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Enhanced rate capability and cycle stability of Ti2C MXene for sodium storage through an aniline molecules welding strategy

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Tungsten 2025年第1期7卷 161-171页

作者： Hao Chen Ya-Qin Wang Dong-Ting Zhang Bei Zhao Min-Peng Li Chen-Yang Li Tian-Peng Xu Mao-Cheng Liu State Key Laboratory of Advanced Processing and Recycling of Non-Ferrous Metals Lanzhou University of Technology school of Materials Science and engineering Lanzhou University of Technology school of computer and Communication Technology Lanzhou University of Technology

MXenes obtained significant attention in the field of energy storage devices due to their characteristic layered structure,modifiable surface functional groups,large electrochemically active surface,and regulable interlayer ***,the self-restacking and sluggish ions diffusion kinetics performance of MXenes during the alkali metal ions insertion/extraction process severely impedes their cycle stability and rate *** paper proposes an aniline molecule welding strategy for welding p-phenylenediamine（PPDA） into the interlayers of Ti2C through a dehydration condensation *** welded PPDA molecules can contribute pillar effect to the layered structure of *** pillar effect effectively maintains the structural stability during the sodium ions insertion/extraction process and effectively expands the interlayer spacing of Ti2C from 1.16 to 1.38 nm,thereby enhancing ions diffusion kinetics performance and improving the long-term cycle *** Ti2C-PPDA demonstrates outstanding Na+storage capability,exhibiting a specific capacity of 100.2 mAh·g-1at a current density of 0.1 A·g-1over 960 cycles and delivering a remarkable rate capability 81.2 mAh·g-1at a current density of 5 A·*** study demonstrates that expanding interlayer spacing is a promising strategy to enhance the Na+storage capacity and improve long-term cycling stability,which provides significant guidance for the design of two-dimensional Na+storage materials with high-rate capability and cycle stability.

关键词： Diffusion kinetics Expanding interlayer spacing Rate capability Sodium ions storage Ti2C

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Optimal Scheduling of an Independent Electro-Hydrogen System with Hybrid Energy Storage Using a Multi-Objective Standardization Fusion Method

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Energy engineering 2025年第1期122卷 63-84页

作者： SuliangMa ZeqingMeng Mingxuan Chen Yuan Jiang school of Electrical and control engineering North China University of TechnologyBeijing100144China ChinaThree Gorges Technology Co. Ltd.Beijing100032China school of Automation and Electrical engineering University of Science and Technology BeijingBeijing100083China

In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is ***,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical *** deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s *** optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization *** proposed method better balances all optimization objective functions and reduces the impact of their *** the cost of BESS decreases by approximately 30%,its participation deepens by about 1 ***,if the price of the electrolyzer is less than 15￥/kWh or if the cost of the fuel cell drops below 4￥/kWh,their participation will increase *** study aims to provide a more reasonable approach to solving multi-objective optimization problems.

关键词： Electro-hydrogen system multi-objective optimization standardization method hybrid energy storage system

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Influence of introducing Zr,Ti,Nb and Ce elements on externally solidified crystals and mechanical properties of high-pressure die-casting Al–Si alloy

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International Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 147-153页

作者： Junjie Li Wenbo Yu Zhenyu Sun Weichen Zheng Liangwei Zhang Yanling Xue Wenning Liu Shoumei Xiong Center of Materials Science and engineering School of Mechanical and Electronic Control EngineeringBeijing Jiaotong UniversityBeijing 100044China Traffic&Transportation engineering Central South UniversityChangsha 410075China Shanghai Synchrotron Radiation Facility Shanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China school of Materials Science and engineering Tsinghua UniversityBeijing 100084China

High pressure die casting(HPDC)AlSi10Mn Mg alloy castings are widely used in the automobile *** can optimize the mechanical properties of castings through heat treatment,while the release of thermal stress arouses the deformation of large integrated ***,the development of non-heat treatment Al alloys is becoming the hot *** addition,HPDC contains externally solidified crystals(ESCs),which are detrimental to the mechanical properties of *** achieve high strength and toughness of non-heat treatment die-casting Al-Si alloy,we used AlSi9Mn alloy as matrix with the introduction of Zr,Ti,Nb,and *** influences on ESCs and mechanical properties were systematically investigated through three-dimensional reconstruction and thermodynamic *** results reveal that the addition of Ti increased ESCs'size and porosity,while the introduction of Nb refined ESCs and decreased ***,large-sized Al_3(Zr,Ti)phases formed and degraded the mechanical *** introduction of Ce resulted in the poisoning effect and reduced mechanical properties.

关键词： aluminium alloy high-pressure die-casting externally solidified crystals non-heat treatment

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AI-Enhanced Secure Data Aggregation for Smart Grids with Privacy Preservation

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computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu school of Software Nanjing University of Information Science and TechnologyNanjing210044China school of Information Science and engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China school of computer Science and Technology(school of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China school of computer Science(school of Cyber Science and engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Optimization of Convolutional Neural Network Based on Improved Sparrow Algorithm for Flywheel Energy Storage System Loss in Small Sample

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Dianwang Jishu/Power System Technology 2025年第1期49卷 366-372页

作者： Wei, Le Li, Chenglin Fang, Fang Liu, Yubin school of control and computer engineering North China Electric Power University Changping District Beijing 102206 China

Flywheel energy storage systems have standby losses and are unsuitable for long-term energy storage. Aiming at the economic index of flywheel loss, based on the small sample data of the operation of the flywheel energy storage system, a flywheel loss calculation model combining a Logistic chaotic sparrow optimization algorithm and a convolutional neural network was proposed. Firstly, the causes of flywheel loss were analyzed. Next, the flywheel operation data of Ningxia Lingwu Power Plant was preprocessed, and the adversarial generation network was used for small-sample enrichment. Then, the loss model is established based on the convolutional neural network, the improved sparrow algorithm is used to optimize the hyperparameters of the model, and the superiority of the model is verified by comparison. Finally, simulation experiments show that the model can optimize the output of the flywheel energy storage system and reduce the flywheel loss. © 2025 Power System Technology Press. All rights reserved.

关键词： Flywheels

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Near-perfect replication on amorphous alloys through active force modulation based on machine learning/neural network parameter prediction

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 181-191页

作者： Senkuan Meng Zheng Wang Ruisong Zhu Ruijie Liu Jiang Ma Lina Hu Weihua Wang Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials (Ministry of Education) School of Materials Science and Engineering Shandong University College of Mechatronics and control engineering Shenzhen University Songshan Lake Materials Laboratory Institute of Physics Chinese Academy of Sciences

As a microforming technique, micro/nano-structural replication possesses advantages of high precision and efficiency. With the remarkable superplasticity in the supercooled liquid region, amorphous alloys or metallic glasses(MGs) are regarded as ideal materials for miniature fabrication. However, due to the intrinsic metastable nature of supercooled liquids, the design of imprinting processes for MGs poses a challenge. In the past, process parameters have largely relied on trial-and-error *** this work, a low-frequency active force modulation system is employed to apply a stable, precise, and real-time feedback stress field for imprinting of MG samples. Low-frequency vibrations can facilitate the filling of microstructures on the template surface by reducing the effective viscosity of the supercooled liquid. With the dataset composed of orthogonal experiments, a machine learning strategy based on back-propagation(BP) neural networks was utilized to construct a 3D parameter space for temperature, stress, and time, and to predict the corresponding filling ratio. Furthermore, the optimal combination of imprinting process parameters was identified, and its filling ratio was experimentally validated to reach as high as 0.94. The near-perfect replication of microstructures confirms the superiority of the active force modulation system and the data-driven strategy of machine learning-assisted parameter design. At the same time, this one-step microforming process provides a new approach to addressing the accuracy-cost trade-off dilemma in precision manufacturing.

关键词： metallic glass amorphous alloy microforming imprinting machine learning

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Anomaly monitoring and early warning of electric moped charging device with infrared image

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Optoelectronics Letters 2025年第3期21卷 136-141页

作者： LI Jiamin HAN Bo JIANG Mingshun school of control Science and engineering Shandong University

Potential high-temperature risks exist in heat-prone components of electric moped charging devices, such as sockets,interfaces, and controllers. Traditional detection methods have limitations in terms of real-time performance and monitoring scope. To address this, a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image, then applying an image segmentation algorithm to divide the image.

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Metal-organic frameworks with dinuclear metal centers for synergistically boosting CO2 photoreduction

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Science China Chemistry 2025年第1期68卷 201-208页

作者： Hui-Feng Wang Wen-Jie Shi Yu-Xin Yang Bao-Xia Dong Tong-Bu Lu Di-Chang Zhong school of Chemistry and Chemical engineering Yangzhou University Institute for New Energy Materials and Low Carbon Technologies School of Materials Science and Engineering Tianjin University of Technology

Dinuclear metal synergistic catalysis （DMSC） has been proved powerful in boosting CO2reduction,while this has not been achieved in heterogeneous catalysts with long-range order *** this study,we,by introduction of metal catalytic centers into an indium-based metal-organic framework （MOF）,obtained a series of isostructural In-MOFs,including three MOFs with mononuclear metal catalytic center （M-In）,three MOFs with dinuclear homometallic catalytic centers （MM-In）,and three MOFs with dinuclear heterometallic catalytic centers (MM’-In,M/M’=Co（II）,Ni（II）,Zn（II）).It was found that all these MOFs can serve as catalysts for photochemical CO2reduction to *** with M-In,the catalytic activities of MM-In were obviously enhanced,and that of MM’-In was further significantly *** example,the CO yield of Co Zn-In reaches a record value of 12,700μmol g-1h-1,which is 4.0,8.1,9.4,and 18.6-fold higher than that of Co Co-In,Zn Zn-In,Co-In,and Zn-In,***,the catalytic activities of Co Ni-In and Ni Zn-In also show different fold increase than those of corresponding dinuclear homometallic and mononuclear metallic *** results of control experiments and theoretical calculation reveal that the higher catalytic activities of MM-In over M-In can be ascribed to the DMSC effect,and the higher catalytic activity of MM’-In over MM-In can be ascribed to the strengthened DMSC *** work reveals the relationship between DMSC effect and bimetal centers in CO2reduction,which provides new insights into the DMSC and paves a new way to design highly active catalysts for photochemical CO2reduction.

关键词： metal-organic frameworks dinuclear metal centers synergistic catalysis CO2 reduction photocatalysis

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Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

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computers, Materials & Continua 2025年第1期82卷 543-560页

作者： Hengyang Liu Yang Yuan Pengcheng Ren Chengyun Song Fen Luo school of computer Science and engineering Chongqing University of TechnologyChongqing400054China College of Artificial Intelligence Chongqing Technology and Business UniversityChongqing400067China

Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution ***,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than *** design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these *** be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled *** introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in *** the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local *** this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation *** two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation *** only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

关键词： Semi-supervised medical image segmentation contrastive learning stochastic augmented

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Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

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computer Modeling in engineering & Sciences 2025年第1期142卷 357-384页

作者： Mohd Anjum Naoufel Kraiem Hong Min Ashit Kumar Dutta Yousef Ibrahim Daradkeh Department of computer engineering Aligarh Muslim UniversityAligarh202002India College of computer Science King Khalid UniversityAbha61413Saudi Arabia school of Computing Gachon UniversitySeongnam13120Republic of Korea Department of computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of computer engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

关键词： computer vision feature selection machine learning region detection texture analysis image classification medical images

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