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Tris-based protic ionic liquids with multiple adsorption effects enabling excellent anti-wear and anti-corrosion performances

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Journal of Molecular Liquids 2025年 425卷

作者： Ge, Shanqin Chen, Long Li, Wei Cai, Qingzhao Gong, Genxiang Wu, Junhao Yu, Jinhong Nishimura, Kazuhito Jiang, Nan Cai, Tao School of Materials Science and Chemical Engineering Ningbo University Zhejiang Ningbo315211 China State Key Laboratory of Advanced Marine Materials Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Advanced Nano-processing Engineering Lab Mechanical Engineering Kogakuin University Tokyo192-0015 Japan

Molecular additives have garnered widespread attention in the fields of lubrication and corrosion protection due to their designable interfacial film-forming efficiency. Herein, three protic ionic liquids (PILs) with the same cation but different anions have been designed and synthesized. Their physicochemical properties, tribological properties and anti-corrosion properties were systematically investigated. Results demonstrated that under dynamic steel-on-steel friction conditions, the PILs can maximally reduce the running-in period, friction coefficient, and wear rate by 90 %, 40 %, and 97 %, respectively, compared to the commercial ionic liquid, BMIMPF6. Under static copper corrosion conditions, the PILs efficiently adsorb onto the copper surface and formed a dense oxide layer on its surface, thus achieving excellent corrosion protection for the copper substrates. Molecular dynamics (MD) simulation results indicate that the exceptional lubricity of PILs is due to its ability to rapidly adsorb onto the metal surface through multiple interaction sites on both its anionic and cationic components, thereby reducing the running-in period and swiftly establishing a lubricating film. This study provides insights into the development of molecular additives featuring multiple adsorption sites, with potential applications in the fields of tribology and corrosion engineering. © 2025

关键词： Tribology

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DLLP: a deep learning-based layer prediction network for three-dimensional fluorescence microscopy

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Optics Express 2025年第9期33卷 20063-20081页

作者： Zhang, Runnan Li, Yifei Gong, Ying Hao, Xiang Tu, Shijie Feng, Ruili Gu, Zhenhong Wang, Wenbao Li, Zhifei Lu, Wei He, Mubin Chu, Jinghui School of Electrical and Information Engineering Tianjin University Tianjin 300072 China State Key Laboratory of Extreme Photonics and Instrumentation Centre for Optical and Electromagnetic Research College of Optical Science and Engineering International Research Center for Advanced Photonics Zhejiang University Hangzhou 310058 China Guangdong-Hong Kong-Macau Institute of CNS Regeneration (GHMICR) Jinan University Guangdong Province Guangzhou 510632 China Nuohai Life Science (Shanghai) Co. Ltd. Shanghai 201615 China Beijing Totop Technology Co. Ltd. Beijing 100043 China

High light-throughput microscopy stands as a potent tool in biological research, spanning applications from cell functional analysis to brain science. The pursuit of achieving elevated light throughput alongside rapid imaging speeds without the need for additional optical hardware has emerged as a pivotal focus in biomedical research. To address this challenge, we introduced a cutting-edge framework known as the deep learning-based layer predictor (DLLP). By integrating a convolutional neural network (CNN) with the inter-layer dynamic and morphological attention mechanism (IDMA) within a transformer architecture, DLLP employed a tomographic prediction technique capable of reducing the number of scanning layers in three-dimensional (3D) microscopy by over 70%, while maintaining light throughput and image fidelity. The DLLP framework significantly improved imaging speed and quality through two complementary strategies: robust unsupervised denoising to effectively mitigate electrical noise in the xy-plane caused by faster imaging and sparse recovery along the Z-axis to ensure high-quality 3D reconstruction without compromising spatial resolution. The DLLP framework has demonstrated consistent performance across various microscopy modalities, including stimulated emission depletion (STED) microscopy, fluorescence modulation optical sectioning tomography (FMOST) microscopy, multi-photon microscopy, and light-sheet microscopy, surpassing traditional methods and existing deep learning approaches in terms of accuracy and image quality. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Confocal microscopy Fluorescence microscopy Imaging techniques Scanning microscopy Spatial resolution Three dimensional microscopy

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Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Huang, Haoqi Wang, Ping Pei, Jianhua Wang, Jiacheng Alexanian, Shahen Niyato, Dusit Lassonde School of Engineering York University TorontoONM3J 1P3 Canada State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Engineering Nanyang Technological University Singapore

The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security breaches, or fraud. As datasets become more complex and high-dimensional, traditional detection methods struggle to effectively capture intricate patterns. Advances in deep learning have made AD methods more powerful and adaptable, improving their ability to handle high-dimensional and unstructured data. This survey provides a comprehensive review of over 180 recent studies, focusing on deep learning-based AD techniques. We categorize and analyze these methods into reconstruction-based and prediction-based approaches, highlighting their effectiveness in modeling complex data distributions. Additionally, we explore the integration of traditional and deep learning methods, highlighting how hybrid approaches combine the interpretability of traditional techniques with the flexibility of deep learning to enhance detection accuracy and model transparency. Finally, we identify open issues and propose future research directions to advance the field of AD. This review bridges gaps in existing literature and serves as a valuable resource for researchers and practitioners seeking to enhance AD techniques using deep learning. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Design, Test and Shimming of a 1.5 T Head-Only MRI Superconducting Magnet

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IEEE Transactions on Applied Superconductivity 2025年

作者： Song, Yunxing Cheng, Zhiwen Jiang, Xunhuang Li, Jianglan Li, Liang Wang, Qiuliang Huazhong University of Science and Technology Wuhan National High Magnetic Field Center Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan430074 China Chinese Academy of Sciences Institute of Electrical Engineering Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

As the core component of magnetic resonance imaging (MRI) system, superconducting magnet provides a high-intensity, stable and homogeneous magnetic field background for the system. The strength, stability, and homogeneity of the magnetic field directly affect the imaging quality of MRI. This paper presents the design, test and shimming of a 1.5 T Head-Only MRI superconducting magnet developed by the Wuhan National High Magnetic Field Center (WHMFC). The magnet consists of 7 primary coils and 2 shielding coils and is operated at an operating current of 510 A using liquid helium (LHe) bath cooling. The test has shown that the magnet reached the target field of 1.5 T directly after the first ramp-up without any quench. Under the magnetic field of 1.5 T, the magnetic field decay rate of the magnet is less than 0.02 ppm/hr. After 3 rounds of passive shimming, the magnet achieves a magnetic field inhomogeneity of 2.89 ppm over an imaging sphere within a diameter of 26 cm. The successful development of this magnet provides practical engineering validation for the lightweight technology of head-only MRI superconducting magnets. © 2002-2011 IEEE.

关键词： Superconducting magnets

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Data-Efficient Synchronous Machine Winding Short Circuit Faults Diagnosis Based on Frequency Response Analysis and Active Learning

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IEEE Sensors Journal 2025年第11期25卷 19804-19815页

作者： Chen, Yu Zhao, Zhongyong Liu, Jiangnan Wang, Wei Yao, Chenguo Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Southwest University College of Engineering and Technology Chongqing400716 China Yibin Academy of Southwest University Sichuang64400 China Guangzhou Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangzhou510620 China Chongqing University State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing400044 China

Winding short-circuit (SC) faults are a prevalent issue in synchronous machines, and the accurate and timely identification of these faults is critical for maintaining power system stability. Current methods for inspecting synchronous machine windings often rely on periodic inspections based on human expertise. Numerous studies have explored the application of deep learning (DL) models for detecting synchronous machine winding SC faults. However, these models often exhibit excessive complexity and overlook physical overhead, leading to inefficient utilization of computational power and data resources. To address these limitations, this study proposes a dual-channel deep learning (DL) model integrated with the active learning (AL) query strategy. In this study, winding SC faults are manually simulated on a 5 kVA synchronous machine, and corresponding frequency response analysis (FRA) data are recorded. Subsequently, the proposed method is validated on the test set and benchmarked against previous studies. Experimental results demonstrate that the proposed method significantly reduces the data annotation effort and accelerates model training to the order of seconds (under 5 seconds) while maintaining satisfactory accuracy (≥ 95%). Comparative experimental results further indicate that the proposed model, requiring only 1/20th of the labeled training samples used in previous studies, achieves a substantial reduction of approximately 99.8% in both model parameters and training time. © 2001-2012 IEEE.

关键词： Synchronous machinery

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Polyoxometalated metal-organic framework superstructure for stable water oxidation

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Science (New York, N.Y.) 2025年第6745期388卷 430-436页

作者： Yue, Kaihang Lu, Ruihu Gao, Mingbin Song, Fei Dai, Yao Xia, Chenfeng Mei, Bingbao Dong, Hongliang Qi, Ruijuan Zhang, Daliang Zhang, Jiangwei Wang, Ziyun Huang, Fuqiang Xia, Bao Yu Yan, Ya State Key Laboratory of High Performance Ceramics and Superfine Microstructure Shanghai Institute of Ceramics Chinese Academy of Sciences Shanghai China School of Chemistry and Chemical Engineering State Key Laboratory of Materials Processing and Die & Mould Technology Key Laboratory of Material Chemistry for Energy Conversion and Storage (Ministry of Education) Hubei Key Laboratory of Material Chemistry and Service Failure Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology (HUST) Wuhan China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China School of Chemical Sciences University of Auckland Auckland New Zealand National Engineering Laboratory for Methanol to Olefins Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai Synchrotron Radiation Facility Shanghai China Center for High Pressure Science and Technology Advanced Research Shanghai China Key Laboratory of Polar Materials and Devices (MOE) Department of Electronics East China Normal University Shanghai China Multiscale Porous Materials Center Institute of Advanced Interdisciplinary Studies and School of Chemistry and Chemical Engineering Chongqing University Chongqing China College of Energy Material and Chemistry Inner Mongolia Key Laboratory of Low Carbon Catalysis Inner Mongolia University Hohhot China State Key Lab of Metal Matrix Composites School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai China Center for Next-Generation Energy Materials and School of Chemical Engineering Sungkyunkwan University (SKKU) Seobu-ro Jangan-guGyeonggi-do Suwon South Korea

Stable, nonprecious catalysts are vital for large-scale alkaline water electrolysis. Here, we report a grafted superstructure, MOF@POM, formed by self-assembling a metal-organic framework (MOF) with polyoxometalate (POM). In situ electrochemical transformation converts MOF into active metal (oxy)hydroxides to produce a catalyst with a low overpotential of 178 millivolts at 10 milliamperes per square centimeter in alkaline electrolyte. An anion exchange membrane water electrolyzer incorporating this catalyst achieves 3 amperes per square centimeter at 1.78 volts at 80°C and stable operation at 2 amperes per square centimeter for 5140 hours at room temperature. In situ electrochemical spectroscopy and theoretical studies reveal that the synergistic interactions between metal atoms create a fast electron-transfer channel from catalytic iron and cobalt sites, nickel, and tungsten in the polyoxometalate to the electrode, stabilizing the metal sites and preventing dissolution.

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Enhance the mechanical properties of CoCrFeNi(TiC)0.2 eutectic high-entropy alloy through carbide precipitation and partial recrystallization

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Materials Science and engineering: A 2025年 937卷

作者： Zhang, Jian Chen, Xiaohong Xiang, Wei Zhou, Zhiqiang Wei, Qinqin Xie, Pan Luo, Guoqiang Shen, Qiang State Key Lab of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan430070 China Hubei Longzhong Laboratory Hubei Xiangyang441000 China School of Materials Science and Engineering Wuhan University of Technology Wuhan430070 China Innovation Institute for Ocean Materials Characterization Technology State Key Lab of Tropic Ocean Engineering Materials and Materials Evaluation Hainan University Hainan Province Haikou570228 China Chaozhou Branch of Chemistry and Chemical Engineering Guangdong Laboratory Chaozhou521000 China

The CoCrFeNi based alloys with high outstanding fracture toughness and thermal stability demonstrates significant potential for aerospace structural frameworks. However, the as-cast TiC enhanced CoCrFeNi alloy exhibits a poor yield strength, limiting its practical application as a structural material. This study proposes a thermomechanical treatment strategy to enhance the CoCrFeNi(TiC)0.2 alloy by utilizing carbide precipitates and partial recrystallization. The carbide precipitates, unrecovered dislocations, refined recrystallize grains, and twins simultaneously enhance the alloy through precipitation strengthening, dislocation strengthening, and grain boundary strengthening. After cold rolling and heat treating at 900 °C, the CoCrFeNi(TiC)0.2 alloy exhibits a high yield strength of 760 MPa, an ultimate tensile strength of 1100 MPa, and a uniform elongation of 14 %. The alloy demonstrates a relatively favorable strength-ductility balance, which is higher than that of reported carbide-reinforced CoCrFeNi alloys. The work offers theoretical guidance for preparing high-performance alloys, demonstrating significant potential for practical applications. © 2025 Elsevier B.V.

关键词： Tensile strength

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Influence of n = 1 resonant magnetic perturbation on flow and turbulence towards L-H transition

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Nuclear Fusion 2025年第6期65卷

作者： Zhang, Y. Jiang, M. Guo, Z.B. Diamond, P.H. Guo, W.X. Xu, J.Q. Zhang, N. Wang, Z. Li, Z.J. Zhou, Y. Liang, A.S. Sun, T.F. Ke, R. Gong, S.B. He, X.X. Chen, W. Zhong, W.L. Southwestern Institute of Physics PO Box 432 Chengdu610041 China State Key Laboratory of Nuclear Physics and Technology Fusion Simulation Center School of Physics Peking University Beijing100871 China University of California San Diego San DiegoCA92093 United States International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electric and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

The application of resonant magnetic perturbation (RMP) with toroidal mode number n = 1 on HL-3 tokamak inhibits the L-H transition at specific heating power. Following RMP application, the electron density increases in the outer plasma region (ρ > 0.85, where ρ is the normalized toroidal flux), while the electron/ion temperature decreases. Notably, the equilibrium flow shear in the edge region is substantially reduced. This reduction, combined with enhanced micro-instabilities driven by increased profile gradients, leads to enhanced turbulence levels. Consequently, the diminished flow shear becomes less effective in suppressing turbulence, providing a comprehensive explanation for the inhibited access to H-mode. Through a modified one-dimensional predator-prey model that incorporates the effects of RMP-induced radial magnetic perturbations, we have conducted a quantitative analysis of the turbulence and flow dynamics during the L-H transition process. Our results indicate that as the strength of magnetic perturbation increases, the turbulence intensity increases and edge flow shear decreases, in agreement with experimental observations. Additionally, we found that the L-H transition power threshold increases almost linearly with the square of the radial magnetic perturbation intensity. These results enhance our understanding of RMP-induced changes in edge plasma transport, providing valuable insights for optimizing the operation of future tokamaks and improving the performance of fusion reactors. © 2025 The Author(s). Published by IOP Publishing Ltd on behalf of the IAEA.

关键词： Tokamak devices

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Facet engineering of Metal-Organic Frameworks for Triboelectric Nanogenerators-Based Self-Powered Water Splitting

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advanced Materials 2025年第20期37卷 e2415616页

作者： Kuang, Tairong Deng, Linbing Liu, Mingjin Ding, Yutong Guo, Wei Cai, Zhao Liu, Wenxian Huang, Zhao-Xia Lab State Key Laboratory of Advanced Separation Membrane Materials College of Materials Science and Engineering Zhejiang University of Technology Hangzhou310014 China National Engineering Research Center of Novel Equipment for Polymer Processing Key Laboratory of Polymer Processing Engineering Ministry of Education Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing Department of Mechanical and Automotive Engineering South China University of Technology Guangzhou510641 China Faculty of Materials Science and Chemistry China University of Geoscience Wuhan430074 China

Metal-organic frameworks (MOFs) are highly versatile materials with tunable chemical and structural properties, making them promising for triboelectric nanogenerators (TENGs) and electrocatalysis. However, achieving precise control over MOF coordination structures to optimize facet-dependent properties remains challenging. Here, a facile and scalable dual-solvent synthesis strategy is presented to fabricate dendrite Co-2-methylimidazole MOF (ZIF-67-D), enabling tailored preferred facet and coordination environments. Using density functional theory (DFT) calculations and synchrotron-based X-ray absorption spectroscopy, it is demonstrated that ZIF-67-D, enriched with (112) facets, features a reduced Co coordination number and enhanced electron-donating ability compared to the conventionally (011) facet-dominated rhombic dodecahedron ZIF-67 (ZIF-67-R). This facet engineering boosts TENG charge density by 2.4-fold, OER current density by 9.9-fold (@1.65 V), and HER current density by 1.9-fold (@-0.3 V). The (112)/(011) facet ratio can be also tuned to precisely alter TENG output. Moreover, the optimized ZIF-67-D shows excellent stability, maintaining electrolyzer performance for 72 h and enabling TENG devices even in high humidity. Consequently, ZIF-67-D-based TENG (D-TENG) devices exhibit robust energy generation and power ZIF-67-D||ZIF-67-D electrolyzers for continuous hydrogen (H2) production. These findings introduce a new paradigm for converting mechanical energy into sustainable chemical energy, offering insights into facet engineering for high-performance energy harvesting systems. © 2025 Wiley-VCH GmbH.

关键词： Electrocatalysis

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification from Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital Department of Pathology Wuhan University Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20,000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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