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Information Needs for Opioid Use Disorder Treatment Using Buprenorphine Product: Qualitative Analysis of Suboxone-Focused Reddit Data

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Journal of medical Internet research 2025年 27卷 e68886页

作者： Madhusudan Basak Omar Sharif Sarah E Lord Jacob T Borodovsky Lisa A Marsch Sandra A Springer Edward V Nunes Charles D Brackett Luke J Archibald Sarah M Preum Department of Computer Science Dartmouth College Hanover NH United States. Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh. Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College Lebanon NH United States. Department of Biomedical Data Science Geisel School of Medicine Dartmouth College Lebanon NH United States. Department of Psychiatry Dartmouth Health Lebanon NH United States. Department of Psychiatry Geisel School of Medicine Dartmouth College Lebanon NH United States. Section of Infectious Disease Department of Internal Medicine Yale School of Medicine New Haven CT United States. Center for Interdisciplinary Research on AIDS Yale University School of Public Health New Haven CT United States. Section of Infectious Disease Department of Internal Medicine Veterans Administration Connecticut Healthcare System West Haven CT United States. Department of Psychiatry Division on Substance Use Columbia University Irving Medical Center and New York State Psychiatric Institute New York NH United States. Department of Medicine Geisel School of Medicine Dartmouth College Hanover NH United States. Department of Internal Medicine Dartmouth Hitchcock Medical Center Lebanon NH United States.

BACKGROUND:Buprenorphine is a Food and Drug Administration-approved medication for opioid use disorder. However, individuals with opioid use disorder often report information needs regarding buprenorphine treatment on social media platforms such as Reddit. The field lacks a systematic approach to organizing these data and characterizing treatment information needs that may be unique and unavailable elsewhere. OBJECTIVE:In this study, we curated and analyzed large-scale data from social media to characterize self-reported buprenorphine treatment information needs using a qualitative analysis. METHODS:We collected 15,253 Reddit posts from the subreddit r/Suboxone. Following a standard protocol and guidance from clinical experts, we first identified 5 main themes from the data and then manually coded 6000 posts based on these themes. Finally, we determined the most frequently appearing topics within each theme by analyzing samples from each group. RESULTS:Among the 6000 posts, 2417 (40.3%) contained a single theme, 2160 (36%) contained two themes, and 834 (13.9%) contained three themes. The most frequent topics found across these themes included reports of psychological and physical effects during recovery, complexities in accessing buprenorphine, and significant information gaps regarding medication administration, tapering, and substance use at different stages of recovery. Moreover, self-treatment strategies and peer-driven advice revealed potential rumors and misinformation. CONCLUSIONS:Our findings can guide the design of interventions to improve patient education and communication. They also help address knowledge gaps and misinformation related to treatment. Additionally, the results can support hypothesis generation for future clinical research on medication for opioid use disorder treatment.

关键词： MOUD Reddit access buprenorphine medication for opioid use disorder opioid use disorder patient experience social media substance use tapering treatment information needs

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Unveiling the Protonation Kinetics-Dependent Selectivity in Nitrogen Electroreduction: Achieving 75.05 % Selectivity

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Angewandte Chemie 2022年第50期134卷

作者： Dr. Yang Liu Lingling Wang Lin Chen Hongdan Wang Dr. Amol R. Jadhav Taehun Yang Yixuan Wang Dr. Jinqiang Zhang Ashwani Kumar Jinsun Lee Dr. Viet Q. Bui Prof. Min Gyu Kim Prof. Hyoyoung Lee Center for Integrated Nanostructure Physics Institute for Basic Science (IBS) Sungkyunkwan University Suwon 16419 Republic of Korea Department of Chemistry Sungkyunkwan University Suwon 16419 Republic of Korea State Key Laboratory of Fine Chemicals Liaoning Key Lab for Energy Materials and Chemical Engineering School of Chemical Engineering Dalian University of Technology Dalian 116024 China School of Chemical Engineering and Advanced Materials The University of Adelaide Adelaide SA 5005 Australia Advanced Institute of Science and Technology The University of Danang 41 Le Duan Danang Vietnam Beamline Research Division Pohang Accelerator Laboratory (PAL) Pohang University of Science and Technology Pohang 37673 Republic of Korea Creative Research Institute Sungkyunkwan University Suwon 16419 Republic of Korea Department of Biophysics Sungkyunkwan University Suwon 16419 Republic of Korea

While higher selectivity of nitrogen reduction reaction (NRR) to ammonia (NH 3 ) is always achieved in alkali, the selectivity dependence on nitrogen (N 2 ) protonation and mechanisms therein are unrevealed. Herein, we profile how the NRR selectivity theoretically relies upon the first protonation that is collectively regulated by proton (H) abundance and adsorption-desorption, along with intermediate-*NNH formation. By incorporating electronic metal modulators (M=Co, Ni, Cu, Zn) in nitrogenase-imitated model-iron polysulfide (FeSx), a series of FeMSx catalysts with tailorable protonation kinetics are obtained. The key intermediates behaviors traced by in situ FT-IR and Raman spectroscopy and operando electrochemical impedance spectroscopy demonstrate the strong protonation kinetics-dependent selectivity that mathematically follows a log-linear Bradley curve. Strikingly, FeCuSx exhibits a record-high selectivity of 75.05 % at −0.1 V (vs. RHE) for NH 3 production in 0.1 M KOH electrolyte.

关键词： Adjustable Protonation Kinetics Electronic Modulation Nitrogen Electroreduction Operando Electrochemical Impedance Spectroscopy Simulations Protonation-Selectivity Relation

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Strong correlations and orbital texture in single-layer 1T-TaSe₂ (vol 16, pg 218, 2020)

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NATURE PHYSICS 2021年第7期17卷 867-867页

作者： Chen, Yi Ruan, Wei Wu, Meng Tang, Shujie Ryu, Hyejin Tsai, Hsin-Zon Lee, Ryan L. Kahn, Salman Liou, Franklin Jia, Caihong Albertini, Oliver R. Xiong, Hongyu Jia, Tao Liu, Zhi Sobota, Jonathan A. Liu, Amy Y. Moore, Joel E. Shen, Zhi-Xun Louie, Steven G. Mo, Sung-Kwan Crommie, Michael F. Department of Physics University of California Berkeley CA USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley CA USA International Collaborative Laboratory of 2D Materials for Optoelectronic Science & Technology of Ministry of Education Engineering Technology Research Center for 2D Material Information Function Devices and Systems of Guangdong Province Shenzhen University Shenzhen China Henan Key Laboratory of Photovoltaic Materials and Laboratory of Low-dimensional Materials Science School of Physics and Electronics Henan University Kaifeng China Kavli Energy Nano Sciences Institute at the University of California Berkeley and the Lawrence Berkeley National Laboratory Berkeley CA USA Stanford Institute for Materials and Energy Sciences SLAC National Accelerator Laboratory and Stanford University Menlo Park CA USA Geballe Laboratory for Advanced Materials Departments of Physics and Applied Physics Stanford University Stanford CA USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley CA USA CAS Center for Excellence in Superconducting Electronics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Physical Science and Technology Shanghai Tech University Shanghai China Center for Spintronics Korea Institute of Science and Technology Seoul South Korea Department of Physics Georgetown University Washington DC USA

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Emergent Dimer-Model Topological Order and Quasiparticle Excitations in Liquid Crystals: Combinatorial Vortex Lattices

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Physical Review X 2025年第2期15卷 021084-021084页

作者： Cuiling Meng Jin-Sheng Wu Žiga Kos Jörn Dunkel Cristiano Nisoli Ivan I. Smalyukh Department of Physics and Chemical Physics Program University of Colorado Boulder Colorado 80309 USA International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM) Hiroshima University Higashihiroshima Hiroshima Japan Department of Mathematics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia Department of Condensed Matter Physics Jožef Stefan Institute Ljubljana Slovenia Theoretical Division Physics of Condensed Matter and Complex Systems and Center For Nonlinear Studies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado Boulder Colorado 80309 USA Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado Boulder Colorado 80309 USA

Liquid crystals have proven to provide a versatile experimental and theoretical platform for studying topological objects such as vortices, skyrmions, and hopfions. In parallel, in hard condensed matter physics, the concept of topological phases and topological order has been introduced in the context of spin liquids to investigate emergent phenomena like quantum Hall effects and high-temperature superconductivity. Here, we bridge these two seemingly disparate perspectives on topology in physics. Combining experiments and simulations, we show how topological defects in liquid crystals can be used as versatile building blocks to create complex, highly degenerate topological phases, which we refer to as “combinatorial vortex lattices” (CVLs). CVLs exhibit extensive residual entropy and support locally stable quasiparticle excitations in the form of charge-conserving topological monopoles, which can act as mobile information carriers and be linked via Dirac strings. CVLs can be rewritten and reconfigured on demand, endowed with various symmetries, and modified through laser-induced topological surgery—an essential capability for information storage and retrieval. We demonstrate experimentally the realization, stability, and precise optical manipulation of CVLs, thus opening new avenues for understanding and technologically exploiting higher-hierarchy topology in liquid crystals and other ordered media.

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2022 Review of Data-Driven Plasma science

arXiv

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arXiv 2022年

作者： Anirudh, Rushil Archibald, Rick Asif, M. Salman Becker, Markus M. Benkadda, Sadruddin Bremer, Peer-Timo Budé, Rick H.S. Chang, C.S. Chen, Lei Churchill, R.M. Citrin, Jonathan Gaffney, Jim A. Gainaru, Ana Gekelman, Walter Gibbs, Tom Hamaguchi, Satoshi Hill, Christian Humbird, Kelli Jalas, Sören Kawaguchi, Satoru Kim, Gon-Ho Kirchen, Manuel Klasky, Scott Kline, John L. Krushelnick, Karl Kustowski, Bogdan Lapenta, Giovanni Li, Wenting Ma, Tammy Mason, Nigel J. Mesbah, Ali Michoski, Craig Munson, Todd Murakami, Izumi Najm, Habib N. Erik J. Olofsson, K. Park, Seolhye Peterson, J. Luc Probst, Michael Pugmire, David Sammuli, Brian Sawlani, Kapil Scheinker, Alexander Schissel, David P. Shalloo, Rob J. Shinagawa, Jun Seong, Jaegu Spears, Brian K. Tennyson, Jonathan Thiagarajan, Jayaraman Ticoş, Catalin M. Trieschmann, Jan van Dijk, Jan Van Essen, Brian Ventzek, Peter Wang, Haimin Wang, Jason T.L. Wang, Zhehui Wende, Kristian Xu, Xueqiao Yamada, Hiroshi Yokoyama, Tatsuya Zhang, Xinhua Lawrence Livermore National Laboratory LivermoreCA94550 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States University of California Riverside RiversideCA92521 United States Felix-Hausdorff-Str. 2 Greifswald17489 Germany Aix Marseille University CNRS PIIM UMR 7345 Marseille France Department of Applied Physics Eindhoven University of Technology Netherlands Princeton Plasma Physics Laboratory Princeton NJ08540 United States Institute of Ion Physics and Applied Physics University of Innsbruck Technikerstrasse 25 Innsbruck6020 Austria DIFFER - Dutch Institute for Fundamental Energy Research Eindhoven Netherlands Science and Technology of Nuclear Fusion Group Eindhoven University of Technology Eindhoven Netherlands Department of Physics and Astronomy University of California Los AngelesCA90095 United States NVIDIA 2788 San Tomas Expressway Santa ClaraCA95051 United States Center for Atomic and Molecular Technologies Graduate School of Engineering Osaka University 2-1 Yamadaoka Suita Osaka Japan Department of Nuclear Sciences and Applications International Atomic Energy Agency Wagramer Strasse 5 PO Box 100 Vienna1400 Austria Center for Free-Electron Laser Science Department of Physics Universität Hamburg Luruper Chaussee 149 Hamburg22761 Germany Division of Information and Electronic Engineering Graduate School of Engineering Muroran Institute of Technology Hokkaido Muroran050-8585 Japan Seoul National University Seoul151-741 Korea Republic of Deutsches Elektronen-Synchrotron DESY Notkestr. 85 Hamburg22607 Germany Los Alamos National Laboratory Los AlamosNM87545 United States Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States Department of Mathematics KULeuven University of Leuven Belgium Department of Physical Sciences The University of Kent CanterburyCT2 7NH United Kingdom Department of Chemical and Biomolecular Engineering University of California Berk

Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasma-processing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Uav and lidar image registration: A surf-based approach for ground control points selection 4

Uav and lidar image registration: A surf-based approach for ...

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4th ISPRS Geospatial Week 2019

作者： Kalantar, B. Ueda, N. Al-Najjar, H.A.H. Moayedi, H. Halin, A.A. Mansor, S. RIKEN Center for Advanced Intelligence Project Goal-Oriented Technology Research Group Disaster Resilience Science Team Tokyo103-0027 Japan Faculty of Engineering and IT University of Technology Sydney NSW2007 Australia Department for Management of Science and Technology Development Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Dept. of Multimedia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor43400 Malaysia Dept. of Civil Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Selangor43400 Malaysia

Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results. © Authors 2019.

关键词： Unmanned aerial vehicles (UAV)

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ANDROIDOFF:Offloading android application based on cost estimation

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Journal of Systems and Software 2019年 158卷 110418-000页

作者： Chen, Xing Chen, Jiaqing Liu, Bichun Ma, Yun Zhang, Ying Zhong, Hao College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China School of Software Tsinghua University Beijing100084 China National Engineering Research Center of Software Engineering Peking University China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Computation offloading is a promising way of improving the performance and reducing the battery power consumption, since it moves some time-consuming computation activities to nearby servers. Although various approaches have been proposed to support computation offloading, we argue that there is still sufficient space for improvements, since existing approaches cannot accurately estimate the execution costs. As a result, we find that their offloading plans are less optimized. To handle the problem, in this paper, given an Android application, we propose a novel approach, called ANDROIDOFF, that supports offloading at the granularity of objects. Supporting such capability is challenging due to the two reasons: (1) through dynamic execution, it is feasible to collect the execution costs of only partial methods, and (2) it is difficult to accurately estimate the execution costs of the remaining methods. To overcome the challenges, given an Android application, ANDROIDOFF first combines static and dynamic analysis to predict the execution costs of all its methods. After all the costs are estimated, ANDROIDOFF synthesizes an offloading plan, in which determines the offloading details. We evaluate ANDROIDOFF on a real-world application, with two mobile devices. Our results show that, compared with other approaches, ANDROIDOFF saves the response time by 8%–49% and reduces the energy consumption by 12%–49% on average for computation-intensive applications. © 2019 Elsevier Inc.

关键词： computation offloading

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AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT

arXiv

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arXiv 2023年

作者： Lee, Edward H. Kelly, Brendan Altinmakas, Emre Dogan, Hakan Mohammadzadeh, Maryam Colak, Errol Fu, Steve Choudhury, Olivia Ratan, Ujjwal Kitamura, Felipe Chaves, Hernan Zheng, Jimmy Said, Mourad Reis, Eduardo Lim, Jaekwang Yokoo, Patrícia Mitchell, Courtney Houshmand, Golnaz Ghassemi, Marzyeh Killeen, Ronan Qiu, Wendy Hayden, Joel Rafiee, Farnaz Klochko, Chad Bevins, Nicholas Sazgara, Faeze Wong, S. Simon Moseley, Michael Halabi, Safwan Yeom, Kristen W. School of Medicine Stanford University United States Insight Centre for Data Analytics University College Dublin Ireland Icahn School of Medicine at Mount Sinai United States Koç University School of Medicine Turkey Division of Neuroradiology Amiralam Hospital Tehran University of Medical Sciences Iran Unity Health Toronto St. Michael’s Hospital University of Toronto Canada Amazon Web Services United States Universidade Federal de São Paulo Brazil Fleni Argentina Radiology Department Centre International Carthage Médicale Monastir Tunisia Hospital Israelita Albert Einstein SP São Paulo Brazil Department of Radiology Kyungpook National University Hospital Korea Republic of Department of Radiology St. Joseph’s Hospital and Medical Center United States Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Iran Dept. of Electrical Engineering and Computer Science at MIT United States Department of Radiology Henry Ford Health System DetroitMI United States Department of Radiology Guilan University of Medical Sciences Iran Department of Electrical Engineering Stanford University United States Ann & Robert H. Lurie Children's Hospital of Chicago United States

While it is well known that population differences from genetics, sex, race, and environmental factors contribute to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We also propose an FL strategy that leverages synthetically generated data to overcome class and size imbalances. We also describe the sources of data heterogeneity in the context of FL, and show how even among the correctly labeled populations, disparities can arise due to these biases. Copyright © 2023, The Authors. All rights reserved.

关键词： Hospitals

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Flux Variations of Cosmic Ray Air Showers Detected by LHAASO-KM2A During a Thunderstorm on 10 June 2021

arXiv

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arXiv 2022年

作者： Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Zhe Cao, Zhen Chang, J. Chang, J.F. Chen, E.S. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.J. Chen, Y. Cheng, H.L. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Long, W.J. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Nan, Y.C. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Z.B. Tian, W.W. Wang, B.D. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Wang, J.S. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Hebei Normal University Hebei Shijiazhuang050024 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Facultéde Sciences Universitéde Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Department of Engineering Physics Tsinghua University Beijing100084 China School

The Large High Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA and WFCTA. The flux variations of cosmic ray air showers were studied by analyzing the KM2A data during the thunderstorm on 10 June 2021. The number of shower events that meet the trigger conditions increases significantly in atmospheric electric fields, with maximum fractional increase of 20%. The variations of trigger rates (increases or decreases) are found to be strongly dependent on the primary zenith angle. The flux of secondary particles increases significantly, following a similar trend with that of the shower events. To better understand the observed behavior, Monte Carlo simulations are performed with CORSIKA and G4KM2A (a code based on GEANT4). We find that the experimental data (in saturated negative fields) are in good agreement with simulations, assuming the presence of a uniform electric field of -700 V/cm with a thickness of 1500 m in the atmosphere above the observation level. Due to the acceleration/deceleration by the atmospheric electric field, the number of secondary particles with energy above the detector threshold is modified, resulting in the changes in shower detection rate. © 2022, CC BY-NC-SA.

关键词： Cosmic rays

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The Influence of Al2O3 Passivation of III-V on Ge Multijunction Solar Cells to the Spatial Distribution of Luminescent Coupling Effect

The Influence of Al2O3 Passivation of III-V on Ge Multijunct...

引用

IEEE Conference on Photovoltaic Specialists

作者： Bernice Mae F. Yu Jeco Abhinav Chikhalkar Maxime Giteau Ryo Tamaki Nazmul Ahsan Richard R. King Yoshitaka Okada Research Center for Advanced Science and Technology The University of Tokyo Japan School for Engineering of Matter Transport and Energy Arizona State University School of Electrical Computer and Energy Engineering Arizona State University Arizona USA

The nonuniformity of luminescent coupling (LC) effect in III-V based multijunction solar cells (MJSCs) is partly ascribed to recombination at perimeter defects. An attempt to solve this was done by sidewall passivation of InGaP/GaAs/Ge triple junction solar cells through atomic layer deposition of thin Al 2 O 3 . Results revealed that sidewall passivation of a complete MJSC can increase LC current collection in a limiting GaAs middle cell by 21.9% and enhance its uniformity by 7.2%; hence, demonstrating a non-invasive way to improve current matching among subcells.

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