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Co-regulation effect of solvation and interface of pyridine derivative enabling highly reversible zinc anode

材料科学技术（英文版） 2025年第1期204卷 1-9页

作者： Binrui Xu Guangbin Wang Yong Liu Quanan Li Fengzhang Ren Jianmin Ma School of Information Engineering Henan University of Science and TechnologyLuoyang 471023China School of Materials Science and Engineering Provincial and Ministerial Coconstruction of Collaborative Innovation Center for Non-ferrous Metal New Materials and Advanced Processing TechnologyHenan University of Science and TechnologyLuoyang 471023China Longmen laboratory Luoyang 471000China School of Chemistry Tiangong UniversityTianjin 300387China

The poor reversibility and stability of Zn anodes greatly restrict the practical application of aqueous Zn-ion batteries(AZIBs),resulting from the uncontrollable dendrite growth and H2O-induced side reactions during *** additive modification is considered one of the most effective and simplest methods for solving the aforementioned ***,the pyridine derivatives(PD)including 2,4-dihydroxypyridine(2,4-DHP),2,3-dihydroxypyridine(2,3-DHP),and 2-hydroxypyrdine(2-DHP),were em-ployed as novel electrolyte additives in ZnSO4 *** density functional theory calculation and experimental findings demonstrated that the incorporation of PD additives into the electrolyte effectively modulates the solvation structure of hydrated Zn ions,thereby suppressing side reactions in ***-ditionally,the adsorption of PD molecules on the zinc anode surface contributed to uniform Zn deposi-tion and dendrite growth ***,a 2,4-DHP-modified Zn/Zn symmetrical cell achieved an extremely long cyclic stability up to 5650 h at 1 mA ***,the Zn/NH4V4O10 full cell with 2,4-DHP-containing electrolyte exhibited an outstanding initial capacity of 204 mAh g-1,with a no-table capacity retention of 79％after 1000 cycles at 5 A ***,this study expands the selection of electrolyte additives for AZIBs,and the working mechanism of PD additives provides new insights for electrolyte modification enabling highly reversible zinc anode.

关键词： Zn anode Pyridine derivative Electrolyte additive Solvation regulation Interface modification

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Modulation of inactive Li_(2)O via iodinated MOF nanocapsules interfacial transformation engineering for high-performance solid electrolyte interphase

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Journal of Energy Chemistry 2025年第5期104卷 482-493页

作者： Xingxing Zhang Qingmei Su Gaohui Du Bingshe Xu Xuehan Hou Xiaowei Yang Weihao Shi Zhuo Chen Yang Shi Yujie Lv Wenhuan Huang School of Materials Science&Engineering Shaanxi University of Science and TechnologyXi’an 710021ShaanxiChina Key Laboratory of Chemical Additives for China National Light Industry College of Chemistry and Chemical EngineeringShaanxi University of Science and TechnologyXi’an 710021ShaanxiChina Materials Institute of Atomic and Molecular Science School of Physics&Information ScienceShaanxi University of Science and TechnologyXi’an 710021ShaanxiChina School of Chemistry and Chemical Engineering Shanghai Jiaotong UniversityShanghai 200240China

Lithium(Li)deposition and nucleation at solid electrolyte interphase(SEI)is the main origin for the capacity decay in Li metal batteries(LMBs).SEI conversion with enhanced electrochemical and mechanical properties is an effective approach to achieve uniform nucleation of Li^(+)and stabilize the lithium metal ***,complex interfacial reaction mechanisms and interface compatibility issues hinder the development of SEI conversion strategies for stabilizing lithium metal ***,we presented the release of I_(3)^(-)in–NH_(2)-modified metal–organic frameworks for a Li metal surface SEI phase conversion ***–NH_(2)group in MOF pores induced the formation of I_(3)^(-)from I_(2),which was further spontaneously reacted with inactive Li_(2)O transforming into high-performance LiI and LiIO_(3)***,theoretical calculation provided deeply insight into the unique reconstructed interfacial formation and electrochemical mechanism of rich LiI and LiIO_(3)*** a result,the Li^(+)deposition and nucleation were improved,facilitating the transport kinetics of Li^(+)and inhibiting the growth of lithium *** assembled solid-state Li||LiFePO_(4)full cells exhibited superior long-term stability of 800 cycles and high Coulombic efficiency(>99%),Li||LiNi_(0.8)Co_(0.1)Mn_(0.1)O_(2)pouch cell also displayed superior practical performance over 200 cycles at 2 C,high loading of 5 mg cm^(-2)and safety *** innovative SEI design strategy promotes the development of high-performance solid-state Li metal batteries.

关键词： Solid electrolyte interphase(SEI) SEI phase conversion MOF Nano-capsule Solid-state electrolytes Li metal battery

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Geometry-Based Stochastic MIMO Channel Model for Near-Field and Far-Field Scenarios of Integrated Sensing and Communications

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IEEE Transactions on Vehicular technology 2025年第5期74卷 6928-6940页

作者： Jin, Yunwei He, Ruisi Ai, Bo Yuan, Yuan Niu, Yong Zhang, Haoxiang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation School of Electronics and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Ministry of Industry and Information Technology China Academy of Industrial Internet Beijing100804 China

Integrated sensing and communication (ISAC) has become a promising technology in current sixth-generation (6G) wireless communications due to its ability to support both communication and sensing. Existing works regarding ISAC channels mainly focus on far-field propagation conditions based on plane wave assumption. However, with the deployment of ultra-massive multiple-input multiple-output (MIMO), the far-field assumption may no longer hold, as the communication and sensing propagation distances distances may be comparable to the array aperture size. To address this issue, we propose a three-dimensional MIMO channel model that simultaneously considers near-field and far-field propagation conditions in ISAC environments. Additionally, we distinguish the communication and sensing propagation environments by employing different effective scatterer distributions. In the model, the sensing channel is divided into target sensing and environmental sensing components, whereas the communication channel is divided into line-of-sight and non-line-of-sight components. The weighted sum of each component in communication and sensing channels results in accurate channel representations. Based on the proposed model, we derive and thoroughly investigate space-time-frequency correlation function, normalized absolute error function, and channel capacity. A key observation is that the channel correlation strongly depends on scatterer distribution. In certain conditions, the sensing channel can effectively capture the behavior of the communication channel. Error function results indicate that the proposed channel model achieves fairly high accuracy in ultra-massive MIMO scenarios. These findings provide essential support for the development of ISAC systems in future 6G wireless communications. © 1967-2012 IEEE.

关键词： Stochastic systems

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BayProNet: Advancing Few-Shot Learning with Bayesian Prototype Network 24

BayProNet: Advancing Few-Shot Learning with Bayesian Prototy...

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8th International Conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Gao, Cheng Lang Miao, Jiang Hui, Chen Hong Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology HuNan ChangSha China

ISBN: (纸本)9798400718182

Text classification is a challenging task in the field of Natural Language Processing (NLP), and significant progress has been made using deep learning methods. Traditional deep-learning approaches for text classification require large amounts of data, thereby performing suboptimally in few-shot data scenarios. To alleviate this bottleneck, this study proposes a Bayesian Prototypical Network with Hybrid Attention (BayProNet) to improve the accuracy of few-shot text classification. Experimental results on the Clinc150 dataset show that the model significantly outperforms existing SOTA baseline models in terms of classification accuracy and also exhibits strong stability when handling noisy data. This research validates the effectiveness and superiority of Bayesian Prototypical Networks in few-shot text classification tasks. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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KCl acts as a flux to assist the growth of sub-millimeter-scale metallic 2D non-layered molybdenum dioxide

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Rare Metals 2025年第1期44卷 404-416页

作者： Li-Ying Deng Qing Zhang Wang-Yang Li Xiao-Yuan Ye Yi-Fan Zhao Shen-Zhong Chen Yu-Lan Wang Xing-Hui Wang Hui-Peng Chen Zhi-Yang Yu Qun Yan Shu-Ying Cheng Tai-Liang Guo Wen-Ping Hu Feng Ding Jie Sun National and Local United Engineering Laboratory of Flat Panel Display Technology College of Physics and Information EngineeringFuzhou UniversityFuzhou350100China Fujian Key Laboratory of Agricultural Information Sensing Technology College of Mechanical and Electrical EngineeringFujian Agriculture and Forestry UniversityFuzhou350002China Key Laboratory of Organic Integrated Circuit Department of ChemistrySchool of ScienceMinistry of Education and Tianjin Key Laboratory of Molecular Optoelectronic SciencesTianjin UniversityTianjin300072China Collaborative Innovation Center of Chemical Science and Engineering(Tianjin) Tianjin300072China College of Physics and Information Engineering Institute of Micro-Nano Devices and Solar CellsFuzhou UniversityFuzhou350108China State Key Laboratory of Photocatalysis on Energy and Environment College of ChemistryFuzhou UniversityFuzhou350116China Faculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518000China

Two-dimensional(2D)metal oxides(2DMOs),such as MoO_(2),have made impressive strides in recent years,and their applicability in a number of fields such as electronic devices,optoelectronic devices and lasers has been ***,2DMOs present challenges in their synthesis using conventional methods due to their non-van der Waals *** report that KCl acts as a flux to prepare large-area 2DMOs with sub-millimeter *** systematically investigate the effects of temperature,homogeneous time and cooling rate on the products in the flux method,demonstrating that in this reaction a saturated homogenous solution is obtained upon the melting of the salt and ***,the cooling rate was adjusted to regulate the thickness of the target crystals,leading to the precipitation of 2D non-layered material from the supersaturated solution;by applying this method,the highly crystalline non-layered 2D MoO_(2)flakes with so far the largest lateral size of up to sub-millimeter scale(~464μm)were *** studies have revealed that the 2D MoO_(2)features metallic properties,with an excellent sheet resistance as low as 99Ω·square^(-1 )at room temperature,and exhibits a property of charge density wave in the measurement of resistivity as a function of temperature.

关键词： 2D non-layered materials Metallic Metal oxide Thickness modulation Flux method

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Tree comprehensive diversity-based pruned deep forest

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International Journal of Machine Learning and Cybernetics 2025年 1-20页

作者： Ding, Jiaman Jiang, Xiangyu Jia, Lianyin Fu, Xiaodong Jiang, Ying Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Artificial Intelligence Key Laboratory of Yunnan Province Kunming University of Science and Technology Yunnan Kunming650500 China

Deep Forest (DF) is proposed as a deep learning model based on non-differentiable modules, which extends the deep learning paradigm by incorporating non-neural network structures and has demonstrated remarkable performance in several fields. DF utilizes a cascade structure where each layer contains one or more sets of random forests. However, some of these forests may include decision trees with high redundancy and suboptimal performance. Additionally, the feature representations in DF are composed of prediction class probability vectors, and these prediction-based representations require the retention of a large amount of information about the forest models for subsequent testing. These two characteristics of DF make it prone to issues related to high time, memory, and storage costs, and may even lead to performance degradation. To address these challenges, we design a novel pruning strategy aimed at optimizing random forests in cascade layers and propose a new deep forest model called Tree Comprehensive Diversity-based Pruned Deep Forest (TCDPDF). Unlike most methods that rely on model prediction results to measure diversity, we explore a diversity measure based on the morphological structure of decision trees. Furthermore, we propose a tree comprehensive diversity measure for the pruning strategy by balancing both structural diversity and prediction accuracy of decision trees. We conduct experiments on 12 public datasets, and the results demonstrate that TCDPDF achieves competitive performance, significantly reduces the model’s high computational complexity, and further enhances its generalization ability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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Computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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Research on the Detection Methods for Changes in the Mangroves of Dongzhaigang, Hainan from 2019 to 2023 24

Research on the Detection Methods for Changes in the Mangrov...

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8th International Conference on Electronic information technology and Computer engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

This study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. The paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. The research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. This study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Robust Orientation Method Based on an Atmospheric Polarization Model for Hazy Weather

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IEEE Internet of Things Journal 2025年第9期12卷 12397-12409页

作者： Yang, Jiangtao Tian, Zhichao Wen, Hao Wang, Zirui Taiyuan University of Science and Technology School of Electronic Information Engineering Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Shanxi Taiyuan030024 China

Accurate and reliable navigation data are key components in the Internet of Things (IoT). High-precision and stable autonomous orientation has attracted considerable attention regarding environments in which the global navigation satellite system signal is unreliable. Bionic polarized light orientation is a method based on the mechanism of insect navigation, and the orientation signal comes from nature, which has broad application prospects. However, there is significant dust or nitrogen oxide particle scattering and sunlight absorption in haze, reducing the polarization degree and abnormal polarization azimuth. This makes heading angle measurements with existing polarized light navigation and positioning methods inaccurate under hazy conditions. A polarization image denoising method based on deep learning can effectively eliminate the influence of haze on heading angles. Noisy polarization images can be decomposed into high- and low-frequency components containing different types of noise. Thus, this study proposes a polarization image denoising method that combines high- and low-frequency image decomposition with a multiscale two-level fusion strategy. This method decomposes the polarization image with noise into a set of low-frequency structural components and a set of high-frequency detailed components by utilizing multiscale Gaussian filters. On this basis, a multiscale two-level fusion strategy based on a complementary feature weighted fusion mechanism is also designed. In the high- and low-frequency merge and reconstruction module, after adding and fusing the high- and low-frequency enhanced features output by LF-UNet and HF-UNet, a residual convolution (Res-Conv) layer and an output convolution layer are used for residual prediction and then fused with the input image to acquire a high-quality polarization image. The proposed method is experimentally verified outdoors using a polarized light/inertial integrated heading measurement system and a vehicle. The e

关键词： Image denoising

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Laser Scribed CNTs/PEEK/TPU Composites Film for High-Performance Stretchable Electrodes

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IEEE Sensors Journal 2025年第10期25卷 16666-16674页

作者： Jiang, Caiyun Tang, Lei Hou, Jian Ding, Guqiao Sheng, Bin University of Shanghai for Science and Technology College of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instrument and System Shanghai200093 China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences State Key Laboratory of Materials for Integrated Circuits Shanghai200050 China

In this article, an elastic composite material comprising carbon nanotubes (CNTs), polyether-ether-ketone (PEEK), and thermoplastic polyurethane (TPU) is presented, in which direct laser-induced graphitization was used to fabricate high-performance stretchable electrodes. The introduction of CNTs can enhance the conductivity and mechanical strength of the composites, thus improving the overall performance of the sensors. Flexible strain sensors with porous structures were prepared by appropriate encapsulation methods, which exhibited superior gauge factors (GFs) of 72, 367, and 972 in the 0%–30%, 30%–44%, and 44%–53% strain regions, respectively. Furthermore, it features a rapid response time (100 ms), excellent stability and durability, and is capable of detecting both large-scale human movement and monitoring of small physical signals. In addition, patterned laser-induced graphene interdigital electrodes (LIG-IDEs) were fabricated on composites using laser light. Highly sensitive humidity sensors were obtained using graphene oxide as the humidity-sensing material for respiratory monitoring and noncontact sensing. The thermoplastic properties of TPU allow the straight fiber to be thermoformed into highly stretchable helical electrode, which was of a helix index of 5 and with a high-quality factor (Q = 130) at 1250% strain. In conclusion, the preparation of LIG on stretchable composite films by direct laser scribing shows a promising approach for large-scale fabrication of wearable devices. © 2001-2012 IEEE.

关键词： Humidity sensors

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