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Unlocking the stable interface in aqueous zinc-ion battery with multifunctional xylose-based electrolyte additives

Journal of Energy Chemistry 2025年第1期100卷 770-778页

作者： Xiaoqin Li Jian Xiang Lu Qiu Xiaohan Chen Yinkun Zhao Yujue Wang Qu Yue Taotao Gao Wenlong Liu Dan Xiao Zhaoyu Jin Panpan Li institute for Advanced Study Chengdu University School of Mechanical engineering Chengdu University College of Food and Biological engineering Chengdu University institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China College of Materials Science and engineering Sichuan University

The growth of dendrites and the side reactions occurring at the Zn anode pose significant challenges to the commercialization of aqueous Zn-ion batteries（AZIBs）. These challenges arise from the inherent conflict between mass transfer and electrochemical kinetics. In this study, we propose the use of a multifunctional electrolyte additive based on the xylose（Xylo） molecule to address these issues by modulating the solvation structure and electrode/electrolyte interface, thereby stabilizing the Zn anode. The introduction of the additive alters the solvation structure, creating steric hindrance that impedes charge transfer and then reduces electrochemical kinetics. Furthermore, in-situ analyses demonstrate that the reconstructed electrode/electrolyte interface facilitates stable and rapid Zn2+ion migration and suppresses corrosion and hydrogen evolution reactions. As a result, symmetric cells incorporating the Xylo additive exhibit significantly enhanced reversibility during the Zn plating/stripping process, with an impressively long lifespan of up to 1986 h, compared to cells using pure ZnSO4electrolyte. When combined with a polyaniline cathode, the full cells demonstrate improved capacity and long-term cyclic stability. This work offers an effective direction for improving the stability of Zn anode via electrolyte design, as well as highperformance AZIBs.

关键词： Solvation

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A comprehensive investigation on nonlinear vibration and bending characteristics of bio-inspired helicoidal laminated composite structures

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 81-100页

作者： S.SAURABH R.KIRAN D.SINGH R.VAISH V.S.CHAUHAN School of Mechanical and Materials engineering Indian Institute of Technology Mandi

Bio-inspired helicoidal composite laminates, inspired by the intricate helical structures found in nature, present a promising frontier for enhancing the mechanical properties of structural designs. Hence, this study provides a comprehensive investigation into the nonlinear free vibration and nonlinear bending behavior of bio-inspired composite plates. The inverse hyperbolic shear deformation theory(IHSDT) of plates is employed to characterize the displacement field, with the incorporation of Green-Lagrange nonlinearity. The problem is modeled using the C0finite element method(FEM), and an in-house code is developed in the MATLAB environment to solve it numerically. Various helicoidal layup configurations including helicoidal recursive(HR), helicoidal exponential(HE), helicoidal semi-circular(HS), linear helicoidal(LH), and Fibonacci helicoidal(FH)with different layup sequences and quasi-isotropic configurations are studied. The model is validated, and parametric studies are conducted. These studies investigate the effects of layup configurations, side-to-thickness ratio, modulus ratios, boundary conditions, and loading conditions at different load amplitudes on the nonlinear vibration and nonlinear bending behaviors of bio-inspired composite plates. The results show that the laminate sequence exerts a substantial impact on both nonlinear natural frequencies and nonlinear bending behaviors. Moreover, this influence varies across different side-to-thickness ratios and boundary conditions of the bio-inspired composite plate.

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Data-Driven Healthcare:The Role of Computational Methods in Medical Innovation

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Computer Modeling in engineering & Sciences 2025年第1期142卷 1-48页

作者： Hariharasakthisudhan Ponnarengan Sivakumar Rajendran Vikas Khalkar Gunapriya Devarajan Logesh Kamaraj Department of Mechanical engineering Dr.Mahalingam College of Engineering and TechnologyPollachiCoimbatoreTamil Nadu642003India Department of Mechanical engineering JKKN College of Engineering and TechnologyKumarapalayamNamakkalTamil Nadu637209India Department of Mechanical engineering Gharda Institute of TechnologyLavelMaharashtra415708India Department of Electrical and Electronics engineering Sri Eshwar College of EngineeringCoimbatoreTamilnadu641202India Department of Mechanical engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India

The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical *** review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health *** findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and *** synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment ***,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease *** review emphasizes the need for interdisciplinary collaboration to further advance this *** research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and *** fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.

关键词： Computational models biomedical engineering bioinformatics machine learning wearable technology

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and engineering College of Engineering GuindyAnna UniversityChennai600025India Department of Information technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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Rock fracture mechanism of buffer blasting with cushion layer at the borehole bottom

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 325-334页

作者： Xinguang Zhu Chenxi Ding Zhe Sui Hong Su Xu Guo School of Civil and Resources engineering University of Science and Technology BeijingBeijing 100083China Key Laboratory for Mechanics in Fluid Solid Coupling Systems Institute of MechanicsChinese Academy of SciencesBeijing 100190China School of Mechanics and Civil engineering China University of Mining and Technology(Beijing)Beijing 100083China School of Chemical and Blasting engineering Anhui University of Science and TechnologyHuainan 232001China

This study primarily investigates the rock fracture mechanism of bottom cushion layer blasting and explores the effects of the bottom cushion layer on rock *** involves analyses of the evolution patterns of blasting stress,characteristics of crack distribution,and rock fracture features in the ***,blasting model experiments were carried out using the dynamic caustics principle to investigate the influence of bottom cushion layers and initiation methods on the integrity of the bottom rock *** experimental results indicate that the combined use of bottom cushion layers and inverse initiation effectively protects the integrity of the bottom rock ***,the process of stress wave propagation and dynamic crack propagation in rocks was simulated using the continuum-discontinuum element method(CDEM)and the Landau explosion source model,with varying thicknesses of bottom cushion *** numerical simulation results indicate that with increasing cushion thickness,the absorption of energy generated by the explosion becomes more pronounced,resulting in fewer cracks in the bottom rock *** illustrates the positive role of the cushion layer in protecting the integrity of the bottom rock mass.

关键词： bottom cushion layer blasting crack propagation continuum-discontinuum element method dynamic stress intensity factor

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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li institute of Biomechanics and Medical engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University institute of Nuclear and New Energy technology Tsinghua University Shanghai Aerospace Control technology institute Shanghai Key Laboratory of Aerospace Intelligent Control technology College of Automation engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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Mechanistic insight into the synergy between nickel single atoms and nanoparticles on N-doped carbon for electroreduction of CO2

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Journal of Energy Chemistry 2025年第1期100卷 327-336页

作者： Mingdong Sun Wenwen Guan Cailing Chen Chao Wu Xiaoling Liu Biao Meng Tao Chen Yu Han Jun Wang Shibo Xi Yu Zhou State Key Laboratory of Materials-Oriented Chemical engineering College of Chemical Engineering Nanjing Tech University Advanced Membranes and Porous Materials Center Physical Sciences and Engineering Division King Abdullah University of Science and Technology institute of Sustainability for Chemicals Energy and Environment A*STAR (Agency for Science Technology and Research) College of Materials Science and engineering Sichuan University

The synergy of single atoms （SAs） and nanoparticles （NPs） has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction （CO2RR）;however,the rationalization of the SAs/NPs proportion remains one challenge for the catalyst ***,a Ni2+-loaded porous poly（ionic liquids）(PIL) precursor synthesized through the free radical self-polymerization of the ionic liquid monomer,1-allyl-3-vinylimidazolium chloride,was pyrolyzed to prepare the Ni,N co-doped carbon materials,in which the proportion of Ni SAs and NPs could be facilely modulated by controlling the annealing *** catalyst Ni-NC-1000 with a moderate proportion of Ni SAs and NPs exhibited high efficiency in the electrocatalytic conversion of CO2into *** Ni K-edge X-ray absorption near-edge structure （XANES） spectra and theoretical calculations were conducted to gain insight into the synergy of Ni SAs and *** charge transfer from Ni NPs to the surrounding carbon layer and then to the Ni SAs resulted in the electron-enriched Ni SAs active *** the electroreduction of CO2,the coexistence of Ni SAs and NPs strengthened the CO2activation and the affinity towards the key intermediate of*COOH,lowering the free energy for the potential-determining*CO2→*COOH step,and therefore promoted the catalysis efficiency.

关键词： Single atom catalyst Synergy effect Carbon catalyst Electrocatalysis

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High-energy-density lithium manganese iron phosphate for lithium-ion batteries: Progresses, challenges, and prospects

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Journal of Energy Chemistry 2025年第1期100卷 1-17页

作者： Bokun Zhang Xiaoyun Wang Shuai Wang Yan Li Libo Chen Handong Jiao Zhijing Yu Jiguo Tu State Key Laboratory of Advanced Metallurgy University of Science and Technology Beijing Research institute for Intelligent Wearable Systems The Hong Kong Polytechnic University institute of Advanced Structure technology Beijing Institute of Technology

The soaring demand for smart portable electronics and electric vehicles is propelling the advancements in high-energy–density lithium-ion *** manganese iron phosphate (LiMnxFe1-xPO4) has garnered significant attention as a promising positive electrode material for lithium-ion batteries due to its advantages of low cost,high safety,long cycle life,high voltage,good high-temperature performance,and high energy *** LiMnxFe1-xPO4has made significant breakthroughs in the past few decades,there are still facing great challenges in poor electronic conductivity and Li-ion diffusion,manganese dissolution affecting battery cycling performance,as well as low tap *** review systematically summarizes the reaction mechanisms,various synthesis methods,and electrochemical properties of LiMnxFe1-xPO4to analyze reaction processes accurately and guide material ***,the main challenges currently faced are concluded,and the corresponding various modification strategies are discussed to enhance the reaction kinetics and electrochemical performance of LiMnxFe1-xPO4,including multi-scale particle regulation,heteroatom doping,surface coating,as well as microscopic morphology ***,in view of the current research challenges faced by intrinsic reaction processes,kinetics,and energy storage applications,the promising research directions are *** importantly,it is expected to provide key insights into the development of high-performance and stable LiMnxFe1-xPO4materials,to achieve practical energy storage requirements.

关键词： Lithium manganese iron phosphate High energy density Lithium-ion batteries Reaction mechanism Tap density

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Co/Co_(7)Fe_(3)heterostructures with controllable alloying degree on carbon spheres as bifunctional electrocatalyst forrechargeable zinc-air batteries

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 476-487页

作者： Junkang Chen Yongyue Zhuang Yanxin Qiao Yu Zhang Aihua Yuan Hu Zhou School of Materials Science and engineering Jiangsu University of Science and TechnologyZhenjiang 212003China School of Environmental and Chemical engineering Jiangsu University of Science and TechnologyZhenjiang 212003China

Exploring efficient and nonprecious metal electrocatalysts of oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)is crucial for developing rechargeable zinc-air batteries(ZABs).Herein,an alloying-degree control strategy was employed to fabricate nitrogen-doped carbon sphere(NCS)decorated with dual-phase Co/Co_(7)Fe_(3)heterojunctions(CoFe@NCS).The phase composition of materials has been adjusted by controlling the alloying *** optimal CoFe_(0.08)@NCS electrocatalyst displays a half-wave potential of 0.80 V for ORR and an overpotential of 283 mV at 10 mA·cm^(-2)for OER in an alkaline *** intriguing bifunctional electrocatalytic activity and durability is attributed to the hierarchically porous structure and interfacial electron coupling of highly-active Co_(7)Fe_(3)alloy and metallic Co *** the CoFe_(0.08)@NCS material is used as air-cathode catalyst of rechargeable liquid-state zinc-air battery(ZAB),the device shows a high peak power-density(157 mW·cm^(-2))and maintains a stable voltage gap over 150 h,outperforming those of the benchmark(Pt/C+RuO_(2))-based *** particular,the as-fabricated solid-state flexible ZAB delivers a reliable compatibility under different bending *** work provides a promising strategy to develop metal/alloy-based electrocatalysts for the application in renewable energy conversion technologies.

关键词： bifunctional electrocatalysts oxygen reduction reaction oxygen evolution reaction zinc-air battery metal/alloy carbon sphere

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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical engineering and Automation Luoyang Institute of Science and Technology School of Mechanical engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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