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作者： Demaine, Erik D. Eppstein, David Hesterberg, Adam Ito, Hiro Lubiw, Anna Uehara, Ryuhei Uno, Yushi Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge United States Computer Science Department University of California Irvine United States Department of Mathematics Massachusetts Institute of Technology United States School of Informatics and Engineering University of Electro-Communications Tokyo Japan David R. Cheriton School of Computer Science University of Waterloo ON Canada School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Japan Graduate School of Science Osaka Prefecture University Osaka Japan

In this paper, we study how to fold a specified origami crease pattern in order to minimize the impact of paper thickness. Specifically, origami designs are often expressed by a mountain-valley pattern (plane graph of creases with relative fold orientations), but in general this specification is consistent with exponentially many possible folded states. We analyze the complexity of finding the best consistent folded state according to two metrics: minimizing the total number of layers in the folded state (so that a "flat folding" is indeed close to flat), and minimizing the total amount of paper required to execute the folding (where "thicker" creases consume more paper). We prove both problems strongly NP-complete even for 1D folding. On the other hand, we prove both problems fixed-parameter tractable in 1D with respect to the number of layers. © 2015 Elsevier B.V.

关键词： Computational methods

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A DUAL-FUNCTION MIMO RADAR-COMMUNICATIONS SYSTEM USING PSK MODULATION

A DUAL-FUNCTION MIMO RADAR-COMMUNICATIONS SYSTEM USING PSK M...

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European Signal Processing Conference

作者： Aboulnasr Hassanien Moeness G. Amin Yimin D. Zhang Braham Himed Center for Advanced Communications Villanova University Dept. Electrical and Computer Engineering Temple University RF Technology Branch Air Force Research Lab (AFRL/RYMD)

ISBN: (纸本)9781509018918

In this paper, we develop a new technique for information embedding into the emission of multiple-input multiple-output (MIMO) radar using dual-functionality platforms. A set of orthogonal waveforms occupying the same band is used to implement the primary MIMO radar operation. The secondary communication function is implemented by embedding one phase-shift keying (PSK) communication symbol in each orthogonal waveform, i.e., the number of embedded communication symbols during each radar pulse equals the number of transmit antennas. We show that the communication operation is transparent to the MIMO radar operation. The communication receiver detects the embedded PSK symbols using standard ratio testing. The achievable data rate is proportional to the pulse repetition frequency, the number of transmit elements, and the size of the PSK constellation. The performance of the proposed technique is investigated in terms of the symbol error rate. Simulations examples demonstrate that data rates in the range of several Mbps can be embedded and reliably detected.

关键词： Data transfer rates Phase shift keying PS-K MIMO radar Radar pulses communications receivers embedding PULSE REPETITION RATE multiple input multiple output systems transmitting antennas

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MoS2 heterostructure with tunable phase stability: Strain induced interlayer covalent bond formation

arXiv

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arXiv 2017年

作者： Ouyang, Bin Xiong, Shiyun Yang, Zhi Jing, Yuhang Wang, Yongjie National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou JiangSu215123 China College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan ShanXi030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province Taiyuan University of Technology Taiyuan ShanXi030024 China Department of Astronautical Science and Mechanics Harbin Institute of Technology Harbin Heilongjiang150001 China Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Electrical Engineering and Computer Engineering University of Michigan Ann ArborMI48109 United States

Due to the distinguished properties offered by different structural phases of monolayer MoS2, phase engineering design are urgently required for achieving switchable structural phase. Strain engineering is widely accepted as a clean and flexible method, however, can not be achieved in engineering monolayer MoS2 phase transition because the critical biaxial strain required (~15%) is much larger than measured elastic limit (~11%). In this study, employing density functional theoretical calculations, it has been found out that with the forming of heterostructure between MoS2 with buckled 2D materials such as silicence, germanene and stanene, only a small strain can trigger the phase transition. As being suggested by the constructed phase stability diagram, biaxial deformation as low as 3% in MoS2/silicene and MoS2/stanene sandwich structure, would be sufficient to induce the structural phase transition in MoS2 lattice. This strain falls well within experimental elastic limit, thus would be feasible to realize in experiment. The origin of such behavior can be understood as strain induced interlayer covalent bond formation, which finally make MoS2 lattice more sensitive to external strain. This theoretical work provides one realistic route for achieving flexible phase stabilities in experimental design. Copyright © 2017, The Authors. All rights reserved.

关键词： Molybdenum compounds

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

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Scientific data 2021年第1期8卷 242页

作者： Julien Cohen-Adad Eva Alonso-Ortiz Mihael Abramovic Carina Arneitz Nicole Atcheson Laura Barlow Robert L Barry Markus Barth Marco Battiston Christian Büchel Matthew Budde Virginie Callot Anna J E Combes Benjamin De Leener Maxime Descoteaux Paulo Loureiro de Sousa Marek Dostál Julien Doyon Adam Dvorak Falk Eippert Karla R Epperson Kevin S Epperson Patrick Freund Jürgen Finsterbusch Alexandru Foias Michela Fratini Issei Fukunaga Claudia A M Gandini Wheeler-Kingshott Giancarlo Germani Guillaume Gilbert Federico Giove Charley Gros Francesco Grussu Akifumi Hagiwara Pierre-Gilles Henry Tomáš Horák Masaaki Hori James Joers Kouhei Kamiya Haleh Karbasforoushan Miloš Keřkovský Ali Khatibi Joo-Won Kim Nawal Kinany Hagen H Kitzler Shannon Kolind Yazhuo Kong Petr Kudlička Paul Kuntke Nyoman D Kurniawan Slawomir Kusmia René labounek Maria Marcella Laganà Cornelia Laule Christine S Law Christophe Lenglet Tobias Leutritz Yaou Liu Sara Llufriu Sean Mackey Eloy Martinez-Heras Loan Mattera Igor Nestrasil Kristin P O'Grady Nico Papinutto Daniel Papp Deborah Pareto Todd B Parrish Anna Pichiecchio Ferran Prados Àlex Rovira Marc J Ruitenberg Rebecca S Samson Giovanni Savini Maryam Seif Alan C Seifert Alex K Smith Seth A Smith Zachary A Smith Elisabeth Solana Y Suzuki George Tackley Alexandra Tinnermann Jan Valošek Dimitri Van De Ville Marios C Yiannakas Kenneth A Weber Ii Nikolaus Weiskopf Richard G Wise Patrik O Wyss Junqian Xu NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. jcohen@polymtl.ca. Functional Neuroimaging Unit CRIUGM Université de Montréal Montreal QC Canada. jcohen@polymtl.ca. Mila - Quebec AI Institute Montreal QC Canada. jcohen@polymtl.ca. NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal QC Canada. Department of Radiology Swiss Paraplegic Centre Nottwil Switzerland. Centre for Advanced Imaging The University of Queensland Brisbane Australia. Department of Radiology University of British Columbia Vancouver BC Canada. Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Charlestown MA USA. Department of Radiology Harvard Medical School Boston MA USA. Harvard-Massachusetts Institute of Technology Health Sciences & Technology Cambridge MA USA. School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia. NMR Research Unit Queen Square MS Centre UCL Queen Square Institute of Neurology Faculty of Brain Sciences University College London London UK. Department of Systems Neuroscience University Medical Center Hamburg-Eppendorf Hamburg Germany. Department of Neurosurgery Medical College of Wisconsin Milwaukee WI USA. Aix-Marseille Univ CNRS CRMBM Marseille France. APHM Hopital Universitaire Timone CEMEREM Marseille France. Vanderbilt University Institute of Imaging Science Vanderbilt University Medical Center Nashville TN USA. Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada. CHU Sainte-Justine Research Centre Montreal QC Canada. Centre de Recherche CHUS CIMS Sherbrooke Canada. Sherbrooke Connectivity Imaging Lab (SCIL) Computer Science department Université de Sherbrooke Sherbrooke Canada. Université de Strasbourg CNRS ICube Strasbourg France. UHB - University Hospital Brno and Masaryk University Department of Radiology and Nuclear

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A New Hopfield-type Neural Network Approach to Multi-Goal Vehicle Navigation in Unknown Environments

A New Hopfield-type Neural Network Approach to Multi-Goal Ve...

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2016年IEEE汽车电子与安全国际会议

作者： Chaomin Luo Jing Zhang Gene Eu Jan Hongwei Mo the Advanced Mobility Lab the Department of Electrical and Computer EngineeringUniversity of Detroit Mercy the Beijing University of Chemical Technology the Graduate Institute of Animation and Film Art National University of the Arts Department of Electrical Engineering National Taipei University the Harbin Engineering University

A Hopfield-type neural networks(HNN) algorithm associated with histogram navigation method is proposed in this paper for real-time map building and path planning for multiple goals *** real world applications such as rescue robots,service robots,mining mobile robots,and mine searching robots,etc.,an autonomous vehicle needs to reach multiple goals with a shortest path that,in this paper,is capable of being implemented by a HNN method with minimized overall *** a global trajectory is planned,a foraging-enabled trail is created to guide the vehicle to the multiple goals.A histogram-based local navigation algorithm is employed to plan a collision-free path along the trail planned by the global path planner.A re-planning-based algorithm aims to generate trajectory while an autonomous vehicle explores through a terrain with map building in unknown *** this paper,simulation and experimental results demonstrate that the real-time concurrent mapping and multi-goal navigation of an autonomous vehicle is successfully performed under unknown environments.

关键词： Hopfield-type neural networks Multi-goal motion planning Autonomous vehicle Mapping Unknown Environments

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Making advanced scientific algorithms and big scientific data management more accessible 14

Making advanced scientific algorithms and big scientific dat...

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Computational Imaging XIV 2016

作者： Venkatakrishnan, S.V. Mohan, K. Aditya Beattie, Keith Correa, Joaquin Dart, Eli Deslippe, Jack R. Hexemer, Alexander Krishnan, Harinarayan MacDowell, Alastair A. Marchesini, Stefano Patton, Simon J. Perciano, Talita Sethian, James A. Stromsness, Rune Tierney, Brian L. Tull, Craig E. Ushizima, Daniela Parkinson, Dilworth Y. Center for Advanced Mathematics for Energy Research Applications Lawrence Berkeley National Lab BerkeleyCA94720 United States Advanced Light Source Lawrence Berkeley National Lab BerkeleyCA94720 United States School of Electrical and Computer Engineering Purdue University PurdueIN47907 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States National Energy Research Scientific Computing Center BerkeleyCA94720 United States Energy Sciences Network BerkeleyCA94720 United States Department of Mathematics UC Berkeley BerkeleyCA94720 United States

Synchrotrons such as the advanced Light Source (ALS) at Lawrence Berkeley National laboratory are user facilities - they are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography). © 2016 Society for Imaging Science and Technology.

关键词： Tomography

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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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13-3: Top-emitting Quantum-dot Light-emitting Diodes with all the p-i-n Functional Layers Deposited by Solution Processes

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SID Symposium Digest of Technical Papers 2017年第1期48卷

作者： Yibin Jiang Shuming Chen Hoi-Sing Kwok State Key Lab on Advanced Displays and Optoelectronics Department of Electronic and Computer Engineering The Hong Kong University of Science & Technology Clear Water Bay Kowloon Hong Kong Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen 518055 PR China

A green top-emitting quantum-dot light-emitting diode (TQLED) with conventional structure is demonstrated in this study with all its functional layers, including hole transportation layer, emitting layer and electron transportation layer, fabricated by spin-coating processes. This green QLED exhibits an external quantum efficiency (EQE) of 6.4 % and a maximum brightness of 200000 cd/m 2 , which is the highest values reported for green TQLEDs in literature. Besides, this is the first report of all-solution-processed TQLEDs.

关键词： quantum-dot light-emitting diodes top-emitting spin-coating all-solution-processed

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