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Preparation of Self-limiting Heating Cables with Excellent Processability, Mechanical Properties and PTC Effect via Thermal and Electrical Treatments

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Chinese Journal of Polymer Science 2024年第4期42卷 511-520页

作者： tian-Hao Yang Tong Wu qiang Fu State Key Laboratory of Polymer Materials Engineering College of Polymer Science and EngineeringSichuan UniversityChengdu610065China State Key Laboratory of Polymer Materials Engineering Polymer Research Institute of Sichuan UniversityChengdu610065China

Polymer/conductive filler composites have been widely used for the preparation of self-limiting heating cables with the positive temperature coefficient (PTC) effect. The control of conductive filler distribution and network in polymer matrix is the most critical for performance of PTC materials. In order to compensate for the destruction of the filler network structure caused by strong shearing during processing, an excessive conductive filler content is usually added into the polymer matrix, which in turn sacrifices its processability and mechanical properties. In this work, a facile post-treatment of the as-extruded cable, including thermal and electrical treatment to produce high-density polyethylene (HDPE)/carbon black (CB) cable with excellent PTC effect, is developed. It is found for the as-extruded sample, the strong shearing makes the CB particles disperse uniformly in HDPE matrix, and 2. wt% CB is needed for the formation of conductive paths. For the thermal-treated sample, a gradually aggregated CB filler structure is observed, which leads to the improvement of PTC effect and the notable reduction of CB content to 2. wt%. It is very interesting to see that for the sample with combined thermal and electrical treatment, CB particles are agglomerated and oriented along the electric field direction to create substantial conductive paths, which leads to a further decrease of CB content down to 15 wt%. In this way, self-limiting heating cables with excellent processability, mechanical properties and PTC effect have simultaneously been achieved.

关键词： Self-limiting heating cables Positive temperature coefficient Thermal treatment Electrical treatment

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A toroidal SAW gyroscope with focused IDTs for sensitivity enhancement

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Microsystems & Nanoengineering 2024年第2期10卷 47-56页

作者： Lu tian Haitao Zhao qiang Shen Honglong Chang Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

A surface acoustic wave(SAW)gyroscope measures the rate of rotational angular velocity by exploiting a phenomenon known as the SAW gyroscope *** a gyroscope is a great candidate for application in harsh environments because of the simplification of the suspension vibration mechanism necessary for traditional microelectromechanical system(MEMS)***,for the first time,we propose a novel toroidal standing-wave-mode SAW gyroscope using focused interdigitated transducers(FIDTs).Unlike traditional SAW gyroscopes that use linear IDTs to generate surface acoustic waves,which cause beam deflection and result in energy dissipation,this study uses FIDTs to concentrate the SAW energy based on structural features,resulting in better focusing performance and increased SAW *** experimental results reveal that the sensitivity of the structure is 1.51µV/(°/s),and the bias instability is 0.77°/s,which are improved by an order of magnitude compared to those of a traditional SAW ***,the FIDT component can enhance the performance of the SAW gyroscope,demonstrating its superiority for angular velocity *** work provides new insights into improving the sensitivity and performance of SAW gyroscopes.

关键词： angular deflection gyroscope

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TSC prediction and dynamic control of BOF steelmaking with state-of-the-art machine learning and deep learning methods

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Journal of Iron and Steel Research International 2024年第1期31卷 174-194页

作者： tian-yi Xie Cai-dong Zhang Quan-lin Zhou Zhi-qiang tian Shuai Liu Han-jie Guo Material Technology Research Institute Hesteel GroupShijiazhuang050023HebeiChina Tangsteel Company Hesteel GroupTangshan063611HebeiChina School of Artificial Intelligence Beijing Technology and Business UniversityBeijing100048China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China

Mathematical(data-driven)models based on state-of-the-art(SOTA)machine learning and deep learning models and data collected from 12.786 heats were established to predict the values of temperature,sample,and carbon(TSC)test,including temperature of molten steel(TSC-Temp),carbon content(TSC-C)and phosphorus content(TSC-P),which made prepa-ration for eliminating the TSC *** maximize the prediction accuracy of the proposed approach,various models with different inputs were implemented and compared,and the best models were applied to the production process of a Hesteel Group steelmaking plant in China in the *** number of tabular features(hot metal information,scrap,additives,blowing practices,and preset values)was expanded,and time series(off-gas profiles and blowing practice curves)that could reflect the entire steelmaking process were introduced as ***,the latest machine learning models(LightGBM,CatBoost,TabNet,and NODE)were used to make predictions with tabular features,and the best coefficient of determination R^(2.values obtained for TSC-P,TSC-C and TSC-Temp predictions were 0.435(LightGBM),0.857(Cat-Boost)and 0.678(LightGBM),respectively,which were higher than those of classic models(backpropagation and support vector machine).Then,making predictions was performed by using SOTA time series regression models(SCINet,DLinear,Informer,and MLSTM-FCN)with original time series,SOTA image regression models(NesT,CaiT,ResNeXt,and GoogLeNet)with resized time series,and the proposed Concatenate-Model and Parallel-Model with both tabular features and time *** optimization and comparisons,it was finally determined that the Concatenate-Model with MLSTM-FCN,SCINet and Informer as feature extractors performed the best,and its R^(2.values for predicting TSC-P,TSC-C and TSC-Temp reached 0.470,0.858 and 0.710,*** field test accuracies for TSC-P,TSC-C and TSC-Temp were 0.459,0.850 and 0.685,respectively.A related importance analysis was carri

关键词： BOF steelmaking In-blow prediction TSC test Machine learning Deep learning Field application

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Pre-activated macrophage membrane-encased aggregationinduced emission featuring nanoparticles:a novel possibility for tuberculosis treatment

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Signal Transduction and Targeted Therapy 2024年第7期9卷 2848-2850页

作者： qiang Cai Yihao tian Quazi T.H.Shubhra Department of Neurosurgery Renmin Hospital of Wuhan UniversityWuhanChina Department of Human Anatomy and Histology and Embryology School of Basic Medical SciencesWuhan UniversityWuhanChina Institute of Chemistry University of Silesia in KatowiceSzkolna 9KatowicePoland

In a recent study published in Nature Nanotechnology,Li et *** the synthesis of a novel photosensitizer with unique aggregation-induced emission(AIE)characteristics.1 They microencapsulated this photosensitizer inside engineered nanoparticles(NPs)coated with pre-activated macrophage cell membranes(CMs),an innovative approach marking a significant leap in nanomedicine by offering a pathway to potent tuberculosis(TB)treatments that bypass antibiotic resistance with unparalleled precision.

关键词： tuberculosis macrophage aggregation

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Recent development on the design,preparation,and application of stretchable conductors for flexible energy harvest and storage devices

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SusMat 2024年第4期4卷 175-210页

作者： Minhan Cheng Ke tian tian Qin Qianyang Li Hua Deng {2. Fu College of Polymer Science and Engineering State Key Laboratory of Polymer Materials EngineeringSichuan UniversityChengduChina

The intensifying energy crisis has made it urgent to develop robust and reli-able next-generation energy *** for conventional large-scale energy sources,the imperceptible and randomly distributed energy embedded in daily life awaits comprehensive exploration and *** the latent energy has the potential to facilitate the further evolution of soft energy *** with rigid energy devices,flexible energy devices are more convenient and suitable for harvesting and storing energy from dynamic and complex structures such as human *** conductors that are capable of withstanding strain(≫1%)while sustaining stable conductive pathways are prerequisites for realizing flexible electronic energy ***,under-standing the characteristics of these conductors and evaluating the feasibility of their fabrication strategies are particularly *** this review,various prepa-ration methods for stretchable conductors are carefully classified and ***,recent progress in the application of energy harvesting and stor-age based on these conductors is discussed in ***,the challenges and promising opportunities in the development of stretchable conductors and inte-grated flexible energy devices are highlighted,seeking to inspire their future research directions.

关键词： energy harvesting energy storage fabrication strategies stretchable conductors

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In situ regulating intimately connected heterostructure by decomposition of solid solution oxides toward high-efficient water oxidation

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Rare Metals 2024年第4期43卷 1557-1569页

作者： Shao-Fei Zhang Xiao-Lu Yin Jin Wang Jian-Li Kang tian-tian Li Jin-Feng Sun Yong-{2. Meng Jian-Jiang Wang Dian-Long Wang Kai-{2. Qin Hebei Key Laboratory of Flexible Functional Materials/Hebei Key Laboratory of Material Near-Net Forming Technology School of Materials Science and EngineeringHebei University of Science and TechnologyShijiazhuang050018China Key Laboratory of Advanced Ceramics and Machining Technology/Ministry of Education School of Materials Science and EngineeringTianjin UniversityTianjin300072China Electrochemical Engine Center Department of Mechanical EngineeringPennsylvania State UniversityUniversity ParkPA16802USA

Heterogeneous interfaces produced by interdomain interactions on a nanoscale performs a crucial role in boosting the properties of an electrocatalyst toward oxygen evolution reaction(OER)***,a series of dual-phase electrodes with intimately connected heterointerfaces are prepared by in situ decomposing solid solution oxide of Ni_(x)Co_(y)Fe_(100-x-y)O,which grew on Ni foam massively via an ultrafast combustion ***,with high-reaction kinetics caused by the reduction treatment at 450℃,the less electronegative Fe and Co are more oxyphilic than Ni,which facilitated their co-exsolution and formation of CoFe_2._4/NiO oxide with enriched oxygen *** from the nanoporous framework,heterojunction structure,and oxygen defects,the self-supporting electrodes present rapid charge/mass transmission and provide abundant active sites for *** optimized sample(R-SNCF4.5)shows low overpotentials of 2.6 and 32. mV at 10 and100 mA·cm^(-2.,a small Tafel slope(46.7 mV·dec^(-1)),and excellent *** assembled R-SNCF4.5//Pt/C/NF electrolyzer demonstrates continuous electrolysis over 50 h at a current density of 10 mA·cm^(-2.,under 1.51 *** functional theory(DFT)calculations verify that the strong electronic modulation plays a critical part in the CoFe_2._4/NiO hybrid by lowering the energy barriers for the ratedetermining steps,and Fe sites are the most active OER sites.

关键词： Rapid combustion Decomposition of solid solution oxide Dual-phase heterostructure Oxygen evolution reaction Theoretical calculation

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Research on aiming methods for small sample size shooting tests of two-dimensional trajectory correction fuse

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Defence Technology（防务技术） 2024年第3期33卷 506-517页

作者： Chen Liang qiang Shen Zilong Deng Hongyun Li Wenyang Pu Lingyun tian Ziyang Lin School of Mechatronical Engineering Beijing Institute of TechnologyBeijing 100081China

The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse *** impact point easily deviates from the target,and thus the correction result cannot be readily ***,the cost of shooting tests is considerably high to conduct many tests for data *** address this issue,this study proposes an aiming method for shooting tests based on small sample *** proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion *** experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.

关键词： Two-dimensional trajectory correction fuse Small sample size test Compatibility test KL divergence Fusion bayesian estimation

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CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

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Journal of Electronic Science and Technology 2024年第2期22卷 77-90页

作者： Xiao-Qian Lu Jun tian qiang Liao Zheng-Wu Xu Lu Gan Bidding and Purchasing Center University of Electronic Science and Technology of ChinaChengdu611731China Yibin Research Institute University of Electronic Science and Technology of ChinaYibin644000China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu611731China

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention ***,a traversal search is conducted through the traversal layer for finite parameters in the phase ***,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input ***,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction *** model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space ***,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction *** experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction ***,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space *** experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

关键词： Chaotic time series Incremental attention mechanism Phase-space reconstruction

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Constructing oxygen deficiency-rich V_(2.O_(3)@PEDOT cathode for high-performance aqueous zinc-ion batteries

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Rare Metals 2024年第2期43卷 635-646页

作者： Dong-Fei Sun Zi-Juan Wang tian tian Xin Yu Dan-Dan Yu Xiao-Zhong Zhou Guo-Fu Ma Zi-{2. Lei Key Laboratory of Eco-functional Polymer Materials of the Ministry of Education Key Laboratory of Eco-environmental Polymer Materials of Gansu ProvinceSchool of College of Chemistry and Chemical EngineeringNorthwest Normal UniversityLanzhou730070China

Aqueous zinc-ion batteries(AZIBs)have attracted widespread attention due to the advantages of high safety and environmental *** V_(2.O_(3) is a promising cathode,the strong electrostatic interaction between Zn^(2.) and V_(2.O_(3) crystal,and the sluggish reaction kinetics still limit their application in ***,the oxygen defects rich V_(2.O_(3) with conducive poly(3,4-ethylenedioxythiophene)(PEDOT)shell(V_(2.O_(3)-Od@PEDOT)was fabricated for AZIBs by combining the sulfur-assisted thermal reduction and in-situ polymerization *** introduced oxygen vacancies of V_(2.O_(3)–Od@PEDOT weaken the electrostatic interaction between Zn^(2.) and the host material,improving the interfacial electron transport,while the PEDOT coating enhances the structural stability and conductivity of V_(2.O_(3),thus accelerating the reaction *** on the advantages,V_(2.O_(3)–Od@PEDOT electrode delivers a reversible capacity of 495 mAh·g^(−1) at 0.1 A·g^(−1),good rate capability(189 mAh·g^(−1)at 8.0 A·g^(−1)),and an impressive cycling stability with 90.1%capacity retention over 1000 cycles at 8.0 A·g^(−1).The strategy may provide a path for exploiting the other materials for high performance AZIBs.

关键词： Oxygen defects V_(2.O_(3) PEDOT Electrochemical self-optimization Aqueous zinc-ion batteries

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Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： Jiu-qiang Yang Nian-tian Lin Kai Zhang Yan Cui Chao Fu Dong Zhang College of Earth Sciences and Engineering Shandong University of Science and Technology Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology Transportation Institute of Inner Mongolia University Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction

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