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Boosting aqueous nickel-zinc battery stability with bimetallic ion-integrated 1D metal-organic nanowires

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science China Chemistry 2025年

作者： Yihao Chen Jin Liang Yichun Su Guoyin Zhu Hengyue Xu Hongye Ding Guangxun Zhang Dianheng Yu Zizhou Shen Yizhou Zhang Wei Huang Huan Pang School of Chemistry and Chemical Engineering Yangzhou University key laboratory of Flexible Electronics (KLoFE) & Institute of Advanced Materials (IAM) School of Flexible Electronics (Future Technologies) Nanjing Tech University (NanjingTech) Institute of Advanced Materials and Flexible Electronics (IAMFE) School of Chemistry and Materials Science Nanjing University of Information Science & Technology Department of Chemistry Tsinghua University State key laboratory of Coordination Chemistry Nanjing University

Limited by low conductivity,designing cathode materials with excellent electron transfer efficiency for high-performance nickel-zinc batteries （NZBs） remains *** this study,a series of bimetallic metal-organic nanowires were fabricated by doping M ions in NiNTA,resulting in NiMNTA (M=Mg2+,Mn2+,Co2+,Zn2+,Ag+),and NixCo6-xNTA （x=1,2,3,4,5）.The proportions and types of metal ions can be modified without altering the initial coordination structure.X-ray absorption fine structure spectroscopy （XAFS） revealed that only M–O bonds,not metal bonds,were present in the *** performance of Ni2Co4NTA as the cathode for aqueous NZBs was significantly improved,achieving the maximum energy density of 279.17Wh kg-1with the high cycle stability （retention rate of 72.0%after 1,000 cycles）.The flexible Ni2Co4NTA//Zn@carbon cloth battery employing gel electrolyte also exhibits the excellent Coulombic efficiency of 96%after 350 *** approach shows considerable potential for designing one-dimensional materials with bimetallic modifications,providing new strategies for developing cathode materials for NZBs with high specific capacity and cycle stability.

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Deliberate design of MOF-based pre-catalyst rationalizing the structural reconstruction toward efficient oxygen evolution reaction

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science China Chemistry 2025年

作者： Yunhui Chen Meng Tian Zhongqi Zhang Guangzhao Wang Yan Guo Jing Wu Dawei Qi Zhenhua Yan Xiangyu Liu Xi Liu State key laboratory of High-efficiency Coal Utilization and Green Chemical Engineering College of Chemistry and Chemical EngineeringNingxia University key laboratory of Extraordinary Bond Engineering and Advanced Materials technology of Chongqing School of Electronic Information Engineering Yangtze Normal University Frontiers science Center for New Organic Matter Key Laboratory of Advanced Energy Materials Chemistry (Ministry of Education) State Key Laboratory of Advanced Chemical Power Sources College of Chemistry Nankai University School of Chemistry and Chemical Engineering In-situ Center for Physical Sciences Frontiers Science Center for Transformative Molecules Shanghai Jiao Tong University

Metal-organic frameworks （MOFs）,that have emerged as promising electrocatalysts in oxygen evolution reaction （OER）,usually undergo structural reconstruction to form metal hydroxides as active ***,it remains a great challenge to design and synthesize ideal MOF-based pre-catalysts for properly cognizing and utilizing the reconstruction process toward efficient OER ***,the well-defined MOF-808 is post-modified by 2-hydroxyphosphonoacetic acid （HPAA） to form a micro-adsorber that enables to capture of metal ions ***3+and Ni2+ions are synergistically anchored into the microadsorber,developing a Fe/Ni-containing pre-catalyst that can be dynamically electrooxidized to be an active and stable dual-site electrocatalyst denoted by R-MOF-HPAA@FeNi with abundant metal *** a result,R-MOF-HPAA@FeNi exhibits an excellent OER performance with a low overpotential of 272 mV and Tafel slope of 35.9 m V dec-1,as well as long-term stability over 300 h at 10 m A *** and theoretical calculations suggest the resulting catalyst mainly follows a lattice oxygen mechanism （LOM） of dual-metal-site mechanism （DMSM）.Owing to the chelating role of HPAA,the post-synthesized MOF-HPAA behaves as a trap to uniformly and firmly adhere Fe3+and Ni2+ions,leading to a functionalized MOF-based pre-catalyst which can be structurally evolved into atomic level-distributed OER electrocatalysts.

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Understanding the local structure and thermophysical behavior of Mg-La liquid alloys via machine learning potential

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 439-449页

作者： Jia Zhao Taixi Feng Guimin Lu National Engineering Research Center for Integrated Utilization of Salt Lake Resource East China University of Science and TechnologyShanghai 200237China Joint International laboratory for Potassium and Lithium Strategic Resources East China University of Science and TechnologyShanghai 200237China

The local structure and thermophysical behavior of Mg-La liquid alloys were in-depth understood using deep potential molecular dynamic(DPMD) simulation driven via machine learning to promote the development of Mg-La alloys. The robustness of the trained deep potential(DP) model was thoroughly evaluated through several aspects, including root-mean-square errors(RMSEs), energy and force data, and structural information comparison results;the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg-La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg-La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg-La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg-La liquid alloy at 1200 K were reliably evaluated,which provides new guidance for related studies.

关键词： magnesium-lanthanum liquid alloys local structure macroscopic properties thermodynamic behavior deep potential mo-lecular dynamic simulation

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A Mechanical Single-molecule Potentiometer Based on Foldamer

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Chemical Research in Chinese Universities 2021年第2期37卷 335-336页

作者： ZHAO Zhikai WANG Qingling XIANG Dong tianjin key laboratory of micro-scale optical information science and technology Institute of Modern OpticsNankai UniversityTianjin300350P.R.China

Molecular potentiometers that can indicate displacement-conductance relationship and predict the molecular conductance are of significant importance towards the real application of molecule-based devices but rarely developed so *** this end,Li et *** a single-molecule potentiometer based on ortho-pentaphenylene *** demonstrated that these pseudoelastic molecules can be stretched and compressed by the mechanical force of the Au electrode along with a dramatically variable conductance by up to two orders of magnitude,demonstrating the great potential as angstrom-scale single-molecule *** work has been published in Nature Communications,2021.

关键词： electrode displacement potential

Spatio-temporal patterns and climatic drivers of spring phenology in eight forest communities across the north-south transitional zone of China

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Journal of Geographical sciences 2025年第1期35卷 17-38页

作者： ZHU Wenbin LU Yu Institute of Geographic sciences and Natural Resources Research CAS School of Geographical science Nanjing University of Information Science & Technology

The Qinba Mountains are climatically and ecologically recognized as the northsouth transitional zone of *** of its phenology is critical for comprehending the response of vegetation to climatic *** retrieved the start of spring phenology （SOS）of eight forest communities from the MODIS products and adopted it as an indicator for spring *** analysis,partial correlation analysis,and Geo Detector were employed to reveal the spatio-temporal patterns and climatic drivers of *** results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of–0.473 d yr–*** SOS of most forests correlated negatively with air temperature （TEMP） and positively with precipitation （PRE）,suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,*** dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively *** response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.

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Point-voxel dual transformer for LiDAR 3D object detection

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Optoelectronics Letters 2025年

作者： Tong Jigang Yang Fanhang Yang Sen Du Shengzhi tianjin key laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot La-boratory， tianjin University of technology， 391 Binshui West Road Department of Electrical Engineering， Tshwane University of technology

Deep learning for point clouds faces the challenge of their inherent unordered nature， which makes traditional CNN-like methods not directly applicable. However， due to the inherent permutation invariance， transformers provide solutions to unordered points problems faced in LiDAR-based object detection. In this paper， a two-stage LiDAR 3D object detection framework is presented， namely Point-Voxel Dual Transformer (PV-DT3D)， which is a transformer-based method. In the proposed PV-DT3D， point-voxel fusion features are used for proposal refinement. Specifically， in the PV-DT3D， keypoints are sampled from entire point cloud scene and used to encode representative scene features via a proposal-aware voxel set abstraction module. Subsequently， following the generation of proposals by the region proposal networks (RPN)， the internal encoded keypoints are fed into dual transformer encoder-decoder architecture. In 3D object detection， the proposed PV-DT3D is the first to take advantage of both pointwise transformer and channel-wise architecture to capture contextual information from the perspective of spatial and channel dimensions. Experiments conducted on the highly competitive KITTI 3D car detection leaderboard show that， the PV-DT3D achieves superior detection accuracy among state-of-the-art point-voxel-based methods.

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The Internet of Things under Federated Learning:A Review of the Latest Advances and Applications

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Computers, Materials & Continua 2025年第1期82卷 1-39页

作者： Jinlong Wang Zhenyu Liu Xingtao Yang Min Li Zhihan Lyu School of information and Control Engineering Qingdao University of TechnologyQingdao260043China Anhui Province key laboratory of Intelligent Building&Building Energy Saving Anhui Jianzhu UniversityHefei230000China Department of Game Design Uppsala UniversityUppsala75310Sweden

With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application *** the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious *** learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data *** review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy *** this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy ***,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning *** also list various application scenarios of FL on the ***,we propose several open research challenges and possible solutions.

关键词： Federated learning Internet of Things sensors machine learning privacy security

Learning to see speckle in the weak laser field through multimode fiber

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Optoelectronics Letters 2025年

作者： JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the key laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer science and Engineering， tianjin University of technology

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model

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Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean

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Atmospheric and Oceanic science Letters 2025年第1期18卷 24-31页

作者： Tiantian Tang Jiaying He Huihang Sun Jingjia Luo Institute for Climate and Application Research(ICAR)/key laboratory of Meteorological Disaster Nanjing University of Information Science and TechnologyNanjingChina

A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 *** study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of information science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine *** sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep *** latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging *** excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the *** improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean *** results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.

关键词： Seasonal forecast Ocean data assimilation Marine heatwave Subsurface temperature

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