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Boosting the Efficiency and Mechanical Stability of Organic Solar Cells Through a Polymer Acceptor by Reducing the Elastic Modulus

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advanced Energy Materials 2025年

作者： Wang, Yan Yu, Han Zhao, Dan Liu, Wei Liu, Baoze Wu, Xin Gao, Danpeng Zhang, Dong Zhang, Shoufeng Sun, Xianglang Zhang, Chunlei Zhao, Chaoyue Fu, Yuchen Song, Wei Gong, Shaokuan Fu, Yuang Kwok, Chung Hang Ge, Ziyi Lu, Xinhui Chen, Xihan Xiao, Shuang Wong, Wai-Yeung Chai, Yu Yan, He Zhu, Zonglong Department of Chemistry City University of Hong Kong Kowloon999077 Hong Kong Department of Chemistry Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials Energy Institute and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration & Reconstruction Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University Hung Hom 999077 Hong Kong Hong Kong University of Science and Technology-Shenzhen Research Institute No. 9 Yuexing 1st RD Hi-tech Park Nanshan Guangdong Shenzhen518057 China School of Automation Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Physics City University of Hong Kong Kowloon999077 Hong Kong Zhejiang Engineering Research Center for Energy Optoelectronic Materials and Devices Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo315201 China Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China Department of Physics The Chinese University of Hong Kong Shatin999077 Hong Kong Center for Advanced Material Diagnostic Technology and College of Engineering Physics Shenzhen Technology University Guangdong Shenzhen518118 China Shenzhen Research Institute City University of Hong Kong Shenzhen518057 China

Organic solar cells (OSCs) are regarded as one of the most promising flexible power sources due to their lightweight and flexible properties, with the improvement of photovoltaic and mechanical performance. To improve the current density and power conversion efficiency (PCE), mPh4F-TS (TS) and PYSe2F-T (PA) are introduced into the binary host, PM6/mPh4F-TT (PM6/TT) as third components. It is demonstrated that the corresponding ternary devices, in both rigid and flexible devices, achieved superior efficiencies (19.6%/17.7% for PM6/TT+TS, and 19.2%/17.4% for PM6/TT+PA) outperform the binary counterparts (18.3%/16.4%). However, distinct differences in mechanical performance are observed between the polymer acceptor (PA) and small-molecular acceptor (TS). The PM6/TT+PA significantly improved the mechanical stability of flexible devices with a lower elastic modulus of 3.6 GPa, while the PM6/TT+TS resulted in the opposite effect with a higher elastic modulus of 5.5 GPa. Through in-depth investigation, a clear correlation between the elastic modulus, crack density, and mechanical stability of the active layer blends is successfully established, revealing the key role of reducing the elastic modulus in enhancing the mechanical stability of flexible OSCs. This study provides important guidance for the development of flexible photovoltaic devices with both high efficiency and mechanical robustness. © 2025 The Author(s). advanced Energy Materials published by Wiley-VCH GmbH.

关键词： Organic solar cells

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Thermometry of photosensitive and optically induced electrokinetics chips

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Microsystems & Nanoengineering 2018年第1期4卷 190-198页

作者： Feifei Wang Lianqing Liu Gongxin Li Pan Li Yangdong Wen Guanglie Zhang Yuechao Wang Gwo-Bin Lee Wen Jung Li State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences110016 ShenyangChina University of Chinese Academy of Sciences 100049 BeijingChina Shenzhen Academy of Robotics 518057 ShenzhenChina Key Laboratory of Advanced Process Control for Light Industry of the Ministry of Education Institute of AutomationJiangnan University214122 WuxiChina Department of Power Mechanical Engineering National Tsing Hua University30013 HsinchuTaiwan Department of Mechanical and Biomedical Engineering City University of Hong KongKowloon TongHong KongChina

Optically induced electrokinetics(OEK)-based technologies,which integrate the high-resolution dynamic addressability of optical tweezers and the high-throughput capability of electrokinetic forces,have been widely used to manipulate,assemble,and separate biological and non-biological entities in parallel on scales ranging from micrometers to ***,simultaneously introducing optical and electrical energy into an OEK chip may induce a problematic temperature increase,which poses the potential risk of exceeding physiological conditions and thus inducing variations in cell behavior or activity or even irreversible cell damage during ***,we systematically measure the temperature distribution and changes in an OEK chip arising from the projected images and applied alternating current(AC)voltage using an infrared *** have found that the average temperature of a projected area is influenced by the light color,total illumination area,ratio of lighted regions to the total controlled areas,and amplitude of the AC *** an example,optically induced thermocapillary flow is triggered by the light image-induced temperature gradient on a photosensitive substrate to realize fluidic hydrogel *** studies show that the projected light pattern needs to be properly designed to satisfy specific application requirements,especially for applications related to cell manipulation and assembly.

关键词： exceeding illumination manipulation

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A registration-aided domain adaptation network for 3D Point cloud based place recognition

arXiv

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

arXiv

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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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A novel obstacle collision avoidance strategy for redundant manipulator using swivel motion control

A novel obstacle collision avoidance strategy for redundant ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Guo, Kunlin Jiang, Yiming Wu, Xinyu Yang, Chenguang South China University of Technology College of Automation Science and Engineering Guangzhou Guangdong Province China Hunan University College of Electrical and Information Engineering Changsha Hunan Province China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen Guangdong Province China University of the West of England Bristol Robotics Laboratory BristolBS16 1QY United Kingdom

ISBN: (纸本)9781728163215

The research of humanoid 7-DoF redundant robotic manipulator has attracted increasing attention, due to its importance of using the redundancy to accomplish sub-tasks. In this paper, a novel obstacle collision avoidance strategy is proposed for the redundant manipulator. At first, a controller of swivel angle defined by a plane consists of the robot's shoulder, elbow and wrist joints is designed. Then, a null space based controller is developed to avoid a moving obstacle by controlling the swivel angle's motion. The designed swivel motion controller is not only to guarantee the end-effector tacking the desired trajectory precisely but also to accomplish obstacle avoidance task in the sub-space with the virtual repulsive force pushing from the obstacle to the swivel joints. The feasibility of the proposed control strategy is proved by simulation experiments based on a 7-DoF redundant robot. © 2019 IEEE.

关键词： Redundant manipulators

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Exploiting offset-guided network for pose estimation and tracking

arXiv

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arXiv 2019年

作者： Zhang, Rui Zhu, Zheng Li, Peng Wu, Rui Guo, Chaoxu Huang, Guan Xia, Hailun Bejing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks Beijing Institute of Automation Chinese Academy of Sciences Beijing China Horizon Robotics Beijing

Human pose estimation has witnessed a significant advance thanks to the development of deep learning. Recent human pose estimation approaches tend to directly predict the location heatmaps, which causes quantization errors and inevitably deteriorates the performance within the reduced network output. Aim at solving it, we revisit the heatmap-offset aggregation method and propose the Offset-guided Network (OGN) with an intuitive but effective fusion strategy for both two-stages pose estimation and Mask R-CNN. For two-stages pose estimation, a greedy box generation strategy is also proposed to keep more necessary candidates while performing person detection. For mask R-CNN, ratio-consistent is adopted to improve the generalization ability of the network. State-of-the-art results on COCO and PoseTrack dataset verify the effectiveness of our offset-guided pose estimation and tracking. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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A Generative Model of Underwater Images for Active Landmark Detection and Docking

A Generative Model of Underwater Images for Active Landmark ...

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2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shuang Liu Mete Ozay Hongli Xu Yang Lin Takayuki Okatani State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Graduate School of Information Sciences Tohoku University Sendai Miyagi Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan

Underwater active landmarks (UALs) are widely used for short-range underwater navigation in underwater robotics tasks. Detection of UALs is challenging due to large variance of underwater illumination, water quality and change of camera viewpoint. Moreover, improvement of detection accuracy relies upon statistical diversity of images used to train detection models. We propose a generative adversarial network, called Tank-to-field GAN (T2FGAN), to learn generative models of underwater images, and use the learned models for data augmentation to improve detection accuracy. To this end, first a T2FGAN is trained using images of UALs captured in a tank. Then, the learned model of the T2FGAN is used to generate images of UALs according to different water quality, illumination, pose and landmark configurations (WIPCs). In experimental analyses, we first explore statistical properties of images of UALs generated by T2FGAN under various WIPCs for active landmark detection. Then, we use the generated images for training detection algorithms. Experimental results show that training detection algorithms using the generated images can improve detection accuracy. In field experiments, underwater docking tasks are successfully performed in a lake by employing detection models trained on datasets generated by T2FGAN.

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Corrections to “Outlier-Robust Student's-t-Based IMM-VB Localization for Manned Aircraft Using TDOA Measurements” [Jun 20 1646-1658]

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IEEE/ASME Transactions on Mechatronics 2020年第4期25卷 2139-2139页

作者： Wonkeun Youn Yulong Huang Hyun Myung Korea Aerospace Research Institute Daejeon South Korea Urban Robotics Laboratory School of Electrical Engineering KI-Robotics KI-AI Korea Advanced Institute of Science and Technology Yuseong-gu South Korea Department of Automation Harbin Engineering University Harbin China

Presents corrections to author information for the above named paper.

关键词： Kalman filters Bayes methods Time of arrival estimation Target tracking

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A robust and accurate SLAM algorithm for omni-directional mobile robots based on a novel 2.5D lidar device 13

A robust and accurate SLAM algorithm for omni-directional mo...

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13th IEEE Conference on Industrial Electronics and Applications, ICIEA 2018

作者： Yang, Yu Yang, Guilin Tian, Yingzhong Zheng, Tianjiang Li, Long Wang, Zhenhua Ningbo Institute of Materials Technology and Engineering CAS Ningbo315201 China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology China School of Mechatronic Engineering and Automation Shanghai University Shanghai China Jiangsu Provincial Key Laboratory of Advanced Robotics Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou215123 China

ISBN: (纸本)9781538637579

Lidars are commonly employed in mobile robot SLAM applications due to their high stability and high measuring accuracy. For a conventional 2D Lidar, only the planar points can be detected in once scan, which limits the robustness of SLAM algorithms. Omni-directional mobile robots with powered caster wheels have 3-DOF planar motion capabilities so that they are widely deployed in narrowly constrained environment. This paper presents a novel design of a 2.5D Lidar device, in which a 2D Lidar is mounted onto a 1-DOF liner stage that moves along the vertical direction. As a result, the 2.5D Lidar device can also perform vertical scanning within the motion range of the liner stage. Consequently, a robust SLAM algorithm based on the 2.5D Lidar device is proposed for the omni-directional mobile robot, where a frame-to-frame scan matching using ICP algorithm is employed to improve the robustness and accuracy of the scan matching. As the 2.5D points cloud is actually located in a belt-shaped space, the map can be finally constructed in a 2D mode to reduce the computational complexity. In addition, when an incorrect matching occurs, the odometry information of the mobile robot will be employed to further improve the robustness and accuracy of the scan matching algorithm. The effectiveness of the proposed approach is validated through computer simulations. © 2018 IEEE.

关键词： Optical radar

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A Novel Obstacle Collision Avoidance Strategy for Redundant Manipulator using Swivel Motion Control

A Novel Obstacle Collision Avoidance Strategy for Redundant ...

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IEEE International Conference on robotics and Biomimetics

作者： Kunlin Guo Yiming Jiang Xinyu Wu Chenguang Yang College of Automation Science and Engineering South China University of Technology Guangzhou China College of Electrical and Information Engineering Hunan University Changsha China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Bristol Robotics Laboratory University of the West of England Bristol UK

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