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Smart Core-Shell Elastomer Fibers with Simultaneous Resistance and Color Responses

Chinese Journal of Polymer Science 2025年第4期43卷 556-562页

作者： Dong-Peng Sun Yao Xiao Yuan Zheng An-Xun Zhang Bao-Ling Guo Dong Chen Department of Oncology Longyan First Affiliated Hospital of Fujian Medical UniversityLongyan364000China College of Energy Engineering and State Key Laboratory of Clean Energy Utilization Zhejiang UniversityHangzhou310027China Zhejiang Key Laboratory of Smart Biomaterials College of Chemical and Biological EngineeringZhejiang UniversityHangzhou310058China

Fibers with deformation-triggered responses are essential for smart textiles and wearable ***,smart core-shell elastomer fibers with a conductive core and a liquid crystal elastomer shell showing simultaneous resistance and color responses are designed and *** conductive core is consisted of interconnected liquid metal nanodroplets dispersed in a polymer matrix and the elastomer shell is made of cholesteric liquid *** stretched,the fiber resistance increases as the interconnected pathways of liquid metal nanodroplets along the fiber axis become narrower,and the selective reflection color from the fiber surface blueshifts since the cholesteric pitch *** smart elastomer fibers could be woven into smart textiles and respond to various mechanical deformations,including stretching,bending,compression and *** average resistance change is 51%under 100%strain and its variation is smaller than 4%over 500 cycles,showing remarkable fatigue *** simultaneous resistance and color responses to mechanical deformations make the fibers attractive for broad applications,such as flexible electronics.

关键词： Elastomer fiber Core-shell Simultaneous responses Resistance Color

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Study of electrochemical biosensor for determination of glucose based on Prussian blue/gold nanoparticles-chitosan nanocomposite film sensing interface

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第1期35卷 015125-015125页

作者： Peng, Zhen Li, Zongyuan Liao, Jingru Zhang, Zhimi Song, Yangyang Xia, Cao Xia, Yuanlin Wang, Zhuqing Sichuan Univ Sch Mech Engn Chengdu 610065 Peoples R China Sichuan Univ West China Hosp MedX Ctr Mfg Chengdu 610041 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Med Chengdu 610041 Peoples R China

A highly electroactive bilayer composite film sensing interface of Prussian blue (PB)/gold nanoparticles-chitosan (AuNPs-CS) was modified on Au electrode through electrochemical deposition and coating method followed by integrating glucose oxidase (GOx) into the interfacial matrix to fabricate a high-performance glucose biosensor. The excellent electrocatalytic ability of the PB/AuNPs-CS composite film sensing interface for H2O2 was evaluated, which has a broad linear response of 0.01-7.95 mM, with a low detection limit (LOD) 0.269 mu M and a high sensitivity of 511.82 mu AmM(-1)cm(-2). The enhanced electrocatalytic activity of this sensing interface for H2O2 is attributed to the protection from the network CS film to PB and the synergistic effect of PB and AuNPs. Consequently, an electrochemical biosensing interface was constructed with GOx immobilized as a model enzyme. The PB/GOx-AuNPs-CS biosensing nanocomposite film was capable of a fast steady-state response time (within 2 s) and high sensitivity to glucose with a wide linear range of: 0.025-2.00 mM (R-2 = 0.99), with a sensitivity of 40.41 mu AmM(-1)cm(-2) and a LOD of 1.62 mu M;and 2.00-6.50 mM (R-2 = 0.98), with a sensitivity of 8.90 mu AmM(-1)cm(-2) and a LOD of 7.16 mu M. The biosensor has good anti-interference and selectivity, which provides a promising wide linear range platform for clinical blood glucose detection in the future.

关键词： Prussian blue chitosan gold nanoparticles nanocomposite glucose oxidase biosensing

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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Leveraging neighborhood and path information for influential spreaders recognition in complex networks

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JOURNAL OF INTELLIGENT INFORMATION SYSTEMS 2024年第2期62卷 377-401页

作者： Ullah, Aman Sheng, Jinfang Wang, Bin Din, Salah Ud Khan, Nasrullah Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China

The study of influential spreaders has become a growing area of interest within network sciences due to its critical implications in understanding the robustness and vulnerability of complex networks. There is a significant degree of focus on the factors that dictate the decision-making process for identifying these influential spreaders in highly complex networks, given their crucial role in network performance and security. Previous research methodologies have offered a deep understanding of the importance of spreaders, also referred to as nodes. These methods, however, have primarily depended on either neighborhood or path information to identify these spreaders. They have often studied local network data, or adopted a more broad-based, global view of the network. Such an approach may not provide a comprehensive understanding of the overall network structure and the relationships between nodes. Addressing this limitation, our research introduces Neighborhood and Path Information-based Centrality (NPIC) algorithm. This innovative centrality algorithm combines both neighborhood and path information to identify influential spreaders in a complex network. By incorporating these two significant aspects, NPIC provides a more holistic analysis of network centrality, enabling a more accurate identification of influential spreaders. We have subjected NPIC to rigorous testing using numerous simulations on both real and artificially-created datasets. These simulations applied an epidemic model to calculate the spreading efficiency of each node within its given environment. Our simulations, conducted across a wide range of synthetic and real-world datasets, demonstrated that NPIC outperforms existing methodologies in identifying influential spreaders in corresponding networks.

关键词： Influential spreaders Spreading effect Neighborhood and path information Complex networks

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Predictive models for the surface roughness and subsurface damage depth of semiconductor materials in precision grinding

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International Journal of Extreme Manufacturing 2025年第3期7卷 423-449页

作者： Shang Gao Haoxiang Wang Han Huang Zhigang Dong Renke Kang State Key Laboratory of High-performance Precision Manufacturing Dalian University of TechnologyDalian 116024People’s Republic of China School of Advanced Manufacturing Sun Yat-sen UniversityShenzhenPeople’s Republic of China

Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium *** roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after *** prediction models lack general applicability and do not accurately account for the complex material behavior under grinding *** paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor *** surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction *** SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage *** surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process ***,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous *** models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for ***,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different *** findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.

关键词： surface quality grinding predictive models semiconductor materials surface roughness subsurface damage depth

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Large Eddy Simulation of NO Formation in Non-Premixed Turbulent Jet Flames with Flamelet/Progress Variable Approach

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Journal of Thermal Science 2024年第6期33卷 2399-2412页

作者： WAN Jiawei GUO Junjun WEI Zhengyun JIANG Xudong LIU Zhaohui State Key Laboratory of Coal Combustion School of Energy and Power EngineeringHuazhong University of Science and Technology(HUST)Wuhan 430074China CCRC King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Kingdom of Saudi Arabia

To improve the NO modelling in turbulent flames,the flamelet/progress variable(FPV)model is extended by introducing NO mass fraction into the progress variable and incorporating an additional NO transport *** sets of flamelet databases are tabulated with progress variables based on major species and NO mass fraction,*** former is used for the acquisition of the main thermochemical variables,while the latter is employed for NO ***,an additional transport equation is solved to obtain the NO mass fraction,with the source term corrected using the scale similarity *** assessments are first conducted on laminar counterflow diffusion flames to identify lookup-related errors and assess the suitability of progress variable *** results show that the progress variables based on major species and NO could correctly describe the main thermochemical quantities and NO-related variables,***,the model is applied to the large eddy simulation(LES)of Sandia *** results indicate that the extended FPV model improves the NO prediction,with a mean error for NO prediction at 55%,significantly lower than those of existing FPV models(130%and 385%).The LES with the extended FPV model quantitatively captures NO suppression in the mid-range of Reynolds numbers from 22400(Flame D)to 33600(Flame E),but underestimates the NO suppression at higher Reynolds numbers from 33600 to 44800(Flame F).This underprediction is primarily attributed to the underestimation of local extinction levels in flames with high Reynolds numbers.

关键词： Sandia flames large eddy simulation flamelet/progress variable model NO emission

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Abiotic aerobic oxidation pathways of stibnite revealed by oxygen and sulfur isotope systematics of sulfate

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Journal of Environmental Sciences 2025年第1期147卷 83-92页

作者： Ningjie He Shu Li Xiaoqian Li Yaning Tang Jiaxue Yang Jianwei Zhou School of Environment Studies China University of GeosciencesWuhan 430074China Key Laboratory of Mine Ecological Effects and Systematic Restoration Ministry of Natural ResourcesBeijing 100081China State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution Ministry of Ecology and EnvironmentWuhan 430074China Institute of Geological Survey China University of GeosciencesWuhan 430074China

The environmental threat posed by stibnite is an important geoenvironmental issue of current *** better understand stibnite oxidation pathways,aerobic abiotic batch experiments were conducted in aqueous solution with varyingδ^(18)O_(H_(2)O) value at initial neutral pH for different lengths of time(15-300 days).The sulfate oxygen and sulfur isotope compositions as well as concentrations of sulfur and antimony species were *** sulfur isotope fractionation factor(△^(34)S_(SO4-stibnite))values decreased from 0.8‰to-2.1‰during the first 90 days,and increased to 2.6‰at the 180 days,indicating the dominated intermediate sulfur species such as S_(2)O_(3)^(2-),S0,and H_(2)S(g)involved in Sb2S3 oxidation *** incorporation of O into sulfate derived from O_(2)(~100%)indicated that the dissociated O_(2)was only directly adsorbed on the stibnite-S sites in the initial stage(0-90 days).The proportion of O incorporation into sulfate from water(27%-52%)increased in the late stage(90-300 days),which suggested the oxidation mechanism changed to hydroxyl attack on stibnite-S sites promoted by nearby adsorbed O_(2)on stibnite-Sb *** exchange of oxygen between sulfite and water may also contributed to the increase of water derived O into SO42-.The new insight of stibnite oxidation pathway contributes to the understanding of sulfide oxidation mechanism and helps to interpret field data.

关键词： Sulfide oxidation δ^(34)S_(SO4) δ^(18)O_(SO4) Oxygen source of sulfate

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A comparative study of data-driven battery capacity estimation based on partial charging curves

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Journal of Energy Chemistry 2024年第1期88卷 409-420,I0010页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Xianggong Zhang Enhu Li Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Shaanxi Key Laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and Automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina Wuhan Institute of Marine Electric Propulsion China State Shipbuilding Corporation LimitedWuhan 430064HubeiChina Gresgying Digital Technology Ltd. Xi’an 710086ShaanxiChina

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

关键词： Lithium-ion battery Partial charging curves Capacity estimation Data-driven Sampling frequency

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Overview of Terrain Traversability Evaluation for Autonomous Robots

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JOURNAL OF FIELD ROBOTICS 2024年第0期

作者： Shu, Yongjie Dong, Linwei Liu, Jianfeng Liu, Cheng Wei, Wei Beijing Inst Technol Sch Mech Engn Beijing Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing Zhejiang Peoples R China

Traversability evaluation is the foundation and core of unmanned platforms for scene understanding and autonomous navigation, whose successful completion relies on the analysis of the platform's characteristics and the semantic and geometric features of the surrounding environment. This topic has been reviewed by many literatures, which are characterized by a single perspective and lack comprehensive evaluation frameworks. Thus, the concept and developmental trajectory of traversability evaluation are initially outlined in this paper, distinguishing it from other issues, while constructing an evaluation framework based on two categories: direct assessment and downstream task assessment. Subsequently, traversability evaluation methods are classified based on multiple dimensions, including sensor types, robot types, usage scenarios, and learning approaches. On the basis of the constructed evaluation framework, comparisons are made among existing algorithms in terms of performance and runtime. Subsequently, a summary is provided on commonly used features and their mainstream computation methods in terrain evaluation. Additionally, open-source data sets in this field and projects for scene construction and algorithm validation are compiled and organized. Finally, an analysis is conducted on the development direction and trends, emphasizing the urgent need to establish standardized evaluation systems and comparison baselines. Furthermore, it is imperative that various environmental and platform information be comprehensively integrated into algorithms, while also ensuring that simulation, demonstration, and exploration are incorporated into a unified framework to enhance the robot's learning capability.

关键词： autonomous robots data-driven learning-based terrain characterization traversability evaluation

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An Integrated Analysis on the Synergistic Reduction of Carbon and Pollution Emissions from china’s Iron and Steel Industry

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Engineering 2024年第9期40卷 111-121页

作者： Quanyin Tan Fei Liu Jinhui Li State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China

Decarbonization and decontamination of the iron and steel industry(ISI),which contributes up to 15%to anthropogenic CO_(2) emissions(or carbon emissions)and significant proportions of air and water pollutant emissions in china,are challenged by the huge demand for *** and pollutants often share common emission sources,indicating that emission reduction could be achieved ***,we explored the inherent potential of measures to adjust feedstock composition and technological structure and to control the size of the ISI to achieve carbon emission reduction(CER)and pollution emission reduction(PER).We investigated five typical pollutants in this study,namely,petroleum hydrocarbon pollutants and chemical oxygen demand in wastewater,particulate matter,SO_(2),and NO_(x) in off gases,and examined synergies between CER and PER by employing cross elasticity for the period between 2022 and *** results suggest that a reduction of 8.7%-11.7%in carbon emissions and 20%-31%in pollution emissions(except for particulate matter emissions)could be achieved by 2025 under a high steel scrap ratio(SSR)***,the SSR and electric arc furnace(EAF)ratio serve critical roles in enhancing synergies between CER and PER(which vary with the type of pollutant).However,subject to a limited volume of steel scrap,a focused increase in the EAF ratio with neglection of the available supply of steel scrap to EAF facilities would lead to an increase carbon and pollution *** CER can be achieved through SSR and EAF ratio optimization,only when the crude steel production growth rate remains below 2.2%can these optimization measures maintain the emissions in 2030 at a similar level to that in ***,the synergistic effects between PER and CER should be considered when formulating a development route for the ISI in the future.

关键词： Iron and steel industry Carbon and pollution emissions Synergistic reduction Technological structure Steel scrap Cross-elasticity

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