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C60-based ionic salt electron shuttle for high-performance inverted perovskite solar modules

science (New York, N.Y.) 2025年第6750期388卷 964-968页

作者： You, Shuai Zhu, Hongwei Shen, Zhongjin Wang, Xiaoming Shao, Bingyao Wang, Qingxiao Lu, Jianxun Yuan, Youyou Dou, Benjia Dak Sanehira, Erin M. Russell, Todd Lorenz, Adam Dong, Yifan Chen, Lei Casareto, Marco Rolston, Nicholas Beard, Matthew C. Berry, Joseph J. Freitag, Marina Yan, Yanfa Bakr, Osman M. Zhu, Kai Chemistry and Nanoscience Center National Renewable Energy Laboratory Golden CO United States Division of Physical Science and Engineering Center for Renewable Energy and Storage Technologies (CREST) King Abdullah University of Science and Technology Thuwal Saudi Arabia School of Natural and Environmental Sciences Newcastle University Newcastle upon Tyne United Kingdom Department of Physics and Astronomy and Wright Center for Photovoltaics Innovation and Commercialization The University of Toledo Toledo OH USA Imaging and Characterization Core Lab King Abdullah University of Science and Technology Thuwal Saudi Arabia Bedford MA United States School of Electrical Computer and Energy Engineering Arizona State University Tempe AZ United States Materials Science Center National Renewable Energy Laboratory Golden CO United States Renewable and Sustainable Energy Institute University of Colorado Boulder Boulder CO United States Department of Physics University of Colorado Boulder Boulder CO United States

Although buckminsterfullerene (C60) is usually the electron transport layer (ETL) in inverted perovskite solar cells (PSCs), its molecular nature leads to weak interfaces that result in nonideal interfacial electronic and mechanical degradation. In this study, we synthesized an ionic salt from C60, 4-(1',5'-dihydro-1'-methyl-2'H-[5,6] fullereno-C60-Ih-[1,9-c]pyrrol-2'-yl) phenylmethanaminium chloride (CPMAC), and used it as the electron shuttle in inverted PSCs. The CH2-NH3+ head group in the CPMA cation improved the ETL interface, and the ionic nature enhanced the packing, leading to a ~threefold increase in the interfacial toughness compared with that of C60. Using CPMAC, we obtained ~26% power conversion efficiencies (PCEs) with ~2% degradation after 2100 hours of 1-sun operation at 65°C. For minimodules (four subcells, 6 square centimeters), we achieved a PCE of ~23% with <9% degradation after 2200 hours of operation at 55°C.

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Pressure induced transition from chiral charge order to time-reversal symmetry-breaking superconducting state in Nb-doped CsV3Sb5

arXiv

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arXiv 2024年

作者： Graham, J.N. Islam, S.S. Sazgari, V. Li, Y. Deng, H. Janka, G. Zhong, Y. Gerguri, O. Král, P. Doll, A. Bialo, I. Chang, J. Salman, Z. Suter, A. Prokscha, T. Yao, Y. Okazaki, K. Luetkens, H. Khasanov, R. Wang, Z. Yin, J.-X. Guguchia, Z. PSI Center for Neutron and Muon Sciences CNM Villigen5232 Switzerland Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement Ministry of Education School of Physics Beijing Institute of Technology Beijing100081 China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of Technology Beijing100081 China Department of Physics Southern University of Science and Technology Guangdong Shenzhen518055 China Institute for Solid States Physics The University of Tokyo Kashiwa Japan Physik-Institut Universität Zürich Winterthurerstrasse 190 ZürichCH-8057 Switzerland AGH University of Science and Technology Faculty of Physics and Applied Computer Science Kraków30-059 Poland International Center for Quantum Materials Beijing Institute of Technology Zhuhai319000 China Trans-scale Quantum Science Institute The University of Tokyo Tokyo Japan Material Innovation Research Center The University of Tokyo Kashiwa Japan

The experimental realisation of unconventional superconductivity and charge order in kagome systems AV3Sb5 is of critical importance. We conducted a highly systematic study of Cs(V1−xNbx)3Sb5 with x=0.07 (Nb0.07-CVS) by employing a unique combination of tuning parameters such as doping, hydrostatic pressure, magnetic fields, and depth, using muon spin rotation, AC susceptibility, and STM. We uncovered tunable magnetism in the normal state of Nb0.07-CVS, which transitions to a time-reversal symmetry (TRS) breaking superconducting state under pressure. Specifically, our findings reveal that the bulk of Nb0.07-CVS (at depths >20 nm from the surface) experiences TRS breaking below T∗ = 40 K, lower than the charge order onset temperature, TCO = 58 K. However, near the surface ( © 2024, CC BY.

关键词： Magnetic susceptibility

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Data augmentation in microscopic images for material data mining

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npj Computational Materials 2020年第1期6卷 601-609页

作者： Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China School of Materials Science and Engineering University of Science and Technology BeijingBeijing 100083China School of Materials Science and Technology Liaoning Technical UniversityLiaoning 114051China The Institute of Statistical Mathematics Research Organization of Information and SystemsTachikawaTokyo 190-8562Japan National Intellectual Property Administration Beijing 100088China Faculty of Engineering Sciences University of BurundiBujumburaBurundi Faculty of Engineering and Technology Palestine Technical University–KadoorieTulkaremPalestine College of Information Science and Engineering China University of PetroleumBeijingChina Key Lab of Petroleum Data Mining China University of PetroleumBeijingChina

Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.

关键词： mining instance competitive

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Deep Learning Techniques for Automatic Lateral X-ray Cephalometric Landmark Detection: Is the Problem Solved?

arXiv

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arXiv 2024年

作者： Zhang, Hongyuan Wang, Ching-Wei Muzakky, Hikam Dai, Juan Li, Xuguang Ma, Chenglong Wu, Qian Cui, Xianan Xu, Kunlun He, Pengfei Guo, Dongqian Wang, Xianlong Lee, Hyunseok Zhong, Zhangnan Zhu, Zhu Huang, Bingsheng Medical AI Lab School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taiwan Department of Stomatology Shenzhen University General Hospital Shenzhen University Shenzhen518055 China Institute of Stomatological Research Shenzhen University Shenzhen518055 China Chohotech Technology Co. Ltd. Lianchuang Street Yuhang District Hangzhou311422 China Singapore School of Computer Science Wuhan University Wuhan430072 China Wangxuan Institute of Computer Technology Peking University Beijing100871 China School of Artificial Intelligence Xidian University Xi’an710126 China State Key Laboratory of Internet of Things for Smart City University of Macau Taipa China The Faculty of Information Science and Engineering Ocean University of China Qingdao266100 China Digital Healthcare Department Daegu-Gyeongbuk Medical Innovation Foundation Daegu427724 Korea Republic of School of Life Science South China Normal University Guangzhou510631 China Information and Data Department Children’s Hospital Zhejiang University School of Medicine Hangzhou310000 China

Localization of the craniofacial landmarks from lateral cephalograms is a fundamental task in cephalometric analysis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Cephalometric Landmark Detection (CL-Detection)" dataset, which is the largest publicly available and comprehensive dataset for cephalometric landmark detection. This multi-center and multi-vendor dataset includes 600 lateral X-ray images with 38 landmarks acquired with different equipment from three medical centers. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go for cephalometric landmark detection. Following the 2023 MICCAI CL-Detection Challenge, we report the results of the top ten research groups using deep learning methods. Results show that the best methods closely approximate the expert analysis, achieving a mean detection rate of 75.719% and a mean radial error of 1.518 mm. While there is room for improvement, these findings undeniably open the door to highly accurate and fully automatic location of craniofacial landmarks. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for the community to benchmark future algorithm developments at https: //***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Trustworthy digital twins in the industrial internet of things with blockchain

arXiv

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arXiv 2020年

作者： Suhail, Sabah Hussain, Rasheed Jurdak, Raja Hong, Choong Seon Department of Computer Science and Engineering Kyung Hee University Korea Republic of Networks and Blockchain Lab Innopolis University Russia Trusted Networks Lab School of Computer Science Queensland University of Technology Australia

Industrial processes rely on sensory data for critical decision-making processes. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the trustworthiness of data. To this end, we envision a blockchain-based framework for the Industrial Internet of Things (IIoT) to address the issues of data management and security. Once the data collected from trustworthy sources are recorded in the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, we leverage Digital Twins (DTs) that can draw intelligent conclusions from data by identifying the faults and recommending precautionary measures ahead of critical events. Furthermore, we discuss the integration of DTs and blockchain to target key challenges of disparate data repositories, untrustworthy data dissemination, and fault diagnosis. Finally, we identify outstanding challenges faced by the IIoT and future research directions while leveraging blockchain and DTs. Copyright © 2020, The Authors. All rights reserved.

关键词： blockchain

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ToPoFM: Topology-Guided Pathology Foundation Model for High-Resolution Pathology Image Synthesis with Cellular-Level Control

引用

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Jingxiong Zhu, Chenglu Zheng, Sunyi Chen, Pingyi Sun, Yuxuan Li, Honglin Yang, Lin Zhejiang University College of Computer Science and Technology Hangzhou310027 China Westlake University School of Engineering Hangzhou310024 China Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center for Cancer Tianjin's Clinical Research Center for Cancer State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine Tianjin Key Laboratory of Digestive Cancer Key Laboratory of Cancer Prevention and Therapy Department of Radiology Tianjin China Westlake University Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Hangzhou310024 China Westlake Institute for Advanced Study Institute of Advanced Technology Hangzhou310024 China Muyuan Laboratory Center for Interdisciplinary Research and Innovation Henan Zhengzhou450016 China

Synthetic data generation emerges as a strategy to mitigate data scarcity in digital pathology, where complicated tissue and cellular features are correlated with cancer diagnosis. The synthesis of such visuals, however, suffers from limited inter class diversity and scarcity of cellular annotations. Current methodologies struggle with capturing the broad spectrum of pathology features, causing unpredictable objects and defected fidelity. Moreover, discrepancies in image resolution across developmental and operational phases can amplify the distribution shifts, undermining the precision of diagnosis. To address these challenges, we introduce TOpology guided PathOlogy Foundation Model (ToPoFM), a visual foundation model designed for the synthesis of high-resolution pathology images with cellular-level control. Our approach integrates a topology-informed cell arrangement generator to steer large language models for crafting synthetic cell arrangements. We correlate cell arrangement guidance with diffusion model for pathology content generation, then further implement a random sliding inference strategy, merging discrete low-resolution samplings into single high-resolution representation. Our model requires only small patches for training. The efficacy of ToPoFM is demonstrated through extensive experiments, complemented by expert validations, showing high fidelity on data synthesis. Additionally, we underscore the utility of our generated imagery as an augmentation tool, enhancing the performance of downstream tasks, including cancer subtype classification and segmentation. © 2025 IEEE.

关键词： Image segmentation

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

arXiv

引用

arXiv 2023年

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Zhou, Hong Zhu, Shanfeng School of Computer Science Fudan University Shanghai200433 China Atypon Systems LLC United Kingdom Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Ministry of Education Shanghai200433 China MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China Zhangjiang Fudan International Innovation Center Shanghai200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai200433 China

Biomedical named entity recognition (BNER) serves as the foundation for numerous biomedical text mining tasks. Unlike general NER, BNER require a comprehensive grasp of the domain, and incorporating external knowledge beyond training data poses a significant challenge. In this study, we propose a novel BNER framework called DMNER. By leveraging existing entity representation models SAP-BERT, we tackle BNER as a two-step process: entity boundary detection and biomedical entity matching. DMNER exhibits applicability across multiple NER scenarios: 1) In supervised NER, we observe that DMNER effectively rectifies the output of baseline NER models, thereby further enhancing performance. 2) In distantly supervised NER, combining MRC and AutoNER as span boundary detectors enables DMNER to achieve satisfactory results. 3) For training NER by merging multiple datasets, we adopt a framework similar to DS-NER but additionally leverage ChatGPT to obtain high-quality phrases in the training. Through extensive experiments conducted on 10 benchmark datasets, we demonstrate the versatility and effectiveness of DMNER. Copyright © 2023, The Authors. All rights reserved.

关键词： Character recognition

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REASSIGNER: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

arXiv

引用

arXiv 2022年

作者： Cui, Haochuan Sheng, Junjie Jin, Bo Hu, Yiqiu Su, Li Zhu, Lei Zhou, Wenli Wang, Xiangfeng School of Computer Science and Technology East China Normal University Shanghai200062 China Algorithm Innovation Lab. Huawei Cloud Computing Technologies Co. Shaanxi Xi’an710049 China Computing Service Product Dept. Huawei Cloud Computing Technologies Co. Shaanxi Xi’an710049 China

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (REASSIGNER), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of REASSIGNER is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. REASSIGNER can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei Cloud, the proposed REASSIGNER achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Cloud computing

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Reducing Share Size in JPEG Image Sharing over GF(2m) and GF(p) Galois Fields

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IEEE Internet of Things Journal 2025年

作者： Yu, Chunqiang Cheng, Shichao Zhang, Xianquan Wang, Pengfei Yang, Ching-Nung Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore National Dong Hwa University Department of Computer Science and Information Engineering Hualien974 Taiwan

Image sharing is a crucial security technique of protecting the original image. Most image sharing methods elaborate on the uncompressed formats and cannot be applied directly to compressed formats such as JPEG format. Recently, some related JPEG sharing methods were proposed. However, these methods are short in size reduction. To address this issue, we introduce a novel JPEG image sharing method that significantly reduces the file sizes of shared images while maintaining compatibility with the JPEG standard. Specifically, an original JPEG bitstream is parsed to obtain three components, namely, DC appended bits (DCAs), AC Huffman codes (ACHs), and AC appended bits (ACAs). Different sharing mechanisms are applied for these three components. The DCAs are shared over GF(2m) in a way that prevents overflow in the shared DC coefficients. The ACH sharing is performed by polynomials over GF(p) with an intermediate process by Huffman table mapping and the ACAs are shared by polynomials over GF(2m) with directed codes. Consequently, the ACH and ACA shares with smaller sizes are generated, respectively. To be compatible with the JPEG standard, the shared ACAs are adaptively modulated according to the shared ACHs. Finally, the shared JPEG images can be reconstructed by the shared DCAs, ACHs, ACAs and the original partial JPEG information. The experimental results demonstrate that the proposed method can efficiently reduce the file sizes of shared JPEG images and preserve the JPEG format for each shared image. © 2014 IEEE.

关键词： Polynomials

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MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learning

arXiv

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arXiv 2021年

作者： Su, Hao Niu, Jianwei Liu, Xuefeng Cui, Jiahe Wan, Ji Wu, Xinghao Wang, Nana State Key Lab of VR Technology and System School of Computer Science and Engineering Beihang University Beijing100000 China Industrial Technology Research Institute School of Information Engineering Zhengzhou University Henan450000 China Hangzhou Innovation Institute Beihang University Zhejiang310000 China

Manga is a fashionable Japanese-style comic form that is composed of black-and-white strokes and is generally displayed as raster images on digital devices. Typical mangas have simple textures, wide lines, and few color gradients, which are vectorizable natures to enjoy the merits of vector graphics, e.g., adaptive resolutions and small file sizes. In this paper, we propose MARVEL (MAnga’s Raster to VEctor Learning), a primitive-wise approach for vectorizing raster mangas by Deep Reinforcement Learning (DRL). Unlike previous learning-based methods which predict vector parameters for an entire image, MARVEL introduces a new perspective that regards an entire manga as a collection of basic primitives—stroke lines, and designs a DRL model to decompose the target image into a primitive sequence for achieving accurate vectorization. To improve vectorization accuracies and decrease file sizes, we further propose a stroke accuracy reward to predict accurate stroke lines, and a pruning mechanism to avoid generating erroneous and repeated strokes. Extensive subjective and objective experiments show that our MARVEL can generate impressive results and reaches the state-of-the-art level. Our code is open-source at: https://***/SwordHolderSH/Mang2Vec. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

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