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Au⋅⋅⋅H−C Interactions Support a Robust Thermally Activated Delayed Fluorescence (TADF) Gold(I) Complex for OLEDs with Little Efficiency Roll-Off and Good Stability

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Angewandte Chemie 2022年第40期134卷

作者： Xingyu Feng Dr. Jian-Gong Yang Dr. Jingsheng Miao Dr. Cheng Zhong Dr. Xiaojun Yin Dr. Nengquan Li Chao Wu Qizheng Zhang Prof. Dr. Yong Chen Dr. Kai Li Prof. Dr. Chuluo Yang Shenzhen Key Laboratory of New Information Display and Storage Materials College of Materials Science and Engineering Shenzhen University Shenzhen 518055 P. R. China Contribution: Data curation (supporting) Investigation (equal) College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen 518060 P. R. China Contribution: Investigation (supporting) Department of Chemistry Hubei Key Lab on Organic and Polymeric Optoelectronic Materials Wuhan University Wuhan 430072 P. R. China Key Laboratory of Photochemical Conversion and Optoelectronic Materials & CAS-HKU Joint Laboratory on New Materials Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing 100190 P. R. China Contribution: Conceptualization (lead) Methodology (lead) Project administration (equal) Supervision (equal) Writing - original draft (lead) Writing - review & editing (equal) Contribution: Conceptualization (supporting) Project administration (equal) Supervision (equal) Writing - review & editing (equal)

The practical use of luminescent mononuclear gold(I) complexes as optoelectronic materials has been limited by their inferior stability. Herein we demonstrate a strategy to improve the stability of gold(I) complexes which display thermally activated delayed fluorescence (TADF). A highly rigid and groove-like σ-donating aryl ligand has been used to form dual Au⋅⋅⋅H−C hydrogen bonds. The secondary metal-ligand interactions have been authenticated by single-crystal structure, NMR spectroscopy and theoretical simulations. The TADF Au I complex exhibits appealing emission properties (photoluminescence quantum yield=76 %; delayed fluorescence lifetime=1.2 μs) and much improved thermal and photo-stability. Vacuum-deposited organic light-emitting diodes (OLEDs) show promising electroluminescence with a maximum external quantum efficiency (EQE) over 23 % and negligible efficiency roll-off even at 10 000 cd m −2 . An estimated LT 50 longer than 77 000 h with initial luminance of 100 cd m −2 reveals good operational stability. This work suggests a way for design of stable luminescent gold(I) complexes.

关键词： Gold(I) Complexes Luminescence OLED Secondary Interactions TADF

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data availability and requirements relevant for the Ariel space mission and other exoplanet atmosphere applications

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RAS Techniques and Instruments 2024年第1期3卷 636-690页

作者： Chubb, Katy L Robert, Severine Sousa-Silva, Clara Yurchenko, Sergei N Allard, Nicole F Boudon, Vincent Buldyreva, Jeanna Bultel, Benjamin Coustenis, Athena Foltynowicz, Aleksandra Gordon, Iouli E Hargreaves, Robert J Helling, Christiane Hill, Christian Hrodmarsson, Helgi Rafn Karman, Tijs Lecoq-Molinos, Helena Migliorini, Alessandra Rey, Michaël Richard, Cyril Sadiek, Ibrahim Schmidt, Frederic Sokolov, Andrei Stefani, Stefania Tennyson, Jonathan Venot, Olivia Wright, Sam O. M Arenales-Lope, Rosa Barstow, Joanna K Bocchieri, Andrea Carrasco, Nathalie Dubey, Dwaipayan Egorov, Oleg Muñoz, Antonio García Gharib-Nezhad, Ehsan Gkouvelis, Leonardos Grübel, Fabian Irwin, Patrick Gerard Joseph Knížek, Antonín Lewis, David A Lodge, Matt G Ma, Sushuang Martins, Zita Molaverdikhani, Karan Morello, Giuseppe Nikitin, Andrei Panek, Emilie Rengel, Miriam Rinaldi, Giovanna Skinner, Jack W Tinetti, Giovanna Van Kempen, Tim A Yang, Jingxuan Zingales, Tiziano HH Wills Physics Laboratory School of Physics University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Centre for Exoplanet Science University of St Andrews North Haugh St Andrews KY16 9SS United Kingdom Royal Belgian Institute for Space Aeronomy (BIRA-IASB) Av. Circulaire 3 Uccle B-1180 Belgium Bard College Institute of Astrophysics and Space Sciences 30 Campus Rd Annandale-On-Hudson 12504 NY United States Institute of Astrophysics and Space Sciences Rua das Estrelas Porto P-4150-762 Portugal Department of Physics and Astronomy University College London London WC1E 6BT United Kingdom Observatoire de Paris PSL Research University 61 Avenue de l'Observatoire Paris F-75014 France Lab. ICB UMR 6303 CNRS / Universite de Bourgogne 9 Av. A. Savary BP 47870 Cedex Dijon F-21078 France Institut UTINAM UMR CNRS 6213 Universite de Franche-Comte 16 Route de Gray Cedex Besançon F-25030 France GEOPS Universite Paris-Saclay CNRS Orsay F-91405 France LESIA Paris Observatory CNRS PSL Univ. 5 place Jules Janssen Cedex Meudon F-92190 France Department of Physics Umeå University Umeå SE-901 87 Sweden Center for Astrophysics | Harvard & Smithsonian 60 Garden Street Cambridge 02138 MA United States Space Research Institute Austrian Academy of Sciences Schmiedlstrasse 6 Graz A-8042 Austria TU Graz Fakultät für Mathematik Physik und Geodäsie Petersgasse 16 Graz A-8010 Austria Atomic and Molecular Data Unit International Atomic Energy Agency Wagramer Strasse 5 Vienna A-1400 Austria Universite Paris Cite and Univ Paris Est Creteil CNRS LISA Paris F-75013 France Institute for Molecules and Materials Radboud University Heyendaalseweg 135 Nijmegen NL-6525 AJ Netherlands Institute of Astronomy KU Leuven Celestijnenlaan 200D bus 2401 Leuven B-3001 Belgium IAPS-INAF Via Fosso del Cavaliere 100 Rome 00133 Italy Universite de Reims Champagne-Ardenne UMR CNRS 7331 BP 1039 Cedex 2 Reims F-51687 France Leibniz Institute for Plasma S

The goal of this white paper is to provide a snapshot of the data availability and data needs primarily for the Ariel space mission, but also for related atmospheric studies of exoplanets and cool stars. It covers the following data-related topics: molecular and atomic line lists, line profiles, computed cross-sections and opacities, collision-induced absorption and other continuum data, optical properties of aerosols and surfaces, atmospheric chemistry, UV photodissociation and photoabsorption cross-sections, and standards in the description and format of such data. These data aspects are discussed by addressing the following questions for each topic, based on the experience of the 'data-provider' and 'data-user' communities: (1) what are the types and sources of currently available data, (2) what work is currently in progress, and (3) what are the current and anticipated data needs. We present a GitHub platform for Ariel-related data, with the goal to provide a go-to place for both data-users and data-providers, for the users to make requests for their data needs and for the data-providers to link to their available data. Our aim throughout the paper is to provide practical information on existing sources of data whether in data bases, theoretical, or literature sources. © 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.

关键词： Algorithms Ariel data Methods data needs Numerical methods

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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Distributed Power Allocation for UAV-Assisted Multi-User Relay Networks

Distributed Power Allocation for UAV-Assisted Multi-User Rel...

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International Symposium on Communication Systems, Networks and Digital Signal Processing, CNSDSP

作者： Jie Gong Jingjing Lin Tsung-Hui Chang Chao Shen Xiang Chen School of Data and Computer Science & SEIT Sun Yat-Sen University Guangzhou China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

In this paper, we consider a multi-user relay system where an unmanned aerial vehicle (UAV) serves as a relay to transfer data from sources to destinations. The UAV cruises over the ground users and is capable of buffering users' data so that uplink and downlink can be scheduled independently. A throughput maximization problem is formulated, and a distributed power allocation algorithm is proposed based on alternating direction method of multipliers (ADMM) to reduce the computational task and energy consumption of the UAV. Numerical results show that the proposed distributed algorithm converges to the centralized optimal one with a few number of iterations, and outperforms the uniform power allocation algorithm.

关键词： Relays Resource management Energy consumption Unmanned aerial vehicles Downlink Trajectory Uplink

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Tools and Benchmarks for Automated Log Parsing

arXiv

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arXiv 2018年

作者： Zhu, Jieming He, Shilin Liu, Jinyang He, Pinjia Xie, Qi Zheng, Zibin Lyu, Michael R. Huawei Noah's Ark Lab Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong School of Data and Computer Science Sun Yat-Sen University Guangzhou China Department of Computer Science ETH Zurich Switzerland School of Computer Science and Technology Southwest Minzu University Chengdu China

Logs are imperative in the development and maintenance process of many software systems. They record detailed runtime information that allows developers and support engineers to monitor their systems and dissect anomalous behaviors and errors. The increasing scale and complexity of modern software systems, however, make the volume of logs explodes. In many cases, the traditional way of manual log inspection becomes impractical. Many recent studies, as well as industrial tools, resort to powerful text search and machine learning-based analytics solutions. Due to the unstructured nature of logs, a first crucial step is to parse log messages into structured data for subsequent analysis. In recent years, automated log parsing has been widely studied in both academia and industry, producing a series of log parsers by different techniques. To better understand the characteristics of these log parsers, in this paper, we present a comprehensive evaluation study on automated log parsing and further release the tools and benchmarks for easy reuse. More specifically, we evaluate 13 log parsers on a total of 16 log datasets spanning distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. We report the benchmarking results in terms of accuracy, robustness, and efficiency, which are of practical importance when deploying automated log parsing in production. We also share the success stories and lessons learned in an industrial application at Huawei. We believe that our work could serve as the basis and provide valuable guidance to future research and deployment of automated log parsing. Copyright © 2018, The Authors. All rights reserved.

关键词： Anomaly detection

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Energy efficient simulation computing of electrocardiogram with TK1 Board

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2018年第1期45卷 177-187页

作者： Shen, Wenfeng Qiu, Feng Hu, Yaopeng Shen, Yanghua Zhu, Xin School of Computer Engineering Science Shanghai Institute for Advanced Communication and Data Science Shanghai University Nanchen Road Shanghai200444 China Department of Physiology School of Medicine Fukuoka University Fukuoka Fukuoka814-0180 Japan Biomedical Information Technology Lab The University of Aizu Aizu-Wakamatsu Fukushima965-8580 Japan

The computer simulation in cardiac models has become an increasingly powerful tool in the study of cardiac electrophysiology. To precise analysis the electrophysiological mechanism, it not only demands a significant computing capacity due to its heavy workload, but also has to be available and convenient in many medical experiments, which seems to be contradictory in a conventional computer perspective. However, with the emergence of new heterogeneous computing platforms, a surging number of scientific applications are dramatically improving upon the energy efficiency of existing solutions. Under such circumstances, this paper presents a metric method for energy efficiency and several optimizing strategies, to fully exploit the energy efficiency of applications. To verify these approaches, we describe a new experience adapting the computer simulation of electrocardiogram(ECG) based on a whole-heart model for a Jetson Tegra K1(TK1) System on Chip(SoC) board, which was previously only possible on desktop-level platforms. The experiments are conducted to evaluate the performance and power draw in different situations on different platforms. In order to investigate the characteristics of unified memory in TK1 board, an extra experiment was provided. The experimental results show that our proposed methods combining with the new hardware can lead to a encouraging consequence.

关键词： Electrocardiograms

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Roadmap on multimode light shaping

arXiv

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arXiv 2021年

作者： Piccardo, Marco Ginis, Vincent Forbes, Andrew Mahler, Simon Friesem, Asher A. Davidson, Nir Ren, Haoran Dorrah, Ahmed H. Capasso, Federico Dullo, Firehun T. Ahluwalia, Balpreet S. Ambrosio, Antonio Gigan, Sylvain Treps, Nicolas Hiekkamäki, Markus Fickler, Robert Kues, Michael Moss, David Morandotti, Roberto Riemensberger, Johann Kippenberg, Tobias J. Faist, Jérôme Scalari, Giacomo Picqué, Nathalie Hänsch, Theodor W. Cerullo, Giulio Manzoni, Cristian Lugiato, Luigi A. Brambilla, Massimo Columbo, Lorenzo Gatti, Alessandra Prati, Franco Shiri, Abbas Abouraddy, Ayman F. Alù, Andrea Galiffi, Emanuele Pendry, J.B. Huidobro, Paloma A. Center for Nano Science and Technology Fondazione Istituto Italiano di Tecnologia Milano Italy Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Data Lab/Applied Physics Vrije Universiteit Brussel Belgium School of Physics University of the Witwatersrand Johannesburg South Africa Department of Physics of Complex Systems Weizmann Institute of Science Rehovot Israel MQ Photonics Research Centre Department of Physics and Astronomy Macquarie University Macquarie ParkNSW Australia Department of Physics and Technology UiT-The Arctic University of Norway Tromsø Norway Laboratoire Kastler Brossel ENS-PSL Research University CNRS Sorbonne Université Collège de France Paris France Tampere University Photonics Laboratory Physics Unit Tampere Finland Institute of Photonics Leibniz University Hannover Germany Optical Sciences Centre Swinburne University of Technology Australia Energie Materiaux Telecommunications Institut National de la Recherche Scientifique Canada Lausanne Switzerland Institute for Quantum Electronics ETH Zurich Zürich Switzerland Max-Planck Institute of Quantum Optics Garching Germany Istituto di Fotonica e Nanotecnologia-CNR Dipartimento di Fisica Politecnico di Milano Milano Italy Dipartimento di Scienza e Alta Tecnologia Università dell'Insubria Como Italy Dipartimento di Fisica Interateneo and CNR-IFN Università e Politecnico di Bari Bari Italy Dipartimento di Elettronica e Telecomunicazioni Politecnico di Torino Torino Italy Istituto di Fotonica e Nanotecnologie IFN-CNR Milano Italy CREOL The College of Optics & Photonics University of Central Florida OrlandoFL United States Department of Electrical and Computer Engineering University of Central Florida OrlandoFL United States Advanced Science Research Center City University of New York United States The Blackett Laboratory Imperial College London London United Kingdom Instituto de Telecomunicações IST-University o

Our ability to generate new distributions of light has been remarkably enhanced in recent years. At the most fundamental level, these light patterns are obtained by ingeniously combining different electromagnetic modes. Interestingly, the modal superposition occurs in the spatial, temporal as well as spatio-temporal domain. This generalized concept of structured light is being applied across the entire spectrum of optics: generating classical and quantum states of light, harnessing linear and nonlinear light-matter interactions, and advancing applications in microscopy, spectroscopy, holography, communication, and synchronization. This Roadmap highlights the common roots of these different techniques and thus establishes links between research areas that complement each other seamlessly. We provide an overview of all these areas, their backgrounds, current research, and future developments. We highlight the power of multimodal light manipulation and want to inspire new eclectic approaches in this vibrant research community. Copyright © 2021, The Authors. All rights reserved.

关键词： Light

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Signed network modeling based on structural balance theory

arXiv

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arXiv 2017年

作者： Derr, Tyler Aggarwal, Charu Tang, Jiliang Data Science and Engineering Lab Michigan State University IBM T.J. Watson Research Center Data Science and Engineering Lab Michigan State University

The modeling of networks, specifically generative models, has been shown to provide a plethora of information about the underlying network structures, as well as many other benefits behind their construction. There has been a considerable increase in interest for the better understanding and modeling of networks, and the vast majority of existing work has been for unsigned networks. However, many networks can have positive and negative links (or signed networks), especially in online social media. It is evident from recent work that signed networks present unique properties and principles from unsigned networks due to the added complexity, which pose tremendous challenges on existing unsigned network models. Hence, in this paper, we investigate the problem of modeling signed networks. In particular,we provide a principled approach to capture important properties and principles of signed networks and propose a novel signed network model guided by Structural Balance Theory. Empirical experiments on three real-world signed networks demonstrate the effectiveness of the proposed model. Copyright © 2017, The Authors. All rights reserved.

关键词： Social networking (online)

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LogCanvas: Visualizing search history using knowledge graphs

arXiv

引用

arXiv 2018年

作者： Xu, Luyan Zhou, Xuan Fernando, Zeon Trevor Nejdl, Wolfgang DEKE lab Renmin University of China Beijing China School of Data Science & Engineering East China Normal University Shanghai China L3S Research Center Leibniz Universität Hannover Hannover Germany

In this demo paper, we introduce LogCanvas, a platform for user search history visualization. Different from the existing visualization tools, LogCanvas focuses on helping users re-construct the semantic relationship among their search activities. LogCanvas segments a user's search history into different sessions and generates a knowledge graph to represent the information exploration process in each session. A knowledge graph is composed of the most important concepts or entities discovered by each search query as well as their relationships. It thus captures the semantic relationship among the queries. LogCanvas offers a session timeline viewer and a snippets viewer to enable users to re-find their previous search results efficiently. LogCanvas also provides a collaborative perspective to support a group of users in sharing search results and experience. Copyright © 2018, The Authors. All rights reserved.

关键词： Visualization

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Universal and generalizable restoration strategies for degraded ecological networks

arXiv

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arXiv 2018年

作者： Bhatia, Udit Gouhier, Tarik C. Ganguly, Auroop R. Sustainability and Data Sciences Lab Department of Civil & Environmental Engineering Northeastern University BostonMA02115 United States Department of Marine and Environmental Sciences Marine Science Center Northeastern University NahantMA01908 United States

Humans are increasingly stressing ecosystems via habitat destruction, climate change and global population movements leading to the widespread loss of biodiversity and the disruption of key ecological services1–3. Ecosystems characterized primarily by mutualistic relationships between species such as plant-pollinator interactions may be particularly vulnerable to such perturbations because the loss of biodiversity can cause extinction cascades that can compromise the entire network4,5. Here, we develop a general restoration strategy based on network-science for degraded ecosystems. Specifically, we show that network topology can be used to identify the optimal sequence of species reintroductions needed to maximize biodiversity gains following partial and full ecosystem collapse6,7. This restoration strategy generalizes across topologically-disparate and geographically-distributed ecosystems. Additionally, we find that although higher connectance and diversity promote persistence in pristine ecosystems8, these attributes reduce the effectiveness of restoration efforts in degraded networks. Hence, focusing on restoring the factors that promote persistence in pristine ecosystems may yield suboptimal recovery strategies for degraded ecosystems. Overall, our results have important insights for designing effective ecosystem restoration strategies to preserve biodiversity and ensure the delivery of critical natural services that fuel economic development, food security and human health around the globe9,10. Copyright © 2018, The Authors. All rights reserved.

关键词： Biodiversity

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