronic Science and Engineering Nanjing University Nanjing210023 China School of Cyber Science and Engineering Xi’an Jiaotong University Xi’an China Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada

The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However, the enhanced capabilities of loV have heightened the demands for efficient network resource allocation while simultaneously giving rise to diverse vehicular service requirements. For network service providers (NSPs), meeting the customized resource-slicing requirements of vehicle service providers (VSPs) while maximizing social welfare has become a significant challenge. This paper proposes an innovative solution by integrating a mean-field multi-agent reinforcement learning (MFMARL) framework with an enhanced Vickrey-Clarke-Groves (VCG) auction mechanism to address the problem of social welfare maximization under the condition of unknown VSP utility functions. The core of this solution is introducing the "value of information" as a novel monetary metric to estimate the expected benefits of VSPs, thereby ensuring the effective execution of the VCG auction mechanism. MFMARL is employed to optimize resource allocation for social welfare maximization while adapting to the intelligent and dynamic requirements of IoV. The proposed enhanced VCG auction mechanism not only protects the privacy of VSPs but also reduces the likelihood of collusion among VSPs, and it is theoretically proven to be dominant-strategy incentive compatible (DSIC). The simulation results demonstrate that, compared to the VCG mechanism implemented using quantization methods, the proposed mechanism exhibits significant advantages in convergence speed, social welfare maximization, and resistance to collusion, providing new insights into resource allocation in intelligent 6G networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Network function virtualization

The Role of Chalcogen Vacancies in Single Photon Emission from Monolayer Tungsten Dichalcogenides

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arXiv 2024年

作者： Gavin, S. Carin Zeman, Charles J. Dasgupta, Anushka Liu, Yiying Wu, Wenjing Huang, Shengxi Marks, Tobin J. Hersam, Mark C. Schatz, George C. Stern, Nathaniel P. Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Rice University HoustonTX77705 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Advanced Materials Institute Rice University HoustonTX77005 United States Department of Chemical and Biological Engineering Northwestern University EvanstonIL60208 United States Materials Research Center Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

Understanding the mechanism of single photon emission (SPE) in two-dimensional (2D) materials is an unsolved problem important for quantum optical materials and the development of quantum information applications. In 2D transition metal dichalcogenides (TMDs) such as tungsten diselenide (WSe2), quantum emission has been broadly attributed to exciton localization from atomic point defects, yet the precise microscopic picture is not fully understood. This work presents a new framework, supported by both computational and experimental evidence, to explain both the origins of facile SPE in WSe2 and the relative scarcity of SPE in the related 2D TMD, tungsten disulfide (WS2). A vertical divacancy configuration of selenium creates a defect-centered, direct energy gap in the band structure of WSe2, giving rise to highly localized, radiative transitions. This configuration is shown to be energetically preferred in the monolayer lattice, which is reflected in the abundant experimental observation of SPE in WSe2 both from the literature and this work. In contrast, the same vertical divacancy configuration in WS2 does not create direct localized transitions, consistent with scarce observations of SPE in that material. By revealing a single mechanism in tungsten-based TMDs that addresses the prevalence of SPE and is consistent between theory and experiment, these results provide a framework for better understanding the rules governing the atomic origins of single photon emission in TMDs. Copyright © 2024, The Authors. All rights reserved.

关键词： Nanocrystals

Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

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Model-Based Reinforcement Learning for Quantized Federated L...

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

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SSRN

SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

Direct imaging of asymmetric interfaces and electrostatic potentials inside a hafnia-zirconia ferroelectric nanocapacitor

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arXiv 2024年

作者： Durham, Daniel B. Noor, Manifa Aabrar, Khandker Akif Liu, Yuzi Datta, Suman Cho, Kyeongjae Guha, Supratik Phatak, Charudatta Materials Science Division Argonne National Laboratory LemontIL60439 United States Department of Materials Science and Engineering University of Texas at Dallas RichardsonTX75080 United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States Center for Nanoscale Materials Argonne National Laboratory LemontIL60439 United States Pritzker School for Molecular Engineering The University of Chicago ChicagoIL60637 United States

In hafnia-based thin-film ferroelectric devices, chemical phenomena during growth and processing such as oxygen vacancy formation and interfacial reactions appear to strongly affect device performance. However, the nanoscale structure, chemistry, and electrical potentials in these devices are not fully known, making it difficult to understand their influence on device properties. Here, we directly image the composition and electrostatic potential with nanometer resolution in the cross section of a nanocrystalline W / Hf0.5Zr0.5O2-δ (HZO) / W ferroelectric capacitor using multimodal electron microscopy. This reveals a 1.4 nm wide tungsten sub-oxide interfacial layer formed at the bottom interface during fabrication which introduces a potential dip and leads to asymmetric switching fields. Additionally, the measured inner potential in HZO is consistent with the presence of about 20% oxygen vacancies and a negative built-in potential in HZO. These chemical and electrostatic details are important to characterize and tune to achieve high performance ferroelectric devices. Copyright © 2024, The Authors. All rights reserved.

关键词： Zirconia

Universal inference meets random projections: a scalable test for log-concavity

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arXiv 2021年

作者： Dunn, Robin Gangrade, Aditya Wasserman, Larry Ramdas, Aaditya Advanced Methodology and Data Science Novartis Pharmaceuticals Corporation Electrical Engineering and Computer Science University of Michigan Department of Electrical & Computer Engineering Boston University United States Department of Statistics & Data Science Machine Learning Department Carnegie Mellon University United States

Shape constraints yield flexible middle grounds between fully nonparametric and fully parametric approaches to modeling distributions of data. The specific assumption of log-concavity is motivated by applications across economics, survival modeling, and reliability theory. However, there do not currently exist valid tests for whether the underlying density of given data is log-concave. The recent universal inference methodology provides a valid test. The universal test relies on maximum likelihood estimation (MLE), and efficient methods already exist for finding the log-concave MLE. This yields the first test of log-concavity that is provably valid in finite samples in any dimension, for which we also establish asymptotic consistency results. Empirically, we find that a random projections approach that converts the d-dimensional testing problem into many one-dimensional problems can yield high power, leading to a simple procedure that is statistically and computationally efficient. Copyright © 2021, The Authors. All rights reserved.

关键词： Maximum likelihood estimation