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Engineering Spin States of Isolated Copper Species in a Metal-Organic Framework Improves Urea Electrosynthesis

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Nano-Micro Letters 2023年第9期15卷 536-549页

作者： Yuhang Gao Jingnan Wang Yijun Yang Jian Wang chuang zhang Xi Wang Jiannian Yao Key Laboratory of Photochemistry Beijing National Laboratory for Molecular SciencesInstitute of ChemistryChinese Academy of SciencesBeijing 100190People’s Republic of China University of Chinese Academy of Sciences Beijing 100049People’s Republic of China Molecular Plus and Collaborative Innovation Center of Chemical Science and Engineering Tianjin UniversityTianjin 300072People’s Republic of China Department of Physics School of Physical Science and EngineeringBeijing Jiaotong UniversityBeijing 100044People’s Republic of China Research Center for Magnetic and Spintronic Materials National Institute for Materials Science Tsukuba 305-0047Japan

The catalytic activities are generally believed to be relevant to the electronic states of their active center, but understanding this relationship is usually difficult. Here, we design two types of catalysts for electrocatalytic urea via a coordination strategy in a metal–organic frameworks: Cu^(Ⅲ)-HHTP and Cu^(Ⅱ)-HHTP. Cu^(Ⅲ)-HHTP exhibits an improved urea production rate of 7.78 mmol h^(−1)g^(−1) and an enhanced Faradaic efficiency of 23.09% at-0.6 V vs. reversible hydrogen electrode, in sharp contrast to Cu^(Ⅱ)-HHTP.Isolated CuⅢspecies with S = 0 spin ground state are demonstrated as the active center in Cu^(Ⅲ)-HHTP, different from Cu^(Ⅱ) with S = 1/2 in Cu^(Ⅱ)-HHTP. We further demonstrate that isolated Cu^(Ⅲ)with an empty dx2-y20orbital in Cu^(Ⅲ)-HHTP experiences a single-electron migration path with a lower energy barrier in the C–N coupling process, while Cu^(Ⅱ)with a single-spin state( d_(x2-y2)^(1)) in Cu^(Ⅱ)-HHTP undergoes a two-electron migration pathway.

关键词： Electrocatalysis Urea synthesis Metal-organic framework Spin catalysis C-N coupling

File Wallet:A File Management System Based on IPFS and Hyperledger Fabric

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Computer Modeling in Engineering & Sciences 2022年第2期130卷 949-966页

作者： Jienan Chen chuang zhang Yu Yan Yuan Liu Software College of Northeastern University ShenyangChina

Online file management systems enable cooperatively editing and sharing.However,due to the cost of communication and storage infrastructures,traditional online file management services,e.g.,Google Drive and OneDrive,usually provide limited storage space and relatively low download speed for free users.To achieve better performance,ordinary users have to purchase their expensive services.Moreover,these file management systems are based on centralized architecture and bear the privacy leakage risk,because users’personal files are stored and controlled by their servers.To address the above problems,we propose a peer-to-peer(P2P)file management system based on IPFS and Hyperledger Fabric,named as FileWallet,which can serve as a personal wallet for individual users or organizations to store and share their files in a secure manner.In FileWallet,the users form a P2P network and a Fabric network,where P2P network builds the connections and distributed storage network and the Fabric network sustains consistent blockchain ledgers to record file operation related transactions.In our FileWallet,the storage and communication costs are mitigated in the decentralized design,and the file owner can fully control the access permission of the file to preserve the file privacy.The design of the system architecture,main functionalities,and system implementations are presented in this paper.The performance of the system is evaluated through experiments,and the experimental results show its wide applicability and scalability.

关键词： Blockchain file sharing IPFS

Uncover rock-climbing fish's secret of balancing tight adhesion and fast sliding for bioinspired robots

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National Science Review 2023年第8期10卷 222-231页

作者： Wenjun Tan chuang zhang Ruiqian Wang Yuanyuan Fu Qin Chen Yongliang Yang Wenxue Wang Mingjun zhang Ning Xi Lianqing Liu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Histology and Embryology Basic Medical CollegeChina Medical University Chengdu Institute of Biology Chinese Academy of Sciences Department of Biomedical Engineering School of Medicine Tsinghua University 7. Emerging Technologies InstituteDepartment of Industrial and Manufacturing Systems EngineeringUniversity of Hong Kong

The underlying principle of the unique dynamic adaptive adhesion capability of a rock-climbing fish(Beaufortia kweichowensis) that can resist a pull-off force of 1000 times its weight while achieving simultaneous fast sliding(7.83 body lengths per second(BL/S)) remains a mystery in the literature. This adhesion-sliding ability has long been sought for underwater robots. However, strong surface adhesion and fast sliding appear to contradict each other due to the need for high surface contact stress. The skillfully balanced mechanism of the tight surface adhesion and fast sliding of the rock-climbing fish is disclosed in this work. The Stefan force(0.1 mN/mm2) generated by micro-setae on pectoral fins and ventral fins leads to a 70 N/m2adhesion force by conforming the overall body of the fish to a surface to form a sealing chamber. The pull-off force is neutralized simultaneously due to the negative pressure caused by the volumetric change of the chamber. The rock-climbing fish’s micro-setae hydrodynamic interaction and sealing suction cup work cohesively to contribute to low friction and high pull-off-force resistance and can therefore slide rapidly while clinging to the surface. Inspired by this unique mechanism, an underwater robot is developed with incorporated structures that mimic the functionality of the rock-climbing fish via a micro-setae array attached to a soft self-adaptive chamber, a setup which demonstrates superiority over conventional structures in terms of balancing tight underwater adhesion and fast sliding.

关键词： bioinspired underwater adhesion crawling robots rock-climbing fish Stefan adhesion underwater robots

Sea fog detection based on unsupervised domain adaptation

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Chinese Journal of Aeronautics 2022年第4期35卷 415-425页

作者： Mengqiu XU Ming WU Jun GUO chuang zhang Yubo WANG Zhanyu MA School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China International School Beijing University of Posts and TelecommunicationsBeijing 100876China

Sea fog detection with remote sensing images is a challenging task. Driven by the different image characteristics between fog and other types of clouds, such as textures and colors, it can be achieved by using image processing methods. Currently, most of the available methods are datadriven and relying on manual annotations. However, because few meteorological observations and buoys over the sea can be realized, obtaining visibility information to help the annotations is difficult. Considering the feasibility of obtaining abundant visible information over the land and the similarity between land fog and sea fog, we propose an unsupervised domain adaptation method to bridge the abundant labeled land fog data and the unlabeled sea fog data to realize the sea fog detection. We used a seeded region growing module to obtain pixel-level masks from roughlabels generated by the unsupervised domain adaptation model. Experimental results demonstrate that our proposed method achieves an accuracy of sea fog recognition up to 99.17%, which is nearly 3% higher than those vanilla methods.

关键词： Deep learning Sea fog detection Seeded region growing Transfer learning Unsupervised domain adaptation

Designing Unpowered Shoulder Complex Exoskeleton via Contralateral Drive for Self-rehabilitation of Post-stroke Hemiparesis

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Journal of Bionic Engineering 2023年第3期20卷 992-1007页

作者： Ning Li Tie Yang Yang Yang Wenyuan Chen Peng Yu chuang zhang Ning Xi Ying Zhao Wenxue Wang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China University of Chinese Academy of Sciences Beijing100049China Emerging Technologies Institute Department of Industrial and Manufacturing Systems EngineeringUniversity of Hong KongPokfulamHong Kong999077China Department of General Surgery Shengjing Hospital of China Medical UniversityShenyang110004China

Rehabilitation using exoskeleton robots can effectively remediate dysfunction and restore post-stroke survivors’ physical ability. However, low kinematic compatibility and poor self-participation of post-stroke patients in rehabilitation restrict the outcomes of exoskeleton-based therapy. The study presents an Unpowered Shoulder Complex Exoskeleton (USCE), consisting of Shoulder Girdle Mechanism (SGM), Ball-and-Socket Joint Mechanism (BSM), Gravity Compensating Mechanism (GCM) and Adjustable Alignment Design (AAD), to achieve self-rehabilitation of shoulder via energy transfer from the healthy upper limb to the affected counterpart of post-stroke hemiplegic patients. The SGM and AAD are designed to improve the kinematic compatibility by compensating for displacements of the glenohumeral joint with the adaptable size of USCE for different wearers. The BSM and GCM can transfer the body movement and energy from the healthy half of the body to the affected side without external energy input and enhance the self-participation with sick posture correction. The experimental results show that the USCE can provide high kinematic compatibility with 90.9% movement similarity between human and exoskeleton. Meanwhile, the motion ability of a post-stroke patient’s affected limb can be increased through energy transfer. It is expected that USCE can improve outcomes of home-based self-rehabilitation.

关键词： Unpowered exoskeleton Health-paralysis combination Energy transfer Self-rehabilitation Shoulder mechanism design

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

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Chinese Journal of Mechanical Engineering 2023年第5期36卷 280-290页

作者： Qing Xu Xuewu Lin Mengchi Cai Yu‑ang Guo chuang zhang Kai Li Keqiang Li Jianqiang Wang Dongpu Cao School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Horizon Information Technology Co. Ltd.Beijing 100096China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Dongfeng USharing Technology Co. Ltd.Wuhan 430000China Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L 3G1Canada

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data *** was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.

关键词： Intelligent transportation systems Joint detection and tracking Global correlation network End-to-end tracking

MSF-Net: A Multilevel Spatiotemporal Feature Fusion Network Combines Attention for Action Recognition

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Computer Systems Science & Engineering 2023年第11期47卷 1433-1449页

作者： Mengmeng Yan chuang zhang Jinqi Chu Haichao zhang Tao Ge Suting Chen School of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Key Laboratory of Meteorological Observation and Information Processing Nanjing210044China

An action recognition network that combines multi-level spatiotemporal feature fusion with an attention mechanism is proposed as a solution to the issues of single spatiotemporal feature scale extraction,information redundancy,and insufficient extraction of frequency domain information in channels in 3D convolutional neural networks.Firstly,based on 3D CNN,this paper designs a new multilevel spatiotemporal feature fusion(MSF)structure,which is embedded in the network model,mainly through multilevel spatiotemporal feature separation,splicing and fusion,to achieve the fusion of spatial perceptual fields and short-medium-long time series information at different scales with reduced network parameters;In the second step,a multi-frequency channel and spatiotemporal attention module(FSAM)is introduced to assign different frequency features and spatiotemporal features in the channels are assigned corresponding weights to reduce the information redundancy of the feature maps.Finally,we embed the proposed method into the R3D model,which replaced the 2D convolutional filters in the 2D Resnet with 3D convolutional filters and conduct extensive experimental validation on the small and medium-sized dataset UCF101 and the largesized dataset Kinetics-400.The findings revealed that our model increased the recognition accuracy on both datasets.Results on the UCF101 dataset,in particular,demonstrate that our model outperforms R3D in terms of a maximum recognition accuracy improvement of 7.2%while using 34.2%fewer parameters.The MSF and FSAM are migrated to another traditional 3D action recognition model named C3D for application testing.The test results based on UCF101 show that the recognition accuracy is improved by 8.9%,proving the strong generalization ability and universality of the method in this paper.

关键词： 3D convolutional neural network action recognition MSF FSAM

B2B情境中的社交媒体营销战略:研究现状与展望

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北京工商大学学报（社会科学版） 2024年第4期39卷 1-15页

作者：张闯郭乐大连理工大学经济管理学院辽宁大连116024 东北财经大学工商管理学院辽宁大连116025

随着社交媒体的广泛普及和影响力的不断攀升,从事B2B营销的企业亟待了解如何通过有效的社交媒体营销战略获取长久的竞争优势和实现价值最大化。B2B情境中的社交媒体营销战略问题也越来越受到学者们的关注,但目前该领域的研究呈现较为分... 详细信息

随着社交媒体的广泛普及和影响力的不断攀升,从事B2B营销的企业亟待了解如何通过有效的社交媒体营销战略获取长久的竞争优势和实现价值最大化。B2B情境中的社交媒体营销战略问题也越来越受到学者们的关注,但目前该领域的研究呈现较为分散的状态,缺乏对相关文献全面系统的梳理和归纳。通过对209篇中英文文献的回顾和解析,采用社交媒体使用的“前因—战略—结果”综述框架,剖析了B2B情境中社交媒体营销战略的研究现状。基于现有研究不足,未来研究应进一步探究企业营销目标与相关主体动机的协同作用、C端社交媒体用户的动机,重点关注社交销售战略、思想领导力营销战略、影响者营销战略和社交媒体组合战略,以及企业边界人员使用个人社交媒体对企业层面的影响和社交媒体营销战略的“阴暗面”等问题,以推动B2B情境中社交媒体营销战略研究的理论构建与深化。

关键词： B2B营销社交媒体社交媒体互动社交媒体营销营销战略

Spin-Enhanced C-C Coupling in CO_(2)Electroreduction with Oxide-Derived Copper

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CCS Chemistry 2023年第9期5卷 2046-2058页

作者： Jinjie Hao Shijie Xie Qing Huang Zijing Ding Hua Sheng chuang zhang Jiannian Yao Key Laboratory of Photochemistry Beijing National Laboratory for Molecular SciencesInstitute of ChemistryChinese Academy of SciencesBeijing 100190 University of Chinese Academy of Sciences Beijing 100049 Hefei National Laboratory for Physical Sciences at the Microscale University of Science and Technology of ChinaHefeiAnhui 230026

Electrocatalytic reduction of carbon dioxide(CO_(2))to multicarbon(C2+)products involves intricate multiple protons and electron transfer of C-C coupling,which is dictated by not only the intrinsic reactivity but also the spin states of electrons in the catalyst.Here,we observe spin-enhanced CO_(2)reduction(CO_(2)RR)electrocatalytic activity on an oxidederived copper(OD-Cu)catalyst due to the existence of a specific Cu*site that carried the magnetic moments.Due to the correlation of magnetic and catalytic properties in OD-Cu,the current density through the OD-Cu electrode increases by nearly 10%at 350 mT.The field strength and angle dependence of such magnetic field effect(MFE),together with the time-resolved measurements proved that it originated from the alignment of magnetic moments on Cu*sites.The MFE on the electrocatalytic process enabled an enhancement(up to 15%)of the CO_(2)RR Faradaic efficiency using the OD-Cu catalyst.Importantly,the enhancement was attributed to the spinantiparallel alignment of electrons to promote C-C coupling on asymmetric Cu*-Cu sites;consequently,the optimal bias was reduced by∼0.2 V under the magnetic field for C2 products with Faradaic efficiency>30%and selectivity>75%.Our work uncovers a new paradigmfor spin-enhanced catalysis applicable to a broad range of chemical reactions involving spin singlet products.

关键词： oxide-derived copper spin catalysis CO_(2)reduction reaction C-C coupling magnetic field effect