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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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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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Stability and Stabilization of Sampled-Data Based LFC for Power systems:A Data-Driven Method

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 291-293页

作者： Yu-Long Fan Chuan-Ke Zhang Yong He the School of Automation China University of Geosciences the Hubei key laboratory of Advanced Control and intelligent Automation for complex systems the Engineering Research Center of intelligent Technology for Geo-Exploration Ministry of Education IEEE

Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.

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Practical approach for detecting k-nonseparability of multipartite quantum states

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science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 32-48页

作者： Jie Guo Jinchuan Hou Xiaofei Qi Kan He College of Mathematics Taiyuan University of Technology School of Mathematics and Statistics Shanxi University key laboratory of complex systems and Data science of Ministry of Education Shanxi University

The instantaneous detection of entanglement in quantum states poses a significant challenge in the fields of quantum computation and quantum information, and there are no practical methods or tools to complete this task satisfactorily so far. We propose a sufficient and necessary criterion of k-nonseparability for any n-partite finite or infinite dimensional systems, 2 ≤ k ≤ n. This criterion serves as the foundation for a practical scheme designed to detect entanglement and k-nonseparability across multipartite finite-dimensional systems. To exemplify the application of our scheme, we have developed a software tool that facilitates swift and precise identification of entanglement, k-nonseparability, and genuine entanglement within n-qubit systems, specifically tailored for systems with 2 ≤ n ≤ 4.

关键词： multipartite systems entanglement states entanglement witnesses ${\bm~k}$-nonseparability

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Single-cell omics: experimental workflow, data analyses and applications

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science China Life sciences 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi Department of Clinical laboratory the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City) Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang University key laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Frontier science Center for Stem Cells School of Life Sciences and TechnologyTongji University Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Research Institute of intelligent Computing Zhejiang Lab Shanghai Research Institute for intelligent Autonomous systems Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of Medicine National key laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang University Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Beijing Institutes of Life science Chinese Academy of Sciences Peking-Tsinghua Center for Life sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology (CQB)Peking University Zhejiang key laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women's HospitalZhejiang University School of Medicine key laboratory of Advanced Theory and Application in Statistics and Data science-MOE School of StatisticsEast China Normal University

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome,proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial sc RNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

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Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

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Atmospheric and Oceanic science Letters 2025年第1期18卷 18-23页

作者： Jiarui Cai Bo Sun Huijun Wang Yi Zheng Siyu Zhou Huixin Li Yanyan Huang Peishu Zong Collaborative Innovation Center on forecast and Evaluation of Meteorological Disasters/key laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern Marine science and Engineering Guangdong laboratory(Zhuhai) ZhuhaiChina China Meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu Meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

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Effects of aggregate size distribution and carbon nanotubes on the mechanical properties of cemented gangue backfill samples under true triaxial compression

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 311-324页

作者： Qian Yin Fan Wen Zhigang Tao Hai Pu Tianci Deng Yaoyao Meng Qingbin Meng Hongwen Jing Bo Meng Jiangyu Wu State key laboratory of intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering China University of Mining and TechnologyXuzhou 221116China State key laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and TechnologyHuainan 232001China State key laboratory for Geomechanics&Deep Underground Engineering China University of Mining and Technology(Beijing)Beijing 100083China key laboratory of Xinjiang Coal Resources Green Mining Ministry of EducationXinjiang Institute of EngineeringUrumqi 830023China

The mechanical behavior of cemented gangue backfill materials(CGBMs)is closely related to particle size distribution(PSD)of aggregates and properties of cementitious ***,the true triaxial compression tests,CT scanning,SEM,and EDS tests were conducted on cemented gangue backfill samples(CGBSs)with various carbon nanotube concentrations(P_(CNT))that satisfied fractal theory for the PSD of *** mechanical properties,energy dissipations,and failure mechanisms of the CGBSs under true triaxial compression were systematically *** results indicate that appropriate carbon nanotubes(CNTs)effectively enhance the mechanical properties and energy dissipations of CGBSs through micropore filling and microcrack bridging,and the optimal effect appears at P_(CNT)of 0.08wt%.Taking PSD fractal dimension(D)of 2.500 as an example,compared to that of CGBS without CNT,the peak strength(σ_(p)),axial peak strain(ε_(1,p)),elastic strain energy(Ue),and dissipated energy(U_(d))increased by 12.76%,29.60%,19.05%,and90.39%,***,excessive CNTs can reduce the mechanical properties of CGBSs due to CNT agglomeration,manifesting a decrease inρ_(p),ε_(1,p),and the volumetric strain increment(Δε_(v))when P_(CNT)increases from 0.08wt%to 0.12wt%.Moreover,the addition of CNTs improved the integrity of CGBS after macroscopic failure,and crack extension in CGBSs appeared in two modes:detour and pass through the ***σ_(p)and U_(d)firstly increase and then decrease with increasing D,and porosity shows the opposite ***ε_(1,p)andΔε_(v)are negatively correlated with D,and CGBS with D=2.150 has the maximum deformation parameters(ε_(1,p)=0.05079,Δε_(v)=0.01990)due to the frictional slip effect caused by coarse *** increasing D,the failure modes of CGBSs are sequentially manifested as oblique shear failure,"Y-shaped"shear failure,and conjugate shear failure.

关键词： cemented gangue backfill materials particle size distribution true triaxial compression test carbon nanotubes mechanical properties failure modes

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SensFL:Privacy-Preserving Vertical Federated Learning with Sensitive Regularization

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Computer Modeling in Engineering & sciences 2025年第1期142卷 385-404页

作者： Chongzhen Zhang Zhichen Liu Xiangrui Xu Fuqiang Hu Jiao Dai Baigen Cai Wei Wang School of Automation and Intelligence Beijing Jiaotong UniversityBeijing100044China Shuohuang Railway Development Co. Ltd.National Energy GroupCangzhou062350China Beijing key laboratory of Security and Privacy in intelligent Transportation School of Computer Science and TechnologyBeijing Jiaotong UniversityBeijing100044China

In the realm of intelligent Railway Transportation systems,effective multi-party collaboration is crucial due to concerns over privacy and data *** Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training ***,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the *** vulnerability poses a significant threat to VFL-based intelligent railway transportation *** this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in ***,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared *** reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the *** experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of *** results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning *** results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.

关键词： Vertical federated learning privacy defenses

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Sequence-activated chemiluminescence probes: programmable illumination for high-contrast in vivo sensing and imaging

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science China Chemistry 2025年第01期 26-34页

作者： Yutao Zhang Juan Li Wei-Hong Zhu Zhiqian Guo Shanghai key laboratory of Functional Materials Chemistry Joint International Research Laboratory of Precision Chemistry and Molecular Engineering Institute of Fine Chemicals School of Chemistry and Molecular Engineering East China University of Science and Technology State key laboratory of Bioreactor Engineering Shanghai Frontier Science Research Base of Optogenetic Techniques for Cell MetabolismEast China University of Science & Technology

Sequence-activated chemiluminescence(CL) technique offers a unique approach to detecting specific biomolecules or cellular events with high sensitivity and spatial resolution. The fundamental principle behind sequence-activated CL probes involves tailoring highly-controllable CL signals in response to specific sequences or events of interest, thus enabling high-contrast signals under complex physiological conditions. This sequential activation could be triggered by enzymes, reactive oxygen species(ROS), biothiols or changes in the microenvironment. These unique sequence-activated CL probes bestow several advantages, including minimal background, deep-tissue penetration, and the ability of non-invasive or minimally invasive detection. The sequence-activated CL techniques represent a significant advancement in molecular imaging and diagnostics. In this mini-review, we focus on the latest developments in sequential CL probes, addressing the technical challenges and discussing the future trajectory by harnessing CL signaling and molecular recognition.

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Identifying the dynamic behaviors in complete reconstruction of Co-based complex precatalysts during electrocatalytic oxygen evolution

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Journal of Energy Chemistry 2025年第1期100卷 226-233页

作者： Jingfang Zhang Danyang Wu Linke Cai Youluan Lu Fanpeng Cheng Lijuan Shi Qun Yi Yao Liu Yi Huang key laboratory of Green Chemical Engineering Process of Ministry of Education Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education Hubei Key Laboratory of Novel Reactor and Green Chemical Technology School of Chemical Engineering & Pharmacy Wuhan Institute of Technology key laboratory of Pesticide & Chemical Biology of Ministry of Education Engineering Research Center of Photoenergy Utilization for Pollution Control and Carbon Reduction of Ministry of Education College of Chemistry Central China Normal University College of Materials science and Engineering Fuzhou University

Transition metal-based nanomaterials have emerged as promising electrocatalysts for oxygen evolution reaction（OER）. Considerable research efforts have shown that self-reconstruction occurs on these nanomaterials under operating conditions of OER process. However, most of them undergo incomplete reconstruction with limited thickness of reconstruction layer, leading to low component utilization and arduous exploration of real catalytic mechanism. Herein, we identify the dynamic behaviors in complete reconstruction of Co-based complexes during OER. The hollow phytic acid（PA） cross-linked CoFe-based complex nanoboxes with porous nanowalls are designed because of their good electrolyte penetration and mass transport ability, in favor of the fast and complete reconstruction. A series of experiment characterizations demonstrate that the reconstruction process includes the fast substitution of PA by OH-to form Co（Fe）(OH)xand subsequent potential-driven oxidation to Co（Fe）OOH. The obtained CoFeOOH delivers a low overpotential of 290 mV at a current density of 10 mA cm-2and a long-term *** experiment results together with theory calculations reveal that the Fe incorporation can result in the electron rearrangement of reconstructed CoFeOOH and optimization of their electronic structure,accounting for the enhanced OER activity. The work provides new insights into complete reconstruction of metal-based complexes during OER and offers guidelines for rational design of high-performance electrocatalysts.

关键词： Complete reconstruction Co-based complex Precatalysts Oxygen evolution reaction

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