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Publisher Correction: An electrochemical thermal transistor

Nature communications 2019年第1期10卷 4465页

作者： Aditya Sood Feng Xiong Shunda Chen Haotian Wang Daniele Selli Jinsong Zhang Connor J McClellan Jie Sun Davide Donadio Yi Cui Eric Pop Kenneth E Goodson Department of Materials Science and Engineering Stanford University Stanford CA 94305 USA. Department of Mechanical Engineering Stanford University Stanford CA 94305 USA. Department of Electrical Engineering Stanford University Stanford CA 94305 USA. Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA 15261 USA. Department of Chemistry University of California Davis CA 95616 USA. Department of Chemical and Biomolecular Engineering Rice University Houston TX 77005 USA. Max Planck Institute for Polymer Research Ackermannweg 10 D-55128 Mainz Germany. Dipartimento di Scienza dei Materiali Universita di Milano-Bicocca 20125 Milano Italy. School of Chemical Engineering and Technology Tianjin University 300350 Tianjin China. Ikerbasque Basque Foundation for Science E-48011 Bilbao Spain. Department of Materials Science and Engineering Stanford University Stanford CA 94305 USA. yicui@stanford.edu. Stanford Institute for Materials and Energy Science SLAC National Accelerator Laboratory Menlo Park CA 94025 USA. yicui@stanford.edu. Department of Materials Science and Engineering Stanford University Stanford CA 94305 USA. epop@stanford.edu. Department of Electrical Engineering Stanford University Stanford CA 94305 USA. epop@stanford.edu. Precourt Institute for Energy Stanford University Stanford CA 94305 USA. epop@stanford.edu. Department of Materials Science and Engineering Stanford University Stanford CA 94305 USA. goodson@stanford.edu. Department of Mechanical Engineering Stanford University Stanford CA 94305 USA. goodson@stanford.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Defect engineering by Codoping in KCaI3:Eu2+ Single-Crystalline Scintillators

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Physical Review Applied 2017年第3期8卷 034011-034011页

作者： Yuntao Wu Qi Li Steven Jones Chaochao Dun Sheng Hu Mariya Zhuravleva Adam C. Lindsey Luis Stand Matthew Loyd Merry Koschan John Auxier, II Howard L. Hall Charles L. Melcher Scintillation Materials Research Center University of Tennessee Knoxville Tennessee 37996 USA Department of Materials Science and Engineering University of Tennessee Knoxville Tennessee 37996 USA Physical Science Division IBM Thomas J Watson Research Center Yorktown Heights New York 10598 USA Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Nuclear Engineering University of Tennessee Knoxville Tennessee 37996 USA Department of Physics Wake Forest University Winston-Salem North Carolina 27109 USA Department of Chemical and Biomolecular Engineering University of Tennessee Knoxville Tennessee 37996 USA

Eu2+-doped alkali or alkali earth iodide scintillators with energy resolutions ≤3% at 662 keV promise the excellent discrimination ability for radioactive isotopes required for homeland-security and nuclear-nonproliferation applications. To extend their applications to x-ray imaging, such as computed tomography scans, the intense afterglow which delays the response time of such materials is an obstacle that needs to be overcome. However, a clear understanding of the origin of the afterglow and feasible solutions is still lacking. In this work, we present a combined experimental and theoretical investigation of the physical insights of codoping-based defect engineering which can reduce the afterglow effectively in KCaI3:Eu2+ single-crystal scintillators. We illustrate that Sc3+ codoping greatly suppresses the afterglow, whereas Y3+, Gd3+, or La3+ codoping enhances the afterglow. Meanwhile, a light yield of 57 000 photons/MeV and an energy resolution of 3.4% at 662 keV can be maintained with the appropriate concentration of Sc3+ codoping, which makes the material promising for medical-imaging applications. Through our thermoluminescence techniques and density-functional-theory calculations, we are able to identify the defect structures and understand the mechanism by which codoping affects the scintillation performance of KCaI3:Eu2+ crystals. The proposed defect-engineering strategy is further validated by achieving afterglow suppression in Mg2+ codoped KCaI3:Eu2+ single crystals.

关键词： Crystal defects Crystal growth Dopants Luminescence Point defects Solid-state detectors Medical imaging Scintillators

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Capacity-Constrained Network-Voronoi Diagram

Capacity-Constrained Network-Voronoi Diagram

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IEEE International Conference on Data engineering

作者： KwangSoo Yang Apurv Hirsh Shekhar Dev Oliver Shashi Shekhar Computer Science Florida Atlantic University Chemical & Biomolecular Engineering Johns Hopkins University ESRI Computer Science University of Minnesota

ISBN: (纸本)9781509020218

Given a graph and a set of service center nodes, a Capacity Constrained Network-Voronoi Diagram (CCNVD) partitions the graph into a set of contiguous service areas that meet service center capacities and minimize the sum of the shortest distances from graph-nodes to allotted service centers. The CCNVD problem is important for critical societal applications such as assigning evacuees to shelters and assigning patients to hospitals. This problem is NPO-hard;it is computationally challenging because of the large size of the transportation network and the constraint that service areas must be contiguous in the graph to simplify communication of allotments. Previous work has focused on honoring either service area contiguity (e.g., Network Voronoi Diagrams) or service center capacity constraints (e.g., min-cost flow), but not both. We introduced a novel Pressure Equalizer (PE) approach for CCNVD to meet the capacity constraints of service centers while maintaining the contiguity of service areas. However, we find that the main bottleneck of the PE algorithm is testing whether service areas are contiguous. We propose novel algorithms that reduce the computational cost. Experiments using road maps from five different regions demonstrate that the proposed approaches significantly reduce computational cost for the PE approach.

关键词： Computational efficiency Roads Partitioning algorithms Scalability computer science Hospitals

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Correction: Stable DNA-based reaction–diffusion patterns

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RSC Advances 2017年第51期7卷 31969-31969页

作者： John Zenk Dominic Scalise Kaiyuan Wang Phillip Dorsey Joshua Fern Ariana Cruz Rebecca Schulman Chemical and Biomolecular Engineering Johns Hopkins University Baltimore Maryland 21218 USA Computer Science Johns Hopkins University Baltimore Maryland 21218 USA

Correction for ‘Stable DNA-based reaction–diffusion patterns’ by John Zenk et al., RSC Adv., 2017, 7, 18032–18040.

Correction for ‘Stable DNA-based reaction–diffusion patterns’ by John Zenk et al., RSC Adv., 2017, 7, 18032–18040.

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Adaptive Soft Robots: Soft Ultrathin Electronics Innervated Adaptive Fully Soft Robots (Adv. Mater. 13/2018)

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Advanced Materials 2018年第13期30卷

作者： Chengjun Wang Kyoseung Sim Jin Chen Hojin Kim Zhoulyu Rao Yuhang Li Weiqiu Chen Jizhou Song Rafael Verduzco Cunjiang Yu Department of Mechanical Engineering University of Houston Houston TX 77204 USA Department of Engineering Mechanics and Soft Matter Research Center Zhejiang University Hangzhou 310027 China Materials Science and Engineering Program University of Houston Houston TX 77204 USA Institute of Solid Mechanics Beihang University Beijing 100191 China Department of Chemical and Biomolecular Engineering Rice University Houston TX 77005 USA Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou 310027 China Department of Materials Sciences and Nano Engineering Rice University Houston TX 77005 USA Department of Electrical and Computer Engineering Department of Biomedical Engineering University of Houston Houston TX 77204 USA

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An emergent space for distributed data with hidden internal order through manifold learning

arXiv

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arXiv 2017年

作者： Kemeth, Felix P. Haugland, Sindre W. Dietrich, Felix Bertalan, Tom Höhlein, Kevin Li, Qianxiao Bollt, Erik M. Talmon, Ronen Krischer, Katharina Kevrekidis, Ioannis G. Physik-Department Nonequilibrium Chemical Physics Technische Universität München James-Franck-Str. 1 GarchingD-85748 Germany Institute for Advanced Study Technische Universität München Lichtenbergstr. 2a GarchingD-85748 Germany Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States Department of Chemical and Biomolecular Engineering Department of Applied Mathematics and Statistics Johns Hopkins University JHMI Department of Mechanical Engineering MIT CambridgeMA02139 United States Institute of High Performance Computing 1 Fusionopolis Way #16-16 Connexis North Singapore138632 Singapore Department of Mathematics Department of Electrical and Computer Engineering Clarkson Center for Complex Systems Science Clarkson University PotsdamNY13699-5815 United States Department of Electrical Engineering Technion Israel Institute of Technology Technion City Haifa32000 Israel

Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations;these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modelled as a spatially distributed system (e.g. a partial differential equation, PDE), but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified - to "emerge from"- the data mining process. We will first validate this "emergent space" reconstruction for time series sampled without space labels in known PDEs;this brings up the issue of observability of physical space from temporal observation data, and the transition from spatially resolved to lumped (order-parameter-based) representations by tuning the scale of the data mining kernels. We will then present actual emergent space "discovery" illustrations. Our illustrative examples include chimera states (states of coexisting coherent and incoherent dynamics), and chaotic as well as quasiperiodic spatiotemporal dynamics, arising in partial differential equations and/or in heterogeneous networks. We also discuss how datadriven "spatial" coordinates can be extracted in ways invariant to the nature of the measuring instrument. Such gauge-invariant data mining can go beyond the fusion of heterogeneous observations of the same system, to the possible matching of apparently different systems. For an older version of this article, including other examples, see https://***/abs/1708.05406. Copyright © 2017, The Authors. All rights reserved.

关键词： Data mining

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Jet substructure classification in high-energy physics with deep neural networks

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Physical Review D 2016年第9期93卷 094034-094034页

作者： Pierre Baldi Kevin Bauer Clara Eng Peter Sadowski Daniel Whiteson Department of Computer Science University of California Irvine California 92697 USA Department of Physics and Astronomy University of California Irvine California 92697 USA Department of Chemical and Biomolecular Engineering University of California Berkeley California 94270 USA

At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet is due to a low-mass single particle or due to multiple decay objects of a massive particle is an important problem in the analysis of collider data. Traditional approaches have relied on expert features designed to detect energy deposition patterns in the calorimeter, but the complexity of the data make this task an excellent candidate for the application of machine learning tools. The data collected by the detector can be treated as a two-dimensional image, lending itself to the natural application of image classification techniques. In this work, we apply deep neural networks with a mixture of locally connected and fully connected nodes. Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current state-of-the-art approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimated hadronic particles, and that such performance gains persist in the presence of pileup interactions.

关键词： Quark & gluon jets

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Optical Waveguides: Flexible Transient Optical Waveguides and Surface‐Wave Biosensors Constructed from Monocrystalline Silicon (Adv. Mater. 32/2018)

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Advanced Materials 2018年第32期30卷

作者： Wubin Bai Hongjun Yang Yinji Ma Hao Chen Jiho Shin Yonghao Liu Quansan Yang Irawati Kandela Zhonghe Liu Seung‐Kyun Kang Chen Wei Chad R. Haney Anlil Brikha Xiaochen Ge Xue Feng Paul V. Braun Yonggang Huang Weidong Zhou John A. Rogers Department of Materials Science and Engineering Northwestern University Evanston IL 60208 USA Center for Bio‐Integrated Electronics Northwestern University Evanston IL 60208 USA Department of Electrical Engineering University of Texas at Arlington Arlington TX 76019‐0072 USA Department of Engineering Mechanics Center for Mechanics and Materials Tsinghua University Beijing 100084 China Department of Materials Science and Engineering Frederick Seitz Materials Research Laboratory Beckman Institute for Advanced Science and Technology University of Illinois Urbana‐Champaign Urbana IL 61801 USA Department of Chemical and Biomolecular Engineering University of Illinois Urbana‐Champaign Urbana IL 61801 USA Department of Mechanical Engineering Northwestern University Evanston IL 60208 USA Center for Developmental Therapeutics Northwestern University Evanston IL 60208 USA Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology Daejeon 34141 Republic of Korea Department of Civil and Environmental Engineering Northwestern University Evanston IL 60208 USA Chemistry Life Processes Institute Northwestern University Evanston IL 60208 USA Department of Biomedical Engineering Department of Neurological Surgery Department of Electrical Engineering and Computer Science Department of Mechanical Engineering and Department of Chemistry Simpson Querrey Institute for BioNanotechnology Northwestern University Evanston IL 60208 USA

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Charged iodide in chains behind the highly efficient iodine doping in carbon nanotubes

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Physical Review Materials 2017年第6期1卷 064002-064002页

作者： Ahmed Zubair Damien Tristant Chunyang Nie Dmitri E. Tsentalovich Robert J. Headrick Matteo Pasquali Junichiro Kono Vincent Meunier Emmanuel Flahaut Marc Monthioux Iann C. Gerber Pascal Puech Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Department of Physics Applied Physics and Astronomy Rensselaer Polytechnic Institute Troy New York 12180 USA CEMES-CNRS UPR-8011 Université Fédérale de Toulouse-Midi-Pyrénées Toulouse France LPCNO UMR-5215 CNRS INSA Université Fédérale de Toulouse-Midi-Pyrénées Toulouse France School of Environmental Science and Engineering Guangdong University of Technology Guangzhou 510006 P. R. China Department of Chemical and Biomolecular Engineering Rice University Houston Texas 77005 USA Department of Chemistry Rice University Houston Texas 77005 USA Department of Materials Science and NanoEngineering Rice University Houston Texas 77005 USA Department of Physics and Astronomy Rice University Houston Texas 77005 USA Institut Carnot CIRIMAT Université Fédérale de Toulouse-Midi-Pyrénées UMR CNRS 5085 Toulouse France

The origin of highly efficient iodine doping of carbon nanotubes is not well understood. Relying on first-principles calculations, we found that iodine molecules (I2) in contact with a carbon nanotube interact to form monoiodide or/and polyiodide from two and three I2 as a result of removing electrons from the carbon nanotube (p-type doping). Charge per iodine atom for monoiodide ion or iodine atom at end of iodine chain is significantly higher than that for I2. This atomic analysis extends previous studies showing that polyiodide ions are the dominant dopants. Moreover, we observed isolated I atoms in atomically resolved transmission electron microscopy, which proves the production of monoiodide. Finally, using Raman spectroscopy, we quantitatively determined the doping level and estimated the number of conducting channels in high electrical conductivity fibers composed of iodine-doped double-wall carbon nanotubes.

关键词： chemical bonding Dopants Electrical conductivity Optical phonons Physisorption

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