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A binary eutectic electrolyte design for high-temperature interface-compatible Zn-ion batteries

Journal of Energy Chemistry 2025年第2期101卷 587-597页

作者： Guomin Li Wentao Wen Kefeng Ouyang Yanyi Wang Jianhui Zhu Ming Yang Hongwei Mi Ning Zhao Peixin Zhang Dingtao Ma College of Chemistry and Environmental Engineering Shenzhen University School of Materials Science and Engineering Harbin Institute of Technology (Shenzhen) School of Materials and Environmental Engineering Shenzhen Polytechnic Guangdong Flexible Wearable Energy and Tools Engineering technology Research Center

The deterioration of aqueous zinc-ion batteries（AZIBs） is confronted with challenges such as unregulated Zn2+diffusion,dendrite growth and severe decay in battery performance under harsh ***,a design concept of eutectic electrolyte is presented by mixing long chain polymer molecules,polyethylene glycol dimethyl ether（PEGDME）,with H2O based on zinc trifluoromethyl sulfonate(Zn（OTf）2),to reconstruct the Zn2+solvated structure and in situ modified the adsorption layer on Zn electrode *** dynamics simulations（MD）,density functional theory（DFT） calculations were combined with experiment to prove that the long-chain polymer-PEGDME could effectively reduce side reactions,change the solvation structure of the electrolyte and priority absorbed on Zn（002）,achieving a stable dendrite-free Zn *** to the comprehensive regulation of solvation structure and zinc deposition by PEGDME,it can stably cycle for over 3200 h at room temperature at 0.5 mA/cm2and 0.5 mAh/*** at high-temperature environments of 60℃,it can steadily work for more than 800 cycles（1600 h）.Improved cyclic stability and rate performance of aqueous Zn‖VO2batteries in modified electrolyte were also achieved at both room and high *** that,the demonstration of stable and high-capacity Zn‖VO2pouch cells also implies its practical application.

关键词： Dendrite suppression Eutectic electrolyte High temperature Solvation structure Zn anode

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Enhancing microbial superoxide generation and conversion to hydroxyl radicals for enhanced bioremediation using iron-binding ligands

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Journal of Environmental Sciences 2025年第1期147卷 597-606页

作者： Yuhan Wang Xue Ning Jinsong Liang Aijie Wang Jiuhui Qu School of Civil and Environmental Engineering Harbin Institute of TechnologyShenzhen 518055China SFI MaREI Centre for Energy Climate and MarineEnvironmental Research InstituteUniversity College CorkCorkIreland Center for Water and Ecology Tsinghua UniversityBeijing 100084China

Harnessing bacteria for superoxide production in bioremediation holds immense promise,yet its practical application is hindered by slow production rates and the relatively weak redox potential of *** study delves into a cost-effective approach to amplify superoxide production using an Arthrobacter strain,a prevalent soil bacterial *** research reveals that introducing a carbon source along with specific iron-binding ligands,including deferoxamine(DFO),diethylenetriamine pentaacetate(DTPA),citrate,and oxalate,robustly augments microbial superoxide ***,our findings suggest that these iron-binding ligands play a pivotal role in converting superoxide into hydroxyl radicals by modulating the electron transfer rate between Fe(Ⅲ)/Fe(Ⅱ)and ***,among the tested ligands,only DTPA emerges as a potent promoter of this conversion process when complexed with Fe(Ⅲ).We identify an optimal Fe(Ⅲ)to DTPA ratio of approximately 1:1 for enhancing hydroxyl radical production within the Arthrobacter *** research underscores the efficacy of simultaneously introducing carbon sources and DTPA in facilitating superoxide production and its subsequent conversion to hydroxyl radicals,significantly elevating bioremediation ***,our study reveals that DTPA augments superoxide production in cultures of diverse soils,with various soil microorganisms beyond Arthrobacter identified as contributors to superoxide *** emphasizes the universal applicability of DTPA across multiple bacterial *** conclusion,our study introduces a promising methodology for enhancing microbial superoxide production and its conversion into hydroxyl *** findings hold substantial implications for the deployment of microbial reactive oxygen species in bioremediation,offering innovative solutions for addressing environmental contamination challenges.

关键词： Superoxide Hydroxyl radical Iron-binding ligand Bioremediation DTPA

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From blocker to booster:Harnessing garnet surface chemistry for advanced solid-state electrolytes

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Nano Research 2025年第1期18卷 323-331页

作者： Jun Cheng Xuan Zhou Hongqiang Zhang Zhen Zeng Yuanyuan Li Yulong Zhu Yingsheng Liao Lijie Ci Deping Li State Key Laboratory of Precision Welding and Joining of Materials and Structures School of Materials Science and EngineeringHarbin Institute of Technology(Shenzhen)Shenzhen 518055China

Garnet electrolytes with high ionic conductivity and electrochemical stability are widely used as fillers to fabricate composite solid electrolytes(CPEs)within polymer ***,the performance of CPEs is significantly influenced by the surface characteristics of the garnet ***,the impact of garnet surface characteristics on CPEs was systematically investigated and a conversion from a typically unstable and Lewis basic surface to a more stable Lewis acidic surface was realized,which is shown to be more conductive to the improved performance of *** simultaneously removing the Li_(2)CO_(3)layer and applying a Li-Al-O coating,the influence of surface characteristics on CPEs was *** Lewis acid Li-Al-O surface coating not only promotes lithium salt dissociation,improving the ionic conductivity and ionic transfer number,but also prevents the reformation of the passive Lewis basic Li_(2)CO_(3)*** to garnet with a Lewis basic Li_(2)CO_(3)surface,the garnet modified with a Lewis acid Li-Al-O coating enhances CPEs,which exhibit an improved critical current density of 1.0 mA·cm^(-2)and highly stable lithium symmetric cell cycling for 400 h at 0.2 mA·cm^(-2).This research highlights the importance of surface chemistry in the design of high-performance solid-state batteries and presents a strategic modification approach for garnetbased CPEs.

关键词： garnet electrolyte composite solid electrolyte surface chemistry all-solid-state batteries

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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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Science China(Information Sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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Progress of crystalline carbon nitride in synthesis,atomic structure characterization and photocatalysis

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Nano Research 2025年第1期18卷 455-476页

作者： Yueyang Tan Zhongyan Zhang Yayun Pu Zongzhao Sun Limin Huang School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemistry Southern University of Science and TechnologyShenzhen 518055China School of Traffic and Environment Shenzhen Institute of Information TechnologyShenzhen 518172China College of Chemistry and Chemical Engineering Linyi UniversityLinyi 276000China School of Optoelectronic Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China

Crystalline carbon nitride(CCN)has emerged as a highly promising semiconductor photocatalyst with unique properties,such as enhanced charge migration rate,reduced carrier recombination probability,narrow band gap and improved light-harvesting efficiency,which are suitable for a wide range of applications in solar-to-chemical conversion,energy storage,therapeutic and environmental pollution *** the past few years,there has been an increasing number of reviews on CCN ***,most of these reviews mainly focus on synthesis methods,modification and applications,with less emphasis on the relationship between structures and properties,as well as on indepth exploration of the crystalline *** electronic instability of CCN presents challenges for conventional characterization techniques to directly and thoroughly investigate the relationship between its intrinsic atom structure and photocatalytic *** mini-review not only highlights the progress in CCN-based photocatalysts,with a focus on molten-salt synthesis(including solid-salt-induced crystallization),but also emphasizes the atomic structure characterization by specifically introducing the differential phase contrast(DPC)scanning transmission electron microscopy(STEM)technique,which is essential for enhancing our understanding of the crystal structure and photocatalytic mechanisms of ***,the review outlines the photocatalytic performance and puts forward potential challenges on CCN *** review will provide a clearer understanding of the relationship in developing precise customization strategies for CCN materials and ultimately explain the regularity and specificity of the enhanced performance in targeted photocatalytic systems.

关键词： crystalline carbon nitride synthesis characterization photocatalysis

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Electromagnetic three-dimensional vibration energy harvester based on an oblique cross-spring vibrator structure

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Science China(Technological Sciences) 2025年第1期68卷 185-202页

作者： Yaohui AN Yimin FAN Kefan XU Yuhao CHEN Yewei ZHANG Liqun CHEN School of Science Harbin Institute of TechnologyShenzhen 518055China College of Aerospace Engineering Shenyang Aerospace UniversityShenyang 110136China

This paper presents an electromagnetic three-dimensional vibration energy harvesting device that uses an oblique cross-spring vibrator structure to collect low-frequency vibration *** oblique cross-spring structure allowed the center vibrator to vibrate in any *** with six-directional coils,this configuration senses and harvests environmental vibrations across all directions and ***,the dynamics of the oblique cross-spring vibrator structure were analyzed and a finite element simulation was performed for the magnet arrangement and mode shape of the structure.A prototype is designed,fabricated,and experimentally *** results showed that the efficiency of the three-dimensional energy output was higher under the same excitation,which was 2.7 times higher than that of the unidirectional *** unidirectional output power with load is 0.94 mW at 11 Hz under 0.3 g base *** highest total output power of the prototype was 50 mW when it was fixed to the leg at a running speed of 8 km/***,the energy harvesting scheme proposed in this study has a considerable low-frequency output effect,which provides a novel means of improving the electrical performance of energy harvesting systems for self-powered IoT and sensor devices.

关键词： energy harvesting electromagnetic three-dimensional oblique cross-spring human motion

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A facile data-driven battery capacity estimation framework for on-road plug-in hybrid electric vehicle

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Progress in Natural Science:Materials International 2025年第01期 166-176页

作者： Jianlu Li Yanming Chen Tongxing Lei Jianguo Liu Guizheng Liu Zhaoyang Deng Xuebiao Wu Zhiyu Ding Yinghe Zhang Junwei Wu Yanan Chen Guangzhou Automobile Group Co. Ltd. School of Materials Science and Engineering Harbin Institute of Technology School of Materials and Environmental Engineering Shenzhen Polytechnic University School of Science Shenzhen Key Laboratory of Flexible Printed Electronics Technology Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application Harbin Institute of Technology School of Materials Science and Engineering Key Laboratory of Advanced Ceramics and Machining Technology of Ministry of Education Tianjin Key Laboratory of Composite and Functional Materials Tianjin University

A data-driven framework with strong generalization capabilities is proposed to effectively extract features and easily access battery capacity. This framework can make highly accurate predictions for the battery capacities of plug-in electric vehicles. The feature extraction process is entirely based on statistics, which are always available and can be generalized to various types of battery data. An improved ampere-hour integral method can easily access battery capacity with just short-charging segments lasting 500 s. Several machine-learning models are trained to verify the framework's effectiveness, with the best model achieving a test error of 0.84 % based on leave-one-out validation. SHAP values are used to provide a reasonable interpretation of the relationships between the constructed features and model outputs. The proposed framework offers advantages such as reduced computational resources, wide generalization, and high prediction accuracy, showing great potential for battery management.

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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Computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and technology Harbin Engineering UniversityHarbin150001China Information technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Ambient-Pressure Anisotropic Single-Gap Superconductivity in La-Trapped B_(26)Cages with Superior Hardness

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Chinese Physics Letters 2025年第1期42卷 177-191页

作者： Haoqi Chen Yushan Tang Haowen Jiang Xuehui Jiang Jialin Wang Chengyao Zhang Defang Duan Yanbin Ma Jing Dong School of Science Harbin University of Science and TechnologyHarbin 150080China School of Science Harbin Institute of Technology(Shenzhen)Shenzhen 518055China College of Physics Jilin UniversityChangchun 130012China School of Public Health Mudanjiang Medical UniversityMudanjiang 157011China

Recently,a breakthrough in the high-pressure synthesis of sp^(3)-bonded clathrate-like R-3m-L_(a)B_(8)featuring Latrapped B_(26)cages was *** the superconducting critical temperature(T_(c))and Vickers hardness(H_(v))of L_(a)B_(8)were estimated,the anisotropic superconductivity and superconducting gap nature remain *** stability of pure B_(26)cages under ambient conditions is of interest for exploring the B_(26)hardness and the role of La atoms in the L_(a)B_(8)*** resolving the Allen–Dynes modified Mc Millan equation and the anisotropic Eliashberg equations,it was found that L_(a)B_(8)exhibits anisotropic single-gap superconductivity with T_(c)=19.0~26.5 K,and the superconducting gap anisotropic ratio reaches 48.50%at 5 *** significantly,our work fundamentally validated the coupling of B-2p orbitals with the optical double-degenerate E_(g)phonon modes and A_(g)phonon mode from a novel *** H_(v)of B_(26)cages was determined to be 18.6 GPa,which is lower than that of La-trapped B_(26)cages(i.e.,L_(a)B_(8)),indicating that La acts as a hardness enhancer for L_(a)B_(8).

关键词： 6) anisotropic breakthrough

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Machine learning strategies for small sample size in materials science

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Science China Materials 2025年第2期68卷 387-405页

作者： Qiuling Tao JinXin Yu Xiangyu Mu Xue Jia Rongpei Shi Zhifu Yao Cuiping Wang Haijun Zhang Xingjun Liu School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhenShenzhen 518055China School of Computer Science&technology Harbin Institute of TechnologyShenzhenShenzhen 518055China Advanced institute for Materials Research(WPI-AIMR) Tohoku UniversitySendai 980-8577Japan Department of Materials Science and Engineering College of MaterialsXiamen UniversityXiamen 361005China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyShenzhenShenzhen 518055China

Machine learning (ML) has been widely used todesign and develop new materials owing to its low computational cost and powerful predictive capabilities. In recentyears, the shortcomings of ML in materials science have gradually emerged, with a primary concern being the scarcity ofdata. It is challenging to build reliable and accurate ML modelsusing limited data. Moreover, the small sample size problemwill remain long-standing in materials science because of theslow accumulation of material data. Therefore, it is importantto review and categorize strategies for small-sample learningfor the development of ML in materials science. This reviewsystematically sorts the research progress of small-samplelearning strategies in materials science, including ensemblelearning, unsupervised learning, active learning, and transferlearning. The directions for future research are proposed, including few-shot learning, and virtual sample *** importantly, we emphasize the significance of embedding material domain knowledge into ML and elaborate on thebasic idea for implementing this strategy.

关键词： material design machine learning small sample size few-shot learning material domain knowledge

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