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Cation and anion topotactic transformations in cobaltite thin films leading to Ruddlesden-Popper phases

Physical Review Materials 2021年第6期5卷 064416-064416页

作者： I-Ting Chiu Min-Han Lee Shaobo Cheng Shenli Zhang Larry Heki Zhen Zhang Yahya Mohtashami Pavel N. Lapa Mingzhen Feng Padraic Shafer Alpha T. N'Diaye Apurva Mehta Jon A. Schuller Giulia Galli Shriram Ramanathan Yimei Zhu Ivan K. Schuller Yayoi Takamura Department of Chemical Engineering University of California Davis Davis California 95616 USA Materials Science and Engineering Program University of California San Diego La Jolla California 92093 USA Department of Physics and Center for Advanced Nanoscience University of California San Diego La Jolla California 92093 USA Condensed Matter Physics and Materials Science Division Brookhaven National Laboratory Upton New York 11973 USA Pritzker School of Molecular Engineering The University of Chicago Chicago Illinois 60637 USA Materials Department University of California Santa Barbara Santa Barbara California 93106 USA School of Materials Engineering Purdue University West Lafayette Indiana 47907 USA Department of Electrical and Computer Engineering University of California Santa Barbara Santa Barbara California 93106 USA Department of Materials Science and Engineering University of California Davis Davis California 95616 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Materials Science Division and Center for Molecular Engineering Argonne National Laboratory Lemont Illinois 60439 USA

Topotactic transformations involve structural changes between related crystal structures due to a loss or gain of material while retaining a crystallographic relationship. The perovskite oxide La0.7Sr0.3CoO3 (LSCO) is an ideal system for investigating phase transformations due to its high oxygen vacancy conductivity, relatively low oxygen vacancy formation energy, and strong coupling of the magnetic and electronic properties to the oxygen stoichiometry. While the transition between cobaltite perovskite and brownmillerite (BM) phases has been widely reported, further reduction beyond the BM phase lacks systematic studies. In this paper, we study the evolution of the physical properties of LSCO thin films upon exposure to highly reducing environments. We observe the rarely reported crystalline Ruddlesden-Popper phase, which involves the loss of both oxygen anions and cobalt cations upon annealing where the cobalt is found as isolated Co ions or Co nanoparticles. First-principles calculations confirm that the concurrent loss of oxygen and cobalt ions is thermodynamically possible through an intermediary BM phase. The strong correlation of the magnetic and electronic properties to the crystal structure highlights the potential of utilizing ion migration as a basis for emerging applications such as neuromorphic computing.

关键词： Crystal defects Electrical conductivity Electronic structure of atoms & molecules Epitaxial strain First-principles calculations Magnetism Phase transitions

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Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

arXiv

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arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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Haptic display for virtual reality: progress and challenges

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Virtual Reality & Intelligent Hardware 2019年第2期1卷 136-162页

作者： Dangxiao WANG Yuan GUO Shiyi LIU Yuru ZHANG Weiliang XU Jing XIAO State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100083China Beijing Advanced Innovation Center for Biomedical Engineering Beihang UniversityBeijing 100083China Peng Cheng Laboratory Shenzhen 518055China Department of Mechanical Engineering University of AucklandAuckland 1142New Zealand School of Computer Science and the Robotics Engineering Program Worcester Polytechnic InstituteWorcesterMA 01609-2280USA

Immersion, interaction, and imagination are three features of virtual reality (VR). Existing VR systems possess fairly realistic visual and auditory feedbacks, and however, are poor with haptic feedback, by means of which human can perceive the physical world via abundant haptic properties. Haptic display is an interface aiming to enable bilateral signal communications between human and computer, and thus to greatly enhance the immersion and interaction of VR systems. This paper surveys the paradigm shift of haptic display occurred in the past 30 years, which is classified into three stages, including desktop haptics, surface haptics, and wearable haptics. The driving forces, key technologies and typical applications in each stage are critically reviewed. Toward the future high-fidelity VR interaction, research challenges are highlighted concerning handheld haptic device, multimodal haptic device, and high fidelity haptic rendering. In the end, the importance of understanding human haptic perception for designing effective haptic devices is addressed.

关键词： Haptic display Virtual reality Desktop haptics Surface haptics Wearable haptics Multimodal haptics

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Efficient Focusing with Large Numerical Aperture Using a Hybrid Metalens

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Physical Review Applied 2020年第4期13卷 044016-044016页

作者： Ming Kang Younes Ra'di Diego Farfan Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Photonics Initiative Advanced Science Research Center City University of New York New York 10031 USA Physics Program Graduate Center City University of New York New York 10016 USA Department of Electrical Engineering City College of The City University of New York New York 10031 USA

Focusing light using electrically thin layers is of paramount importance in several applications, from integrated optics to microwave engineering and sensing. Recently, gradient metasurfaces, which are electrically thin arrays of densely located polarizable particles, have been employed to perform different wave-front transformations, including focusing. In comparison to a bulk lens, these designs provide ultrathin geometries, but they suffer from fundamental limitations on their overall efficiency and achievable numerical aperture. Metagratings offer a solution for efficient beam steering at large angles, but it is challenging to utilize them in the small-angle limit. Here, we introduce a hybrid metalens design, which provides dramatic enhancement in lensing performance compared with that of state-of-the-art metasurfaces, combining metagratings and conventional gradient approaches. Our experimental prototype enables microwave focusing with large efficiency (η=0.479) and near-unity numerical aperture (NA=0.98), yielding a sharp focal point at the diffraction limit in the far field (FWHM=0.332λ). We propose a hybrid metalens design with exceptional performance in terms of efficiency and numerical aperture, opening up opportunities for high-throughput optical lithography, high-density data recording, focal plane arrays, radar, and communication systems.

关键词： Classical optics Integrated optics Metamaterials Optical & microwave phenomena Devices Surfaces

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Deep Learning in Cross-Lingual English-Vietnamese Sentiment Classification

Deep Learning in Cross-Lingual English-Vietnamese Sentiment ...

引用

International Conference on Knowledge and Systems Engineering (KSE)

作者： Thanh H. Nguyen Phuc H. Le Dien Dinh Advanced Program in Computer Science University of Science

ISBN: (纸本)9781538661147;9781538661130

The lack of Vietnamese sentiment resources limits the performance of lexicon-based methods in Vietnamese sentiment classification. However, most of the research is conducted for English language, and English resources are publicly far richer than Vietnamese. Therefore, the main problem we solve is to improve the performance of Vietnamese sentiment classification at document-level by leveraging translated English texts as training data. Vietnamese texts are machine-translated into English texts which are used to extract useful features. Our proposed method is based on Convolution Neural Network model for text classification. We propose to remove insignificant lexical features to speeds up the training process. Furthermore, we propose to replace normal max pooling layer with a static K-max pooling layer at convolutional layer to improve the overall accuracy of the model. Experiment results show our proposed method is able to perform sentiment classification with an accuracy of 84.40% on 25,991 Vietnamese and 25,991 translated English product reviews.

关键词： Target tracking Acoustics Radar tracking Microphones Helicopters Testing

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Inverse-designed silicon photonic circuit for nonreciprocal transmission

Inverse-designed silicon photonic circuit for nonreciprocal ...

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Frontiers in Optics, FIO 2019 - Part of Frontiers in Optics + Laser science APS/DLS

作者： Yang, Ki Youl Skarda, Jinhie Cotrufo, Michele Ahn, Geun Ho Alù, Andrea Vučković, Jelena E. L. Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712 United States Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Department of Electrical Engineering City University of New York New YorkNY10031 United States

Broadband nonreciprocal transmission is demonstrated in an inverse-designed silicon photonic circuit. Cascaded nonlinear resonators break the single-resonator fundamental bound on forward transmission at wide nonrecip... 详细信息

ISBN: (纸本)9781943580675

关键词： Silicon photonics

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Author Correction: Predicting the efficiency of prime editing guide RNAs in human cells

引用

Nature biotechnology 2024年第3期42卷 529页

作者： Hui Kwon Kim Goosang Yu Jinman Park Seonwoo Min Sungtae Lee Sungroh Yoon Hyongbum Henry Kim Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. Electrical and Computer Engineering Seoul National University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Graduate School of Data Science Seoul National University Seoul Republic of Korea. Department of Pharmacology Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Brain Korea 21 Plus Project for Medical Sciences Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Center for Nanomedicine Institute for Basic Science (IBS) Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of Nano Biomedical Engineering (NanoBME) Advanced Science Institute Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac. Severance Biomedical Science Institute Yonsei University College of Medicine Seoul Republic of Korea. hkim1@yuhs.ac. Graduate Program of NanoScience and Technology Yonsei University Seoul Republic of Korea. hkim1@yuhs.ac.

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Image Size, Color Depth, Age variant on Convolution Neural Network 1

Image Size, Color Depth, Age variant on Convolution Neural N...

引用

1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018

作者： Pranoto, Hady Budiharto, Widodo Hendric Spits Warnars, Harco Leslie Matsuo, Tokuro Heryadi, Yaya Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Graduate School of Industrial Technology Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9781538694220

Facial recognition as of biometric authentication used in the field of security, military, finance and daily use is become a trend or famous, because of its natural and not intrusive nature. Many methods for face recognition such as holistic learning, the use of local features, shallow learning and deep learning, some methods are susceptible to variations in pose change, illumination, expression and age variation. State of the art of face recognition today is a deep learning technique that delivers high accuracy. In this paper author replicate an face recognition using deep learning architecture called OpenFace Convolutional Neural Network. In this research author make variation on the size of image, color dept and age, and see how that factor impact on accuracy of face recognition in that architecture. As the result from the research, the accuracy of a model depends on the image size, color depth, and age variation, but in OpenFace CNN that recognition still provides fairly good accuracy when reducing the size of image and color depth, as long as the image can still be detected on the landmark facial, so the alignment process can be done on the face image. © 2018 IEEE.

关键词： Face recognition

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Zeeman polaritons as a platform for probing Dicke physics in condensed matter

arXiv

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arXiv 2024年

作者： Kritzell, T. Elijah Doumani, Jacques Asano, Tobias Yamada, Sota Tay, Fuyang Xu, Hongjing Yan, Han Katayama, Ikufumi Takeda, Jun Nevidomskyy, Andriy Nojiri, Hiroyuki Bamba, Motoaki Baydin, Andrey Kono, Junichiro Applied Physics Graduate Program Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University 6100 Main Street HoustonTX77005 United States Department of Physics Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Department of Physics and Astronomy Rice University 6100 Main Street HoustonTX77005 United States Smalley-Curl Institute Rice University 6100 Main Street HoustonTX77005 United States Institute for Multidisciplinary Sciences Yokohama National University 79-5 Tokiwadai Hodogaya-ku Kanagawa Yokohama240-8501 Japan Institute for Materials Research Tohoku University 2-1-1 Katahira Aoba-ku Miyagi Sendai980-8577 Japan Rice Advanced Materials Institute Rice University 6100 Main Street HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University 6100 Main Street HoustonTX77005 United States

The interaction of an ensemble of two-level atoms and a quantized electromagnetic field, described by the Dicke Hamiltonian, is an extensively studied problem in quantum optics. However, experimental efforts to explore similar physics in condensed matter typically employ bosonic matter modes (e.g., phonons, magnons, and plasmons) that are describable as simple harmonic oscillators, i.e., an infinite ladder of equally spaced energy levels. Here, we examine ultrastrong coupling between a coherent light mode and an ensemble of paramagnetic spins, a finite-multilevel system, in Gd3Ga5O12. The electron paramagnetic resonance of Gd3+ ions is tuned by a magnetic field into resonance with a Fabry–Pérot cavity mode, resulting in the formation of spin–photon hybrid states, or Zeeman polaritons. We observe that the light–matter coupling strength, measured through the vacuum Rabi splitting, decreases with increasing temperature, which can be explained by the temperature-dependent population difference between the lower and higher-energy states, a trait of a finite-level system. This finding demonstrates that a spin–boson system is more compatible with the Dicke model and has advantages over boson–boson systems for pursuing experimental realizations of phenomena predicted for ultrastrongly coupled light–matter hybrids. Copyright © 2024, The Authors. All rights reserved.

关键词： Phonons

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