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A Fully Amorphous, Dynamic Cross-Linked Polymer Electrolyte for Lithium-Sulfur Batteries Operating at Subzero-Temperatures

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Angewandte Chemie 2023年第5期136卷

作者： Juan Zhang Jia Chou Xiao-Xi Luo Dr. Yi-Ming Yang Ming-Yan Yan Prof. Di Jia Chao-Hui Zhang Ya-Hui Wang Wen-Peng Wang Shuang-Jie Tan Jun-Chen Guo Prof. Yao Zhao Prof. Fuyi Wang Prof. Sen Xin Prof. Li-Jun Wan Prof. Yu-Guo Guo CAS Key Laboratory of Molecular Nanostructure and Nanotechnology CAS Research/Education Centre for Excellence in Molecular Sciences Beijing National Laboratory for Molecular Sciences (BNLMS) Institute of Chemistry Chinese Academy of Sciences (CAS) 100190 Beijing P. R. China Contribution: Conceptualization (supporting) Data curation (lead) Formal analysis (supporting) Funding acquisition (supporting) Investigation (equal) Methodology (lead) Visualization (lead) Writing - original draft (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Formal analysis (supporting) Investigation (supporting) Methodology (supporting) Visualization (supporting) Writing - original draft (supporting) Contribution: Data curation (supporting) Key Laboratory of Science and Technology on High-tech Polymer Materials Institute of Chemistry Chinese Academy of Sciences 100190 Beijing P. R. China University of Chinese Academy of Sciences (UCAS) 100049 Beijing P. R. China Beijing National Laboratory for Molecular Sciences State Key Laboratory of Polymer Physics and Chemistry Institute of Chemistry Chinese Academy of Sciences 100190 Beijing P. R. China Contribution: Data curation (supporting) Visualization (supporting) Beijing National Laboratory for Molecular Sciences National Centre for Mass Spectrometry in Beijing CAS Key Laboratory of Analytical Chemistry for Living Biosystems Institute of Chemistry Chinese Academy of Sciences 100190 Beijing P. R. China Contribution: Conceptualization (supporting) Formal analysis (equal) Funding acquisition (supporting) Investigation (supporting) Supervision (equal) Validation (equal) Writing - review & editing (equal) Contribution: Investigation (supporting) Supervision (supporting) Validation (supporting) Contribution: Conceptualization (lead) Formal analysis (equal) Funding acquisition (lead) Investigation (equal) Project administration (lead) Resources (lead) Supervision (lead) Validation (equal) Writing - review & editi

Solid-state lithium-sulfur batteries have shown prospects as safe, high-energy electrochemical storage technology for powering regional electrified transportation. Owing to limited ion mobility in crystalline polymer electrolytes, the battery is incapable of operating at subzero temperature. Addition of liquid plasticizer into the polymer electrolyte improves the Li-ion conductivity yet sacrifices the mechanical strength and interfacial stability with both electrodes. In this work, we showed that by introducing a spherical hyperbranched solid polymer plasticizer into a Li + -conductive linear polymer matrix, an integrated dynamic cross-linked polymer network was built to maintain fully amorphous in a wide temperature range down to subzero. A quasi-solid polymer electrolyte with a solid mass content >90 % was prepared from the cross-linked polymer network, and demonstrated fast Li + conduction at a low temperature, high mechanical strength, and stable interfacial chemistry. As a result, solid-state lithium-sulfur batteries employing the new electrolyte delivered high reversible capacity and long cycle life at 25 °C, 0 °C and −10 °C to serve energy storage at complex environmental conditions.

关键词： Interface Stability Li−S Batteries Low-Temperature Performance Solid Electrolyte Interfaces Solid-State Batteries

Coupled-channel approach to Tcc+ including three-body effects

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arXiv 2021年

作者： Du, Meng-Lin Baru, Vadim Dong, Xiang-Kun Filin, Arseniy Guo, Feng-Kun Hanhart, Christoph Nefediev, Alexey Nieves, Juan Wang, Qian Institutos de Investigación de Paterna Apartado 22085 Valencia46071 Spain Institut für Theoretische Physik II Ruhr-Universität Bochum BochumD-44780 Germany Institute for Theoretical and Experimental Physics NRC Kurchatov Institute Moscow117218 Russia CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China Institute for Advanced Simulation Institut für Kernphysik Jülich Center for Hadron Physics Forschungszentrum Jülich JülichD-52425 Germany P.N. Lebedev Physical Institute The Russian Academy of Sciences Leninskiy Prospect 53 Moscow119991 Russia Moscow Institute of Physics and Technology Institutsky lane 9 Moscow Region Dolgoprudny141700 Russia Guangdong Provincial Key Laboratory of Nuclear Science Institute of Quantum Matter South China Normal University Guangzhou510006 China Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China

A coupled-channel approach is applied to the charged tetraquark state Tcc+ recently discovered by the LHCb Collaboration. The parameters of the interaction are fixed by a fit to the observed line shape in the three-body D0D0π+ channel. Special attention is paid to the three-body dynamics in the Tcc+ due to the finite life time of the D∗. An approach to the Tcc+ is argued to be self-consistent only if both manifestations of the three-body dynamics, the pion exchange between the D and D∗ mesons and the finite D∗ width, are taken into account simultaneously to ensure that three-body unitarity is preserved. This is especially important to precisely extract the pole position in the complex energy plane whose imaginary part is very sensitive to the details of the coupled-channel scheme employed. The D0D0 and D0D+ invariant mass distributions, predicted based on this analysis, are in good agreement with the LHCb data. The low-energy expansion of the D∗D scattering amplitude is performed and the low-energy constants (the scattering length and effective range) are extracted. The compositeness parameter of the Tcc+ is found to be close to unity, which implies that the Tcc+ is a hadronic molecule generated by the interactions in the D∗+D0 and D∗0D+ channels. Employing heavy-quark spin symmetry, an isoscalar D∗D∗ molecular partner of the Tcc+ with JP = 1+ is predicted under the assumption that the DD∗-D∗D∗ coupled-channel effects can be neglected. Copyright © 2021, The Authors. All rights reserved.

关键词： Hadrons

A Slice-Guided Method of Indoor Scene Structure Retrieving 12th

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A Slice-Guided Method of Indoor Scene Structure Retrieving

12th International Conference on e-Learning and Games, Edutainment 2018

作者： Wang, Lijuan Wang, Yinghui Wang, Ningna Ning, Xiaojuan Lv, Ke Huang, Liangyi Xi’an University of Technology South Jinhua Road no. 5 Xi’an710032 China Xi’an Technology University Mid Xuefu Road no. 2 Xi’an710021 China Shaanxi Key Laboratory of Network Computing and Security Technology South Jinhua Road no. 5 Xi’an710032 China *** B.V. Vijzelstraat 66-80 Amsterdam1017HL Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington DC20052 United States

ISBN: (纸本)9783030237110

The structure information of indoor scene is necessary for a robot who works in a room. In order to achieve structure of an indoor scene, a slice-guided method of indoor scene structure retrieving is proposed in this paper. We present a slicing based approach that transforms three-dimensional (3D) segmentations into two-dimensional (2D) segmentation and segments different kinds of primitive shapes while keeping the global topology structure of the indoor scene. The global topology structure is represented by a graph. The graph is compared with the given indoor scene template. The matched objects and the topology relation between them are finally presented. Our experiment results show that the proposed method performs well on several typical indoor scenes, even if some data are missing. © 2019, Springer Nature Switzerland AG.

关键词： Topology

A survey on edge computing systems and tools

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arXiv 2019年

作者： Liu, Fang Tang, Guoming Li, Youhuizi Cai, Zhiping Zhang, Xingzhou Zhou, Tongqing School of Data and Computer Science Sun Yat-sen University GuangzhouGuangdong China Key Laboratory of Science and Technology on Information System Engineering National University of Defense Technology ChangshaHunan China School of Compute Science and Technology Hangzhou Dianzi University China College of Computer National University of Defense Technology ChangshaHunan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

—Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

Nano Proteolysis Targeting Chimeras (PROTACs) with Anti-Hook Effect for Tumor Therapy

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Angewandte Chemie 2023年第37期135卷

作者： Ni-Yuan Zhang Da-Yong Hou Xing-Jie Hu Jian-Xiao Liang Man-Di Wang Zhang-Zhi Song Li Yi Zhi-Jia Wang Prof. Hong-Wei An Prof. Wanhai Xu Prof. Hao Wang CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology (NCNST) Beijing 100190 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 P. R. China Contribution: Conceptualization (lead) Data curation (lead) Methodology (lead) Project administration (lead) Writing - original draft (lead) Writing - review & editing (lead) Department of Urology Harbin Medical University Cancer Hospital Heilongjiang Key Laboratory of Scientific Research in Urology Harbin 150001 China NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy Harbin Medical University Harbin 150001 China Henan Institute of Advanced Technology Zhengzhou University Zhengzhou 450052 China Contribution: Data curation (equal) Investigation (equal) Methodology (equal) Supervision (equal) Contribution: Formal analysis (equal) Investigation (equal) Methodology (equal) Supervision (equal) Contribution: Formal analysis (equal) Investigation (equal) Supervision (equal) Contribution: Investigation (equal) Methodology (equal) Supervision (equal) Contribution: Data curation (equal) Formal analysis (equal) Methodology (equal) Contribution: Conceptualization (lead) Data curation (lead) Project administration (lead) Supervision (lead) Writing - review & editing (lead) Contribution: Conceptualization (lead) Data curation (lead) Methodology (lead) Project administration (lead) Supervision (lead) Writing - review & editing (lead)

Proteolysis targeting chimera (PROTAC) is an emerging pharmacological modality with innovated post-translational protein degradation capabilities. However, off-target induced unintended tissue effects and intrinsic “hook effect” hinder PROTAC biotechnology to be maturely developed. Herein, an intracellular fabricated nano proteolysis targeting chimeras (Nano-PROTACs) modality with a center-spoke degradation network for achieving efficient dose-dependent protein degradation in tumor is reported. The PROTAC precursors are triggered by higher GSH concentrations inside tumor cells, which subsequently in situ self-assemble into Nano-PROTACs through intermolecular hydrogen bond interactions. The fibrous Nano-PROTACs can form effective polynary complexes and E3 ligases degradation network with multi-binding sites, achieving dose-dependent protein degradation with “anti-hook effect”. The generality and efficacy of Nano-PROTACs are validated by degrading variable protein of interest (POI) such as epidermal growth factor receptor (EGFR) and androgen receptor (AR) in a wide-range dose-dependent manner with a 95 % degradation rate and long-lasting potency up to 72 h in vitro. Significantly, Nano-PROTACs achieve in vivo dose-dependent protein degradation up to 79 % and tumor growth inhibition in A549 and LNCap xenograft mice models, respectively. Taking advantages of in situ self-assembly strategy, the Nano-PROTACs provide a generalizable platform to promote precise clinical translational application of PROTAC.

关键词： Nanomedicine PROTACs Peptide Self-Assembly Tumor

Overlapping Community Detection Based on Influence and Seeds Extension

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2019年第1期47卷 153-160页

作者： Yu, Zhi-Yong Chen, Ji-Jie Guo, Kun Chen, Yu-Zhong Xu, Qian College of Mathematics and Computer Sciences Fuzhou University FuzhouFujian350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing FuzhouFujian350116 China Ministry of Education Key Laboratory of Spatial Data Mining & Information Sharing FuzhouFujian350116 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd. FuzhouFujian350003 China

Community detection is a significant research direction in the research of social networks. To improve the quality of seeds selection and expansion, we propose an influence seeds extension overlapping community detection (i-SEOCD) algorithm for overlapping community detection. First, i-SEOCD uses a node influence strategy to find the seed communities with tight structures. Second, on the basis of the seed communities, we calculate the similarity among communities and their neighbor nodes. The nodes whose similarity is greater than a predefined threshold are selected. Third, the strategy of optimizing a self-adaptive function is adopted to expand the communities. Finally, the free nodes in the network are assigned to their corresponding communities in order to find out all the overlapping community structures. Experiments on the real and artificial networks show that i-SEOCD is capable of discovering overlapping communities in complex social networks efficiently. © 2019, Chinese institute of Electronics. All right reserved.

关键词： Population dynamics

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GLOBAL CLIMATE

Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

MQGrad: Reinforcement learning of gradient quantization in parameter server

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arXiv 2018年

作者： Cui, Guoxin Xu, Jun Zeng, Wei Lan, Yanyan Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of ComputingTechnology Chinese Academy of Sciences Beijing100190 China

One of the most significant bottleneck in training large scale machine learning models on parameter server (PS) is the communication overhead, because it needs to frequently exchange the model gradients between the workers and servers during the training iterations. Gradient quantization has been proposed as an effective approach to reducing the communication volume. One key issue in gradient quantization is setting the number of bits for quantizing the gradients. Small number of bits can significantly reduce the communication overhead while hurts the gradient accuracies, and vise versa. An ideal quantization method would dynamically balance the communication overhead and model accuracy, through adjusting the number bits according to the knowledge learned from the immediate past training iterations. Existing methods, however, quantize the gradients either with fixed number of bits, or with predefined heuristic rules. In this paper we propose a novel adaptive quantization method within the framework of reinforcement learning. The method, referred to as MQGrad, formalizes the selection of quantization bits as actions in a Markov decision process (MDP) where the MDP states records the information collected from the past optimization iterations (e.g., the sequence of the loss function values). During the training iterations of a machine learning algorithm, MQGrad continuously updates the MDP state according to the changes of the loss function. Based on the information, MDP learns to select the optimal actions (number of bits) to quantize the gradients. Experimental results based on a benchmark dataset showed that MQGrad can accelerate the learning of a large scale deep neural network while keeping its prediction accuracies. Copyright © 2018, The Authors. All rights reserved.

关键词： Markov processes

Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning

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arXiv 2018年

作者： Humayoo, Mahammad Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China

Off-policy learning is more unstable compared to on-policy learning in reinforcement learning (RL). One of the reasons for instability of off-policy learning is a discrepancy between target (π) and behavior (b) policy distribution. The discrepancy between π and b distribution can be alleviated by employing the smooth variant of importance sampling (IS), such as relative importance sampling (RIS).The RIS has parameter β ∈ [0, 1] that controls the smoothness. To cope with the instability of off-policy learning, we present the first relative importance sampling-off-policy actor-critic (RIS-off-PAC) model-free algorithms in RL. In our method, A network yields the target policy (actor), the value function (critic) assessing the current policy (π) using samples drawn from the behavior policy. We use action values generated from the behavior policy in reward function to train our algorithms rather than from the target policy. We also use deep neural networks to train both the actor and critic. We evaluated our algorithms on a number of OpenAI Gym problems and demonstrated better or comparable performance to several state-of-the-art RL baselines. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning