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Synthese an der Wasseroberfläche von Vinylen-Verbundenen Kationischen Zwei-Dimensionalen Polymerfilmen als Beschichtung für Anion-Selektive Elektroden

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Angewandte Chemie 2024年第24期136卷

作者： Ye Yang Davood Sabaghi Dr. Chang Liu Dr. Arezoo Dianat David Mücke Dr. Haoyuan Qi Dr. Yannan Liu Dr. Mike Hambsch Prof. Zhi-Kang Xu Dr. Minghao Yu Prof. Gianaurelio Cuniberti Prof. Stefan C. B. Mannsfeld Prof. Ute Kaiser Prof. Renhao Dong Dr. Zhiyong Wang Prof. Xinliang Feng Center for Advancing Electronics Dresden & Faculty of Chemistry and Food Chemistry Technische Universität Dresden 01069 Dresden Germany Diese Autoren haben gleichermaßen zu dieser Arbeit beigetragen. MOE Engineering Research Center of Membrane and Water Treatment and Key Lab of Adsorption and Separation Materials & Technologies of Zhejiang Province Department of Polymer Science and Engineering Zhejiang University 310058 Hangzhou China The “Belt and Road” Sino-Portugal Joint Lab on Advanced Materials International Research Center for X Polymers Zhejiang University 310058 Hangzhou China Max Planck Institute of Microstructure Physics 06120 Halle Germany Institute for Materials Science and Max Bergmann Center for Biomaterials Technische Universität Dresden 01069 Dresden Germany Central Facility for Electron Microscopy Electron Microscopy of Materials Science Universität Ulm 89081 Ulm Germany Center for Advancing Electronics Dresden & Faculty of Electrical and Computer Engineering Technische Universität Dresden 01062 Dresden Germany Dresden Center for Computational Materials Science (DCMS) Technische Universität Dresden 01062 Dresden Germany Key Laboratory of Colloid and Interface Chemistry of the Ministry of Education School of Chemistry and Chemical Engineering Shandong University 250100 Jinan China

Vinylen verbrückte zwei-dimensionale Polymere (V-2DPs) und deren schichtgestapelten kovalent organische Gerüstverbindungen (V-2D COFs) zeichnen sich durch ihre hohe π-Konjugation in der Ebene und ihre robuste Struktur als vielversprechnende Kanidaten für energiebezogene Anwendungen aus. Allerdings sind derzeitige syntheisische Ansätze auf die Herstellung von V-2D COF Pulver beschränkt, welche schlecht prozessierbar sind, was die Integration in Geräte, insbesondere für Membranthechnologien, welche auf dünnen Filmen beruhen erschwert. Im folgenden berichten wir über die neu-artige Synthese von kationischen 2DPs-Filmen an der Wasseroberfläche, die über Vinyleneinheiten verbrückt sind ( V-C2DP-1 und V-C2DP-2 ) mittels Knoevenagel-Polykondensation, diese dienen als anionenselektive Elektrodenbeschichtung für hochreversible und langlebige Zink-basierte Dual-Ionen-Batterien (ZDIBs). Modellreaktionen und theoretische Modellierung zeigen eine erhöte Reaktivität und Reversibilität der Knoevenagel-Reaktion an der Wasseroberfläche an. Basierend darauf zeigten wir die 2D Polykondensation hin zu V-C2DP-Filmen an der Wasseroberfläche, welche eine weitreichen-de laterale Größe, anpassbare Dicke und hohe chemischer Stabilität besitzen. Repräsentativ zeigt sich V-C2DP-1 als ein vollständig crystalliner und flächen-orientierter Film mit den in der Ebene liegenden Gitterparametern von a =b≈43.3 Å. Dank der gut definierten katinoischen Funktionalitäten, orientierten 1D-Kanälen und des sabilen Gerüstes besitzt der V-C2DP-1 Film eine herausragende Transportselektivität von Bis(trifluormethansulfon)imid-anion (TFSI − ) (Transferrate, t =0.85) für die Graphitkathode in Hochspannungs-ZDIBs. Die hohe Selektivität führt zu zusätzlichen TFSI − -Intercalationsstufen wodurch eine höhere spezifischen Kapazität (von ~83 bis 124 mAh g −1 ), sowie Zyklusdauer (>1000 Zyklen, 95 % Kapazitätserhaltung) gefördert wird.

关键词： Grenzflächen Chemie Vinylen-verbundene 2DP Filme Selektiver Iontransport Elektroden Beschicktung Zwei-Ionen Batterien

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The Performance Investigation of Junctionless Transistor by Considering Different Recessed Gates

The Performance Investigation of Junctionless Transistor by ...

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2018 IEEE International Conference on Electron Devices and Solid State Circuits, EDSSC 2018

作者： Lou, Haijun Li, Wentao Yang, Yumei Lin, Xinnan School of Science Lanzhou University of Technology Lanzhou730050 China Shenzhen Key Lab of Advanced Electron Device and Integration School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

ISBN: (纸本)9781538662342

In this paper, the performance of junctionless transistor with three different recessed gates is investigated by numerical simulation. Then the impact of sidewall and the overlapped gate is discussed. The results show that the different recessed gates change the threshold voltage of devices. As compared between the three proposed devices, the gate-over structure has more superior subthreshold characterictics and drain current peformance. The sidewall angle variation in the gate-over devices is also studied. It will offer a helpful guide for fabricating the junctionless transistor with recessed gates. © 2018 IEEE.

关键词： Drain current

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Foggy Day Image Sharpening Algorithm Based on Depth of Field Estimation

Foggy Day Image Sharpening Algorithm Based on Depth of Field...

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2018 International Conference on advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Changli Li Qian Jia Advanced Signal and IMage Processing Learning and Engineering Lab (A Simple Lab)College of Computer and InformationHohai University

Traditional dehazing methods always use the dark channel a priori method to recover the original image,which leads to the calculation of the transmission is large and takes too much time. Therefore, in this paper, we present a fog-day-based image sharpening algorithm based on depth of field estimation. This approach makes use of white balancing,which eliminates the color cast that is caused by the atmospheric color. Then we extract the depth features of the haze image and construct the depth of field estimation model. Finally, the original image is restored by estimating the depth of field of each pixel of the image. The experimental results show that the method we de veloped has the capability to remove the haze efficiently, by which the finest details and edges are enhanced significantly.

关键词： Image dehazing depth of field estimation White balancing feature extraction

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Large language models for diabetes training:a prospective study

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Science Bulletin 2025年第6期70卷 934-942页

作者： Haoxuan Li Zehua Jiang Zhouyu Guan Yuqian Bao Yuexing Liu Tingting Hu Jiajia Li Ruhan Liu Liang Wu Di Cheng Hongwei Ji Yong Wang Ya-Xing Wang Carol Y.Cheung Yingfeng Zheng Jihong Wang Zhen Li Weibing Wu Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan Ching-Yu Cheng Tien Yin Wong Huating Li Chun Cai Lijuan Mao Lee-Ling Lim Yih-Chung Tham Bin Sheng Weiping Jia Shanghai University of Sport Shanghai 200438China School of Clinical Medicine Beijing Tsinghua Changgung HospitalTsinghua MedicineTsinghua UniversityBeijing 100084China Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua MedicineTsinghua UniversityBeijing 100084China Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and EngineeringSchool of ElectronicInformationand Electrical EngineeringShanghai Jiao Tong UniversityDepartment of Endocrinology and MetabolismShanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for DiabetesShanghai 200240China MoE Key Lab of Artificial Intelligence Artificial Intelligence InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Cardiology the Affiliated Hospital of Qingdao UniversityQingdao 266011China CEMS NCMISHCMSMADISAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100049China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China Beijing Institute of Ophthalmology Beijing Tongren HospitalCapital Medical UniversityBeijing Ophthalmology and Visual Sciences Key LaboratoryBeijing 100730China k.Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong 999077China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic CenterSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular DiseasesGuangzhou 510060China Department of Renal Medicine Singapore General HospitalSingHealth-Duke Academic Medical CentreSingapore169

Diabetes poses a considerable global health challenge,with varying levels of diabetes knowledge among healthcare professionals,highlighting the importance of diabetes *** Language Models(LLMs)provide new insights into diabetes training,but their performance in diabetes-related queries remains uncertain,especially outside the English language like *** first evaluated the performance of ten LLMs:ChatGPT-3.5,ChatGPT-4.0,Google Bard,LlaMA-7B,LlaMA2-7B,Baidu ERNIE Bot,Ali Tongyi Qianwen,MedGPT,HuatuoGPT,and Chinese LlaMA2-7B on diabetes-related queries,based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC)and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United ***,we assessed the training of primary care physicians(PCPs)without and with the assistance of ChatGPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical *** found that ChatGPT-4.0 outperformed other LLMs in the English examination,achieving a passing accuracy of 62.50%,which was significantly higher than that of Google Bard,LlaMA-7B,and *** the NCE-CPFC examination,ChatGPT-4.0,Ali Tongyi Qianwen,Baidu ERNIE Bot,Google Bard,MedGPT,and ChatGPT-3.5 successfully passed,whereas LlaMA2-7B,HuatuoGPT,Chinese LLaMA2-7B,and LlaMA-7B ***-4.0(84.82%)surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%).In summary,LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language,and hold great potential to assist future diabetes training for physicians globally.

关键词： Diabetes Diabetestraining Largelanguagemodels Primarydiabetescare Prospectivestudy

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Revisiting metric learning for few-shot image classification

arXiv

引用

arXiv 2019年

作者： Li, Xiaomeng Yu, Lequan Fu, Chi-Wing Fang, Meng Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Tencent AI Lab United States

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Field-varying aberration recovery in EUV microscopy using mask roughness

Field-varying aberration recovery in EUV microscopy using ma...

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Computational Optical Sensing and Imaging, COSI 2018

作者： Gunjala, Gautam Wojdyla, Antoine Shanker, Aamod Sherwin, Stuart Benk, Markus P. Goldberg, Kenneth A. Naulleau, Patrick P. Waller, Laura Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Advanced Light Source Lawrence Berkeley National Lab BerkeleyCA United States Center for X-Ray Optics Lawrence Berkeley National Lab BerkeleyCA United States

ISBN: (纸本)9781943580446

We derive and solve a simplified, self-calibrated inverse problem to characterize the field-dependent aberrations of an EUV synchrotron-based full-field microscope, using images of the surface roughness of an EUV photomask under several angles of illumination. We demonstrate diffraction-limited imaging performance at the center of its field-of-view. © 2018 The Author(s).

关键词： Surface roughness

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GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS

arXiv

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arXiv 2025年

作者： Li, Zhenlong Ning, Huan Gao, Song Janowicz, Krzysztof Li, Wenwen Arundel, Samantha T. Yang, Chaowei Bhaduri, Budhendra Wang, Shaowen Zhu, A. Xing Gahegan, Mark Shekhar, Shashi Ye, Xinyue McKenzie, Grant Cervone, Guido Hodgson, Michael E. Geoinformation and Big Data Research Lab Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of Wisconsin – Madison WI United States STKO Lab Department of Geography and Regional Research University of Vienna Vienna Austria Spatial Analysis Research Center School of Geographical Sciences and Urban Planning Arizona State University AZ United States Center of Excellence for Geospatial Information Science U.S. Geological Survey VA United States NSF Spatiotemporal Innovation Center Department of Geography & Geoinformation Science George Mason University VA United States TN United States CyberGIS Center for Advanced Digital and Spatial Studies Department of Geography and Geographic Information Science University of Illinois Urbana-Champaign IL United States School of Computer Science University of Auckland New Zealand Department of Computer Science & Engineering University of Minnesota MN United States Department of Landscape Architecture & Urban Planning Center for Geospatial Sciences Applications & Technology Texas A&M University TX United States Platial Analysis Lab Department of Geography McGill University Quebec Canada Institute for Computational and Data Sciences Department of Geography The Pennsylvania State University University ParkPA United States Department of Geography University of South Carolina SC United States

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five autonomous levels, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modeling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future. © 2025, CC BY.

关键词： Image retrieval

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Hamiltonian Hopping for Efficient Chiral Mode Switching in Encircling Exceptional Points

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Physical Review Letters 2020年第18期125卷 187403-187403页

作者： Aodong Li Jianji Dong Jian Wang Ziwei Cheng John S. Ho Dawei Zhang Jing Wen Xu-Lin Zhang C. T. Chan Andrea Alù Cheng-Wei Qiu Lin Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology No. 516 Jungong Road Shanghai 200093 China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun 130012 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong 999077 China Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.

关键词： Open quantum systems & decoherence PT-symmetric quantum mechanics Photonics Waveguides

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Deep learning for image and point cloud fusion in autonomous driving: A review

arXiv

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arXiv 2020年

作者： Cui, Yaodong Chen, Ren Chu, Wenbo Chen, Long Tian, Daxin Li, Ying Cao, Dongpu The Waterloo CogDrive Lab Department of Mechanical and Mechatronics Engineering University of Waterloo 200 University Ave West WaterlooONN2L3G1 Canada Research Institute Co. Ltd. Beijing100176 China School of Data and Computer Science Sun Yat-sen University Zhuhai519082 China Waytous Inc. Qingdao266109 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China

Autonomous vehicles were experiencing rapid development in the past few years. However, achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic driving environment. Therefore, autonomous vehicles are equipped with a suite of different sensors to ensure robust, accurate environmental perception. In particular, the camera-LiDAR fusion is becoming an emerging research theme. However, so far there has been no critical review that focuses on deep-learning-based camera-LiDAR fusion methods. To bridge this gap and motivate future research, this paper devotes to review recent deep-learning-based data fusion approaches that leverage both image and point cloud. This review gives a brief overview of deep learning on image and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion methods in depth completion, object detection, semantic segmentation, tracking and online cross-sensor calibration, which are organized based on their respective fusion levels. Furthermore, we compare these methods on publicly available datasets. Finally, we identified gaps and over-looked challenges between current academic researches and real-world applications. Based on these observations, we provide our insights and point out promising research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

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Quantum Computing for High-Energy Physics: State of the Art and Challenges

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PRX Quantum 2024年第3期5卷 037001-037001页

作者： Alberto Di Meglio Karl Jansen Ivano Tavernelli Constantia Alexandrou Srinivasan Arunachalam Christian W. Bauer Kerstin Borras Stefano Carrazza Arianna Crippa Vincent Croft Roland de Putter Andrea Delgado Vedran Dunjko Daniel J. Egger Elias Fernández-Combarro Elina Fuchs Lena Funcke Daniel González-Cuadra Michele Grossi Jad C. Halimeh Zoë Holmes Stefan Kühn Denis Lacroix Randy Lewis Donatella Lucchesi Miriam Lucio Martinez Federico Meloni Antonio Mezzacapo Simone Montangero Lento Nagano Vincent R. Pascuzzi Voica Radescu Enrique Rico Ortega Alessandro Roggero Julian Schuhmacher Joao Seixas Pietro Silvi Panagiotis Spentzouris Francesco Tacchino Kristan Temme Koji Terashi Jordi Tura Cenk Tüysüz Sofia Vallecorsa Uwe-Jens Wiese Shinjae Yoo Jinglei Zhang 1211 Geneva Switzerland CQTA Computation-based Science and Technology Research Center 8803 Rüschlikon Switzerland Department of Physics IBM Quantum Physics Division Deutsches Elektronen-Synchrotron (DESY) Notkestraße 85 22607 Hamburg Germany Templergraben 55 52062 Aachen Germany TIF Lab Dipartimento di Fisica Institut für Physik 〈aQa Physics Division Department of Computer Science Facultad de Ciencias Institute of Theoretical Physics 38116 Braunschweig Germany Transdisciplinary Research Area “Building Blocks of Matter and Fundamental Interactions” (TRA Matter) and Helmholtz Institute for Radiation and Nuclear Physics (HISKP) Institute for Theoretical Physics 6020 Innsbruck Austria Department of Physics and Arnold Sommerfeld Center for Theoretical Physics Munich Germany Institute of Physics CNRS/IN2P3 IJCLab Department of Physics and Astronomy Via Marzolo 8 35131 Padua Italy Science Park 105 1098 XG Amsterdam Netherlands Department of Gravitational Waves and Fundamental Physics International Center for Elementary Particle Physics Department of Physical Chemistry 20018 Donostia–San Sebastián Spain EHU Quantum Center University of the Basque Country UPV/EHU P.O. Box 644 48080 Bilbao Spain Basque Foundation for Science Via Sommarive 14 38123 Povo Trento Italy Department Física Center of Physics and Engineering of Advanced Materials (CeFEMA) Instituto Superior Técnico Lisbon Portugal Laboratory of Physics for Materials and Emergent Technologies (LaPMET) Lisbon Portugal Kirk and Pine Street Batavia Illinois 60510 USA Albert Einstein Center for Fundamental Physics Institute for Theoretical Physics 98 Rochester Sreet Upton New York 11973 USA Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage—namely, a significant (in some cases exponential) speedup of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small-scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models that are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this Roadmap paper, led by CERN, DESY, and IBM, we provide the status of high-energy physics quantum computations and give examples of theoretical and experimental target benchmark applications, which can be addressed in the near future. Having in mind hardware with about 100 qubits capable of executing several thousand two-qubit gates, where possible, we also provide resource estimates for the examples given using error-mitigated quantum computing. The ultimate declared goal of this task force is therefore to trigger further research in the high-energy physics community to develop interesting use cases for demonstrations on near-term quantum computers.

关键词： Quantum computation Quantum simulation

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