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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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High-Performance Mid-IR to Deep-UV van der Waals Photodetectors Capable of Local Spectroscopy at Room Temperature

arXiv

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arXiv 2022年

作者： Shen, Daozhi Yang, HeeBong Spudat, Christian Patel, Tarun Zhong, Shazhou Chen, Fangchu Yan, Jian Luo, Xuan Cheng, Meixin Sciaini, Germán Sun, Yuping Rhodes, Daniel A. Timusk, Thomas Zhou, Y. Norman Kim, Na Young Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Centre for Advanced Materials Joining Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Institute of Electrodynamics Microwave and Circuit Engineering TU Wien Gusshausstrasse 25/354 Vienna1040 Austria Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Key Laboratory of Materials Physics Institute of Solid State Physics HFIPS Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Waterloo Institute for Nanotechnology University of Waterloo WaterlooONN2L 3G1 Canada Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions High Magnetic Field Laboratory HFIPS Chinese Academy of Sciences Hefei230031 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Materials Science and Engineering University of Wisconsin MadisonWI United States Department of Physics and Astronomy McMaster University HamiltonONL8S 4M1 Canada

The ability to perform broadband optical spectroscopy with sub-diffraction-limit resolution is highly sought-after for a wide range of critical applications. However, sophisticated tip-enhanced techniques are currently required to achieve this goal. We bypass this challenge by demonstrating an extremely broadband photodetector based on a two-dimensional (2D) van der Waals heterostructure that is sensitive to light across over a decade in energy from the mid-infrared (MIR) to deep-ultraviolet (DUV) at room temperature. The devices feature high detectivity (> 109 cm Hz1/2 W-1) together with high bandwidth (2.1 MHz). The active area can be further miniaturized to submicron dimensions, far below the diffraction limit for the longest detectable wavelength of 4.1 µm, enabling such devices for facile measurements of local optical properties on atomic-layer-thickness samples placed in close proximity. This work can lead to the development of low-cost and high-throughput photosensors for hyperspectral imaging at the nanoscale. © 2022, CC BY.

关键词： Van der Waals forces

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Comparison of Learning Cellular Computational Networks with EKF and CPSO for Multi-Location Wind Speed Prediction 8

Comparison of Learning Cellular Computational Networks with ...

引用

8th IEEE Symposium Series on Computational Intelligence, SSCI 2018

作者： Pathiravasam, Hirath Venayagamoorthy, Ganesh Real-Time Power and Intelligent Systems Laboratory Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical Engineering University of Moratuwa Katubedda Sri Lanka School of Engineering University of KwaZulu-Natal Durban South Africa

ISBN: (纸本)9781538692769

Multiple-location wind speed prediction is a challenging and important problem in the modern power system operations. A distributed computing approach is implemented for spatio-temporal wind speed predictions. The multi-location wind speed prediction problem is carried out by respective cells in the Cellular Computational Network (CCN) framework. CCN based systems capture spatial dynamics via connections to neighboring cells. CCN is a framework for learning of learning systems. A Jordan-Elman hybrid recurrent neural network is used as the computational unit in the CCN. In this study, the effectiveness of Extended Kalman Filter (EKF) algorithm and Cooperative Particle Swarm optimization (CPSO) based learning algorithms in cell parameter estimation is compared. CCNs with neural networks (NNs) as computational units are essentially deep NNs via several neighbor cell connections. Three spatially distributed wind power stations are modeled using CCN framework to predict the respective wind speeds. Typical results illustrates that skill factors with EKF learning are positive and those with CPSO learning are negative. © 2018 IEEE.

关键词： Learning systems

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Randomized Compiling for Scalable Quantum computing on a Noisy Superconducting Quantum Processor

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Physical Review X 2021年第4期11卷 041039-041039页

作者： Akel Hashim Ravi K. Naik Alexis Morvan Jean-Loup Ville Bradley Mitchell John Mark Kreikebaum Marc Davis Ethan Smith Costin Iancu Kevin P. O’Brien Ian Hincks Joel J. Wallman Joseph Emerson Irfan Siddiqi Quantum Nanoelectronics Laboratory Department of Physics University of California at Berkeley Berkeley California 94720 USA Graduate Group in Applied Science and Technology University of California at Berkeley Berkeley California 94720 USA Computational Research Division Lawrence Berkeley National Lab Berkeley California 94720 USA Materials Sciences Division Lawrence Berkeley National Lab Berkeley California 94720 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02142 USA Quantum Benchmark Inc. Kitchener Ontario N2H 5G5 Canada Keysight Technologies Canada Kanata Ontario K2K 2W5 Canada Institute for Quantum Computing and Department of Applied Mathematics University of Waterloo Waterloo Ontario N2L 3G1 Canada

The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error that has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.

关键词： Quantum algorithms Quantum benchmarking Quantum control Quantum information with solid state qubits

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A potential superhard carbon allotrope: T5-carbon

arXiv

引用

arXiv 2020年

作者： Ding, Xian-Yong Zhang, Chao Wang, Dong-Qi Li, Bing-Sheng Wang, Qingping Yu, Zhi Gen Ang, Kah-Wee Zhang, Yong-Wei School of Materials Science and Engineering Anhui University of Science and Technology Huainan232001 China Multidisciplinary Initiative Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Environment-friendly Energy Materials Southwest University of Science and Technology Mianyang Sichuan621010 China Institute of High Performance Computing A*STAR 138632 Singapore Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

A novel stable carbon allotrope is predicted by using first-principles calculations. This allotrope is obtained by replacing one of the two atoms in the primitive cell of diamond with a carbon tetrahedron, thus it contains five atoms in one primitive cell, termed T5-carbon. The stabilities of T5-carbon are checked in structural, thermal, vibrational and energy calculations. The calculations on electronic, thermal, and mechanical properties reveal that T5-carbon is a semiconductor with an indirect band gap of 3.18 eV, and has a lattice thermal conductivity of 409 W/mK. More importantly, the Vickers hardness of T5-carbon is 76.5 GPa which is lower than that of diamond but larger than those of T-carbon and cubic boron nitride, confirming the superhard properties of T5-carbon, suggesting its wide applications in mechanical devices. Copyright © 2020, The Authors. All rights reserved.

关键词： Vickers hardness

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Correction to: A digital rights management system based on a scalable blockchain

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Peer-to-Peer Networking and Applications 2021年第4期14卷 1869-1869页

作者： Garba, Abba Dwivedi, Ashutosh Dhar Kamal, Mohsin Srivastava, Gautam Tariq, Muhammad Hasan, M. Anwar Chen, Zhong Key Laboratory of High Confidence Software Technologies Peking University MoE Beijing China Department of Computer Science and Technology EECS Peking University Beijing China Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark Department of Electrical Engineering National University of Computer and Emerging Sciences Peshawar Pakistan Department of Mathematics and Computer Science Brandon University Brandon Canada Research Center for Interneural Computing China Medical University Taichung Republic of China Princeton University Princeton USA Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada

A Correction to this paper has been published: https://***/10.1007/s12083-021-01098-2

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Roadmap on Atomic-scale Semiconductor Devices

arXiv

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arXiv 2025年

作者： Schofield, Steven R. Fisher, Andrew J. Ginossar, Eran Lyding, Joseph W. Silver, Richard Fei, Fan Namboodiri, Pradeep Wyrick, Jonathan Masteghin, M.G. Cox, D.C. Murdin, B.N. Clowes, S.K. Keizer, Joris G. Simmons, Michelle Y. Stemp, Holly G. Morello, Andrea Voisin, Benoit Rogge, Sven Wolkow, Robert A. Livadaru, Lucian Pitters, Jason Stock, Taylor J.Z. Curson, Neil J. Butera, Robert E. Pavlova, Tatiana V. Jakob, A.M. Spemann, D. Räcke, P. Schmidt-Kaler, F. Jamieson, D.N. Pratiush, Utkarsh Duscher, Gerd Kalinin, Sergei V. Kazazis, Dimitrios Constantinou, Procopios Aeppli, Gabriel Ekinci, Yasin Owen, James H.G. Fowler, Emma Moheimani, S.O. Reza Randall, John N. Misra, Shashank Ivie, Jeffrey Allemang, Christopher R. Anderson, Evan M. Bussmann, Ezra Campbell, Quinn Gao, Xujiao Lu, Tzu-Ming Schmucker, Scott W. London Centre for Nanotechnology University College London 17-19 Gordon St WC1H 0AH United Kingdom Department of Physics and Astronomy University College London WC1E 6BT United Kingdom School of Mathematics and Physics University of Surrey United Kingdom Department of Electrical and Computer Engineering University of Illinois UrbanaIL61801 United States Atom Scale Device Group National Institute of Standards and Technology GaithersburgMD20899 United States Advanced Technology Institute University of Surrey GU2 7XH United Kingdom School of Physics University of New South Wales Sydney Australia School of Electrical Engineering & Telecommunications UNSW Sydney Australia Silicon Quantum Computing Pty Ltd Sydney Australia Australia University of Alberta Department of Physics EdmontonT6G 2E1 Canada National Research Council of Canada Quantum and Nanotechnologies Research Centre 11421 Saskatchewan Drive NW EdmontonT6G 2M9 Canada Department of Electronic and Electrical Engineering University College London WC1E 7JE United Kingdom Laboratory for Physical Sciences University of Maryland College ParkMD20740 United States Prokhorov General Physics Institute The Russian Academy of Sciences Vavilov str. 38 Moscow119991 Russia School of Physics The University of Melbourne ParkvilleVIC3010 Australia Leipzig04318 Germany Universität Leipzig Felix Bloch Institute for Solid State Physics Applied Quantum Systems Leipzig04103 Germany QUANTUM Institut für Physik Johannes Gutenberg-Universität Mainz Mainz55128 Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Paul Scherrer Institute Villigen5232 Switzerland Zürich8093 Switzerland Zyvex Labs 1301 N. Plano Road RichardsonTX75081 United States University of Texas DallasTX United States Sandia National Laboratories 1515 Eubank Blvd. SE AlbuquerqueNM87185 United States

Spin states in semiconductors provide exceptionally stable and noise-resistant environments for qubits, positioning them as optimal candidates for reliable quantum computing technologies. The proposal to use nuclear and electronic spins of donor atoms in silicon, introduced by Kane in 1998, sparked a new research field focused on the precise positioning of individual impurity atoms for quantum devices, utilising scanning tunnelling microscopy and ion implantation. This roadmap article reviews the advancements in the 25 years since Kane’s proposal, the current challenges, and the future directions in atomic-scale semiconductor device fabrication and measurement. It covers the quest to create a silicon-based quantum computer and expands to include diverse material systems and fabrication techniques, highlighting the potential for a broad range of semiconductor quantum technological applications. Key developments include phosphorus in silicon devices such as single-atom transistors, arrayed few-donor devices, one- and two-qubit gates, three-dimensional architectures, and the development of a toolbox for future quantum integrated circuits. The roadmap also explores new impurity species like arsenic and antimony for enhanced scalability and higher-dimensional spin systems, new chemistry for dopant precursors and lithographic resists, and the potential for germanium-based devices. Emerging methods, such as photon-based lithography and electron beam manipulation, are discussed for their disruptive potential. This roadmap charts the path toward scalable quantum computing and advanced semiconductor quantum technologies, emphasising the critical intersections of experiment, technological development, and theory. © 2025, CC BY.

关键词： Qubits

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Structural Monoclinicity and its coupling to layered magnetism in few-layer CrI3

arXiv

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arXiv 2021年

作者： Guo, Xiaoyu Jin, Wencan Ye, Zhipeng Ye, Gaihua Xie, Hongchao Yang, Bowen Kim, Hyun Ho Yan, Shaohua Fu, Yang Tian, Shangjie Lei, Hechang Tsen, Adam W. Sun, Kai Yan, Jia-An He, Rui Zhao, Liuyan Department of Physics University of Michigan 450 Church Street Ann ArborMI48109 United States Department of Physics Auburn University 380 Duncan Drive AuburnAL36849 United States Department of Electrical and Computer Engineering Texas Tech University 910 Boston Ave LubbockTX79409 United States Institute for Quantum Computing Department of Physics and Astronomy Department of Chemistry University of Waterloo ONN2L 3G1 Canada School of Materials Science and Engineering Kumoh National Institute of Technology Gumi39177 Korea Republic of Department of Physics Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China Department of Physics Astronomy & Geosciences Towson University TowsonMD21252 United States

Using polarization-resolved Raman spectroscopy, we investigate layer number, temperature, and magnetic field dependence of Raman spectra in one- to four-layer CrI3. Layer-number-dependent Raman spectra show that in the paramagnetic phase a doubly degenerated Eg mode of monolayer CrI3 splits into one Ag and one Bg mode in N-layer (N > 1) CrI3 due to the monoclinic stacking. Their energy separation increases in thicker samples until an eventual saturation. Temperature-dependent measurements further show that the split modes tend to merge upon cooling but remain separated until 10 K, indicating a failed attempt of the monoclinic-to-rhombohedral structural phase transition that is present in the bulk crystal. Magnetic-field-dependent measurements reveal an additional monoclinic distortion across the magnetic-field-induced layered antiferromagnetism-to-ferromagnetism phase transition. We propose a structural change that consists of both a lateral sliding toward the rhombohedral stacking and a decrease in the interlayer distance to explain our experimental observations. Copyright © 2021, The Authors. All rights reserved.

关键词： Chromium compounds

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A New Superhard Carbon Allotrope: T5-Carbon

SSRN

引用

SSRN 2020年

作者： Ding, Xian-Yong Zhang, Chao Wang, Dong-Qi Li, Bing-Sheng Wang, Qingping Yu, Zhi Gen Ang, Kah-Wee Zhang, Yong-Wei School of Materials Science and Engineering Anhui University of Science and Technology Huainan232001 China Multidisciplinary Initiative Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China State Key Laboratory for Environment-friendly Energy Materials Southwest University of Science and Technology MianyangSichuan621010 China Institute of High Performance Computing A*STAR Singapore138632 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

A novel carbon allotrope is predicted by first-principles calculations. This allotrope is obtained by replacing one of the two atoms in the primitive cell of diamond with a carbon tetrahedron, thus it contains five atoms in one primitive cell, termed T5-carbon. The stabilities of T5-carbon are checked in structural, thermal, vibrational and energy calculations. T5-carbon is a semiconductor with an indirect band gap of 3.18 eV, and has a lattice thermal conductivity of 409 W/mK. The Vickers hardness of T5-carbon is 76.5 GPa which is lower than that of diamond but larger than those of T-carbon and cubic boron nitride. © 2020, The Authors. All rights reserved.

关键词： III-V semiconductors

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