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Efficient ORR catalysts for zinc-air battery: Biomass-derived ultra-stable Co nanoparticles wrapped with graphitic layers via optimizing electron transfer

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Journal of Energy Chemistry 2022年第7期31卷 211-218,I0006页

作者： Yu Feng Kexin Song Wei Zhang Xinyan Zhou Seung Jo Yoo Jin-Gyu Kim Sifan Qiao Yugang Qi Xu Zou Zhongjun Chen Tingting Qin Nailin Yue Zizhun Wang Dabing Li Weitao Zheng State Key Laboratory of Automotive Simulation and Control School of Materials Science&EngineeringKey Laboratory of Automobile Materials MOEElectron Microscopy Centerand International Center of Future ScienceJilin UniversityJilin UniversityChangchun 130012JilinChina Division of Scientific Instrumentation&Management Korea Basic Science InstituteDaejeon 34133Republic of Korea Institute of High Energy Physics Chinese Academy of SciencesBeijing 100049China State Key Laboratory of Luminescence and Applications Changchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of SciencesChangchun 130033JilinChina

The poor stability of non-noble metal catalysts in oxygen reduction reaction(ORR) is a main bottleneck that limits their big-scale application in metal-air batteries. Herein, we construct a chainmail catalyst(Co-NC-AD) with outstanding stability, via the competitive complexation and post absorption strategy,consisting of highly graphitic layers wrapped uniform-size Co nanoparticles(Co-NPs). Experiments combined with density functional theory(DFT) calculations jointly confirmed that the electron transfer occurred from the inner Co-NPs to the external graphitic layers. It facilitated the adsorption process of oxygen molecules and the hybridization of the O-2 p and C-1 p orbitals, which accelerated the ORR reaction kinetics. Consequently, our prepared Co-NC-AD shows excellent ORR activity, offered with a more positive initial potential(E_(onset)= 0.95 V) and half-wave potential(E_(1/2)= 0.86 V). The remarkable stability and resistance of methanol poisoning are merited from the protection effect of stable graphitic layers. In addition, the high electrochemical performance of Co-NC-AD-based zinc-air battery demonstrates their potential for practical applications. Therefore, our work provides new ideas for the design of nanoconfined catalysts with high stability and activity.

关键词： Chainmail catalyst Graphitic layers Co nanoparticles Oxygen reduction reaction

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TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection

arXiv

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arXiv 2024年

作者： Chen, Jiankang Zhang, Tong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China

Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overconfidence when misclassifying OOD data as ID classes. In this study, we propose a new learning framework which leverage simple Jigsaw-based fake OOD data and rich semantic embeddings (‘anchors’) from the ChatGPT description of ID knowledge to help guide the training of the image encoder. The learning framework can be flexibly combined with existing post-hoc approaches to OOD detection, and extensive empirical evaluations on multiple OOD detection benchmarks demonstrate that rich textual representation of ID knowledge and fake OOD knowledge can well help train a visual encoder for OOD detection. With the learning framework, new state-of-the-art performance was achieved on all the benchmarks. The code is available at https://***/Cverchen/TagFog. Copyright © 2024, The Authors. All rights reserved.

关键词： Jigs

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Feature Rotation Invariance Learning for Point Cloud Analysis 10

Feature Rotation Invariance Learning for Point Cloud Analysi...

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10th IEEE Smart World Congress, SWC 2024

作者： Shi, Lu Cao, Qi Zhang, Guoqing Yi, Jin Huang, Yansen Cen, Yigang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer Science and Technology Visual Intellgence +X International Cooperation Joint Laboratory of Moe Beijing100044 China University of Glasgow The School of Computing Science 567739 Singapore Beijing Jiaotong University With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education The State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer and Information Technology Beijing100044 China Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. Ltd The College of Civil Engineering Guizhou550025 China

ISBN: (纸本)9798331520861

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks. © 2024 IEEE.

关键词： Contrastive Learning

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TexCIL: Text-Guided Continual Learning of Disease with Vision-Language Model

TexCIL: Text-Guided Continual Learning of Disease with Visio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wentao Zhang Defeng Zhao Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Current intelligent diagnostic systems often catastrophically forget old knowledge when learning new diseases only from the training dataset of the new diseases. Inspired by human learning of visual classes with the effective help of language, we propose a continual learning framework based on a pre-trained visual-language model (VLM) without storing any image of previously learned diseases. In this framework, textual prior knowledge of each new disease can be obtained by utilizing the frozen VLM’s text encoder, and then used to guide the visual learning of the new disease. This framework innovatively utilizes the textual prior knowledge of all previously learned diseases as out-of-distribution (OOD) information to help differentiate currently being-learned diseases from others. Extensive empirical evaluations on both medical and natural image datasets confirm the superiority of the proposed method over existing state-of-the-art methods in continual learning of new visual classes. The source code is available at https://***/OpenMedIA/TexCIL.

关键词： Continuing education Training Visualization Source coding Biological system modeling Benchmark testing Bioinformatics Medical diagnostic imaging Diseases

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Leveraging GCN and User Friendship for Next POI Recommendation 22

Leveraging GCN and User Friendship for Next POI Recommendati...

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Proceedings of the 2022 5th International Conference on Machine Learning and Natural Language Processing

作者： Cheng Fu Wei Zhou Junhao Wen Xiangxing Guo Yuhang Chang School of Bigdata and Software Engineering Chongqing University China and Key Laboratory of Dependable Service Computing in Cyber Physical Society Moe China

ISBN: (纸本)9781450399067

The next POI (point of interest) recommendation aims to explore the behaviour patterns from users’ historical check-in records and recommend the next location. However, solving the data sparsity problem and improving the model recommendation performance are still considerable challenges in POI recommendations. This paper proposes a GFUC model consisting of the GCN (Graph Convolutional Networks) and FUC (Friendship of User Context) modules. The GCN module integrates weighted graph convolutional networks to obtain the best representation of users and POIs. The FUC module divides user check-in records with multiple fine-grain manners. Then it incorporates the user friendship and context information, significantly improving the model’s performance while solving the data sparsity problem. More specifically, experimental results show that our proposed GFUC model improves the performance of POI recommendations by more than 10% on both Yelp and Gowalla datasets with the evaluation metrics Precision@10.

关键词： Collaborative Filtering

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Spectral Galerkin Approximation of Fractional Optimal Control Problems with Fractional Laplacian

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Advances in Applied Mathematics and Mechanics 2023年第6期15卷 1631-1654页

作者： Jiaqi Zhang Yin Yang Zhaojie Zhou School of Mathematics and Computational Science Xiangtan UniversityHunan Key Laboratory for Computation and Simulation in Science and EngineeringKey Laboratory of Intelligent Computing and Information Processing of Ministry of EducationXiangtan 411105HunanChina School of Mathematics and Computational Science Xiangtan UniversityNational Center for Applied Mathematics in HunanHunan International Scientific and Technological Innovation Cooperation Base of Computational ScienceXiangtan 411105HunanChina School of Mathematics and Statistics Shandong Normal UniversityJinan 250014ShandongChina

In this paper spectral Galerkin approximation of optimal control problem governed by fractional elliptic equation is *** deal with the nonlocality of fractional Laplacian operator the Caffarelli-Silvestre extension is *** first order optimality condition of the extended optimal control problem is derived.A spectral Galerkin discrete scheme for the extended problem based on weighted Laguerre polynomials is developed.A priori error estimates for the spectral Galerkin discrete scheme is *** experiments are presented to show the effectiveness of our methods and to verify the theoretical findings.

关键词： Fractional Laplacian optimal control problem Caffarelli-Silvestre extension weighted Laguerre polynomials

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Satellite Federated Fine-Tuning for Foundation Models: Architecture Design and System Optimization

Satellite Federated Fine-Tuning for Foundation Models: Archi...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhu, Yan Yang, Peng Zhu, Jingyang Wen, Dingzhu Wang, Ting Zhou, Yong Shi, Yuanming Jiang, Chunxiao East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China

ISBN: (纸本)9798350351255

With the surge in the number of low earth orbit (LEO) satellites, continuous research has emerged on using satellite data to train artificial intelligence models. On one hand, traditional centralized training on the ground is not feasible due to privacy concerns and limited bandwidth for downloading raw satellite data. On the other hand, due to the limited energy and computational capability of satellites, training directly on satellites suffers from prolonged latency, especially for large models. To alleviate these issues, we propose a novel satellite-ground collaborative federated fine-tuning architecture, where ground stations (GSs) and satellites collaboratively train a global model without the need for data downloads. In this proposed architecture, satellites serve as edge devices and the ground server serves as a coordinator. However, the short satellite-ground communication windows caused by the high mobility of satellites and the substantial intra-orbit data transmission bring special challenges to the transmission process of federated edge learning. To tackle these challenges, we carefully design the satellite-ground collaborative fine-tuning architecture and utilize an optimized ring all-reduce algorithm and network flow algorithm to enhance the intra-orbit and ground-satellite transmissions, respectively. Experimental results demonstrate that our proposed architecture significantly reduces the training time by 40% compared to training solely on satellite. © 2024 IEEE.

关键词： Satellite ground stations

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High-sensitive and fast MXene/silicon photodetector forsingle-pixel X-ray imaging

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InfoMat 2024年第9期6卷 91-102页

作者： Yance Chen Yue Dai Srikrishna Chanakya Bodepudi Xinyu Liu Yuan Ma Shiyu Xing Dawei Di Feng Tian Xin Ming Yingjun Liu Kai Pang Fei Xue Yunyan Zhang Zexin Yu Yaping Dan Oleksiy VPenkov Yishu Zhang Dianyu Qi Wenzhang Fang Yang Xu Chao Gao College of Integrated Circuits ZJU-Hangzhou Global Scientific and Technological Innovation CenterState Key Laboratory of Silicon MaterialsZhejiang UniversityHangzhouPeople's Republic of China MOE Key Laboratory of Macromolecular Synthesis and Functionalization Department of Polymer Science and EngineeringInternational Research Center for X PolymersZhejiang UniversityHangzhouPeople's Republic of China State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZhejiang UniversityHangzhouPeople's Republic of China Institute for Manufacturing Technologies of Ceramic Components and Composites(IMTCCC) University of StuttgartStuttgartGermany University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong UniversityShanghaiPeople's Republic of China ZJU-UIUC Institute International CampusZhejiang UniversityHainingPeople's Republic of China Zhejiang ICsprout Semiconductor Co. LtdHangzhouPeople's Republic of China

The demand for high-performance X-ray detectors leads to material innovationfor efficient photoelectric conversion and carrier transfer. However, currentX-ray detectors are often susceptible to chemical and irradiation instability,complex fabrication processes, hazardous components, and difficult ***, we investigate a two-dimensional (2D) material with a relativelylow atomic number, Ti_(3)C_(2)T_(x) MXenes, and single crystal silicon for X-ray detectionand single-pixel imaging (SPI). We fabricate a Ti_(3)C_(2)T_(x) MXene/Si X-raydetector demonstrating remarkable optoelectronic performance. This detectorexhibits a sensitivity of 1.2 × 10^(7) μC Gyair^(-1) cm^(-2), a fast response speed with arise time of 31 μs, and an incredibly low detection limit of 2.85 nGyair s^(-1).These superior performances are attributed to the unique charge couplingbehavior under X-ray irradiation via intrinsic polaron formation. The deviceremains stable even after 50 continuous hours of high-dose X-ray *** device fabrication process is compatible with silicon-based semiconductortechnology. Our work suggests new directions for eco-friendly X-ray detectorsand low-radiation imaging system.

关键词： MXene single-pixel imaging X-ray detector

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A Diffuse-Interface Lattice Boltzmann Method for the Dendritic Growth with Thermosolutal Convection

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Communications in Computational Physics 2023年第4期33卷 1164-1188页

作者： Chengjie Zhan Zhenhua Chai Baochang Shi Ping Jiang Shaoning Geng Dongke Sun School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan430074P.R.China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and TechnologyWuhan 430074P.R.China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan430074P.R.China The State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074P.R.China School of Mechanical Engineering Southeast UniversityNanjing 211189P.R.China

In this work,we proposed a diffuse-interface model for the dendritic growth with thermosolutal *** this model,the sharp boundary between the fluid and solid dendrite is firstly replaced by a thin but nonzero thickness diffuse interface,which is described by the order parameter,and the diffuse-interface based governing equations for the dendritic growth are *** solve the model for the dendritic growth with thermosolutal convection,we also developed a diffuse-interface multirelaxation-time lattice Boltzmann(LB)*** this method,the order parameter in the phase-field equation is combined into the force caused by the fluid-solid interaction,and the treatment on the complex fluid-solid interface can be *** addition,four LB models are designed for the phase-field equation,concentration equation,temperature equation and the Navier-Stokes equations in a unified ***,we performed some simulations of the dendritic growth to test the present diffuse-interface LB method,and found that the numerical results are in good agreements with some previous works.

关键词： Dendritic growth diffuse interface lattice Boltzmann method phase-field method.

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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