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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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Approach from the laboratory to the field: New strategies in the control of mosquito-borne infectious diseases

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Science China Life Sciences 2025年第1期68卷 303-305页

作者： Ziqian Xu Lianpan Dai Jun Liu National key laboratory of intelligent Tracking and Forecasting for Infectious Diseases (NITFID) National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and Prevention CAS key laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of Sciences

Vector-borne viruses, transmitted among humans and animal hosts through bites of vectors such as mosquitoes and ticks,pose a great and ongoing threat to public health and biosafety(Yang et al., 2022).Among them, dengue virus-induced epidemics, have dramatically increased worldwide in recent decades. The report released by the World Health Organization(WHO) at the end of 2023 showed that dengue epidemic globally surged in2023, with the number of reported cases nearing a historical peak.

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Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping

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Journal of Energy Chemistry 2025年第1期100卷 565-577页

作者： Shuquan Wang Feng Gao Zhan Ma Hao Tian Yusen Zhang School of control Science and Engineering Shandong University key Lab of Power System intelligent Dispatch and control of Ministry of Education Shandong University

As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and *** on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and *** robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.

关键词： Lithium-ion battery Battery prognostics Fusion model Convolutional neural network Transfer learning

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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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An NIR-Ⅱ-emissive Ru(Ⅱ) metallacycle for efficient and safe immunotherapy

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Science China Chemistry 2025年第1期68卷 5-7页

作者： Chonglu Li Jianbo Zhan Biao Dong Institute of Health Inspection and Testing Hubei Provincial Center for Disease Control and Prevention state key laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University

Immunotherapy, as a groundbreaking cancer treatment, has gained considerable attention for its utilization of the body’s immune system to combat tumors [1]. This therapy induces tumor cell apoptosis, triggering a sustained anti-tumor immune response and effectively preventing tumor recurrence and metastasis [2]. The fundamental principle lies in inducing tumor cell death through specific treatment modalities, leading to the release of a cascade of signaling molecules and antigens, thereby mobilizing the immune system to actively identify and attack tumor cells [3].

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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： Contrastive Learning

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Low-cost and fluoride-free synthesis of MFI zeolite nanosheets with enhanced stability for benzene alkylation with ethanol

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Journal of Energy Chemistry 2025年第1期100卷 458-468页

作者： Peng Zhu Cun Liu Yiren Yu Guoshu Gao Yumeng Zhao Xiongfu Zhang Guodong Liu Guohui Yang state key laboratory of Fine Chemicals School of Chemical Engineering Dalian University of Technology state key laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences

Zeolite nanosheets with a short b-axis thickness are highly desirable in lots of catalytic reactions due to their reduced diffusion resistance. Nevertheless, conventional synthesis methods usually require expensive structure-directing agents(SDAs), pricey raw materials, and eco-unfriendly fluorine-containing additives. Here, we contributed a cost-effective and fluoride-free synthesis method for synthesizing high-quality MFI zeolite nanosheets through a Silicalite-1(Sil-1) seed suspension and urea cooperative strategy, only with inexpensive colloidal silica as the Si source. Our approach was effective for synthesizing both Sil-1 and aluminum-containing ZSM-5 nanosheets. By optimizing key synthesis parameters,including seed aging time, seed quantity, and urea concentration, we achieved precise control over the crystal face aspect ratio and b-axis thickness. We also revealed a non-classical oriented nanosheet growth mechanism, where Sil-1 seeds induced the formation of quasi-ordered precursor particles, and the(010)crystal planes of these particles facilitated urea adsorption, thereby promoting c-axis-oriented *** obtained ZSM-5 nanosheets exhibited exceptional catalytic performance in the benzene alkylation with ethanol, maintaining stability for over 500 h, which is 5 times longer than traditional ZSM-5 catalysts. Furthermore, large-scale production of ZSM-5 nanosheets was successfully carried out in a 3 L highpressure autoclave, yielding samples consistent with those from laboratory-scale synthesis. This work marks a significant step forward in the sustainable and efficient production of MFI nanosheets using inexpensive and environmentally friendly raw materials, offering the broad applicability in catalysis.

关键词： Aspect ratio

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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： Semiconductor doping

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Distributed optimal consensus control for multiagent systems based on event-triggered and prioritized experience replay strategies

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Science China(Information Sciences) 2025年第1期68卷 306-321页

作者： Cuijuan ZHANG Lianghao JI Shasha YANG Xing GUO Huaqing LI Chongqing key laboratory of Image Cognition Chongqing University of Posts and Telecommunications key laboratory of intelligent Perception and Computing of Anhui Province Anqing Normal University College of Electronic and Information Engineering Southwest University

This study uses event-triggered(ET) and reinforcement learning methods to investigate the optimal consensus control problem for cooperative-competitive multiagent systems. It proposes a novel distributed ET control strategy, which relies on a prioritized experience replay(PER) policy. This strategy not only conserves communication resources but also ensures acceptable system performance. To implement the proposed method, actor-critic(AC) dual-structured neural networks(NNs) are used to approximate the value function and control policy. In the AC NNs, the weight estimates for the NNs are updated at the moment of event triggering, resulting in a nonperiodic weight adjustment pattern. This approach decreases the computational cost in comparison with the traditional ET mechanism. The PER-based ET mechanism makes full use of valid historical data and effectively establishes a balance between system performance and communication resource ***, it does not require the following two conditions in most existing studies:(1) requirement of the system dynamics model to be known, and(2) persistent excitation. In addition, Zeno behavior is excluded from this study. Finally, a simulation is conducted to confirm the validity of the suggested approach.

关键词： multiagent systems event-triggered mechanism prioritized experience replay dual-structured neural networks optimal consensus control

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Mechanical behavior and failure mechanisms of rock bolts subjected to static-dynamic loads

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International Journal of mining Science and Technology 2024年第3期34卷 281-288页

作者： Hongpu Kang Guiyang Yuan Linpo Si Fuqiang Gao Jinfu Lou Jinghe Yang Shuangyong Dong CCTEG Coal Mining Research InstituteBeijing 100013China state key laboratory of intelligent coal mining and strata control Beijing 100013China

This study explores the effects of dynamic and static loading on rock bolt performance a key factor in maintaining the structural safety of coal mine roadways susceptible to coal *** a housemade load frame to simulate various failure scenarios,pretension-impact-pull tests on rock bolts were conducted to scrutinize their dynamic responses under varied static load conditions and their failure traits under combined *** experimental results denote that with increased impact energy,maximum and average impact loads on rock bolts escalate significantly under pretension,initiating plastic deformation beyond a certain *** minor reductions in the yield load due to impactinduced damage,pretension aids in constraining post-impact deformation rate and fluctuation degree of rock ***,impact-induced plastic deformation causes internal microstructure dislocation,fortifying the stiffness of the rock bolt support *** magnitude of this fortification is directly related to the plastic deformation induced by the *** findings provide crucial guidance for designing rock bolt support in coal mine roadway excavation,emphasizing the necessity to consider both static and dynamic loads for improved safety and efficiency.

关键词： Rock bolt Pretension Static and dynamic load Impact

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