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Low cycle fatigue characteristics and life prediction of 316LN austenitic stainless steel

Progress in Natural science:Materials International 2024年第6期34卷 1194-1206页

作者： Kailun Ding Zhengxin Tang Xikou He Xitao Wang Jinshan He Collaborative Innovation Center of Steel Technology University of Science and Technology Beijing Research Institute of Special Steels Central Iron and Steel Research Institute Co.Ltd Shandong Provincial Key Laboratory for High Strength Lightweight Metallic Materials Advanced Materials Institute Qilu University of Technology (Shandong Academy of Science)

An investigation was conducted on the low cycle fatigue behavior, fatigue life, and fatigue fracture mechanism of forged 316LN stainless steel at 330°C, under varying strain amplitudes. The results indicate that the cyclic softening degree of 316LN austenitic stainless steel increases proportionally with the strain amplitude. This increase can be attributed to variations in the arrangement and development process of the dislocation ***, it was observed that both back stress and friction stress augment as the total strain amplitude increases. This phenomenon is primarily caused by enhanced interactions between dislocations and precipitated phases, as well as dislocations themselves. The hysteresis loop area exhibits an increase in direct correlation with the total strain amplitude. Moreover, a smaller strain amplitude corresponds to reduced plastic strain energy and increased fatigue life. In engineering applications, the simplicity and universality of a model, along with easily obtainable parameters, are crucial considerations. To evaluate the fatigue life of 316LN austenitic stainless steel,four models were employed: Coffin-Manson fatigue life model, energy life model, three-parameter power model,and three-parameter power life correction model. The predicted data from the three-parameter power life correction model fell within the acceptable error range of 1.2, demonstrating a strong alignment with the experimental values. Consequently, it can be concluded that the three-parameter power life correction model exhibits remarkable accuracy in predicting low cycle fatigue(LCF) behavior, thereby facilitating the safety assessment of component lifespan. The fatigue fracture surface exhibits distinct features of fatigue striations, with a noticeable decrease in the interval between these striations as the total strain amplitude increases.

关键词： 316LN austenitic stainless steel Low cycle fatigue Fatigue life Cyclic hardening and softening Fatigue striation

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Machine learning-driven insights into biaxial strain-induced anomalous thermal conductivity enhancement of boron arsenide

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science China(Technological sciences) 2025年第5期68卷 1-14页

作者： Yikun LIU Yurong HE Tianqi TANG Jiaqi ZHU School of Energy Science and Engineering Harbin Institute of TechnologyHarbin 150001China Zhengzhou Research Institute Harbin Institute of TechnologyZhengzhou 450000China Heilongjiang Key Laboratory of New Energy Storage Materials and Processes Harbin 150001China National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin 150080China

Boron arsenide(BAs)is a promising integrated circuit thermal management material with ultra-high thermal conductivity(κ)and exceptional semiconductor *** practical devices,mechanical stress and strain caused by thermal expansion inevitably affect the thermodynamic properties of heat sink ***,investigation of the effects of stress and strain on theκof BAs is *** assessment and regulation of theκand phonon heat transport of BAs requires consideration of both three-phonon(3ph)and four-phonon(4ph)***,anharmonicity computation using density functional theory is computationally expensive,particularly for the fourth-order interatomic force *** work uses a machine learning-driven moment tensor potential method to evaluate higher-order anharmonicity and extends it to include fourth-order forces,accelerating the calculations *** show that theκof BAs calculated using the moment tensor potential method when considering both 3ph and 4ph processes agrees well with experimental *** contrast,considering the 3ph interaction alone causesκto be *** machine learning approach reduces computational costs by approximately 94%while maintaining comparable accuracy to density functional *** solving the phonon Boltzmann transport equation,the thermal transport properties of BAs under biaxial tensile strains ranging up to 7%are *** shows an anomalousκenhancement when including both 3ph and 4ph interactions,with a maximum enhancement of 15%at a small strain of 1%,primarily because of the weakening of the 3ph *** 2%strain,both the 3ph and 4ph scattering rates increase significantly,causing shorter phonon lifetimes and a monotonic reduction inκ.Additionally,theκtemperature dependence under strain is explored,highlighting temperature’s role in modulating phonon *** study provides machine learning-driven insights into the BAs thermal response under bi

关键词： boron arsenide machine learning potential biaxial strain thermal conductivity enhancement phonon transport

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Numerical study on the dynamic fracture of explosively driven cylindrical shells

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Defence technology（防务技术） 2023年第9期27卷 154-168页

作者： Zhi-yong Yin Xiao-wei Chen State Key Laboratory of Explosion Science and Technology Beijing Institute of TechnologyBeijing100081China School of Mechatronical Engineering Beijing Institute of TechnologyBeijing100081China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of TechnologyBeijing100081China

Research on the expansion and fracture of explosively driven metal shells has been a key issue in weapon development and structural *** is important to study and predict the failure mode,fracture mechanism,and fragment distribution characteristics of explosively driven metal *** this study,we used the finite element-smoothed particle hydrodynamics(FE-SPH)adaptive method and the fluid-structure interaction method to perform a three-dimensional numerical simulation of the expansion and fracture of a metal cylindrical *** method combined the advantages of the FEM and SPH,avoiding system mass loss,energy loss,and element distortion;in addition,the proposed method had a good simulation effect on the interaction between detonation waves and the cylindrical *** simulated detonation wave propagation,shell damage morphology,and fragment velocity distribution were in good agreement with theoretical and experimental *** divided the fragments into three regions based on their shape *** analyzed the failure mode and formation process of fragments in different *** numerical results reproduced the phenomenon in which cracks initiated from the inner surface and extended to the outer surface of the cylindrical shell along the 45°or 135°shear *** addition,fragments composed of elements are identified,and the mass and characteristic lengths of typical fragments at a stable time are ***,the mass and size distribution characteristics of the fragments were explored,and the variation in the fitting results of the classical distribution function under different explosion pressures was ***,based on mathematical derivation,the distribution formula of fragment velocity was *** improved formula provided higher accuracy and could be used to analyze any metal cylindrical shells with different length-to-diameter ratios.

关键词： Metal cylindrical shell Shear failure Fragment distribution Numerical simulation Fragment velocity

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Discriminating circular polarization of light:Left or right?

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Light(science & Applications) 2025年第1期14卷 20-22页

作者： Wanhee Lee Changsoon Cho Department of Material Science and Engineering Pohang University of Science and Technology(POSTECH)PohangRepublic of Korea Institute for Convergence Research and Education in Advanced Technology Yonsei UniversitySeoulRepublic of Korea

Achiral dielectric nanostructures provide an efficient method for discriminating left-and right-circularly polarized photons,leveraging the photothermoelectric effect.

关键词： effect circular dielectric

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A rapid and effective method for alloy materials design via sample data transfer machine learning

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npj Computational Materials 2023年第1期9卷 2083-2094页

作者： Lei Jiang Zhihao Zhang Hao Hu Xingqun He Huadong Fu Jianxin Xie Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing100083 BeijingChina Beijing Laboratory of Metallic Materials and Processing for Modern Transportation Institute for Advanced Materials and TechnologyUniversity of Science and Technology Beijing100083 BeijingChina Key Laboratory for Advanced Materials Processing(MOE) University of Science and Technology Beijing100083 BeijingChina

One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing ***,a rapid and effective method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data.A new type of aluminum alloy(E2 alloy)with ultra strength and high toughness previously developed by the authors is used as an *** optimal three-stage solution-aging treatment process(T66R)was efficiently designed transferring 1053 pieces of process-property relationship data of existing AA7xxx commercial aluminum *** realizes the substantial improvement of strength and plasticity of E2 alloy simultaneously,which is of great significance for lightweight of high-end ***,the microstructure analysis clarifies the mechanism of alloy performance *** study shows that transferring the existing alloy data is an effective method to design new alloys.

关键词： alloy rapid toughness

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Object Recognition from Scientific Document Based on Compartment and Text Blocks Refinement Framework

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SN Computer science 2024年第7期5卷 1-23页

作者： Li, Jinghong Gu, Wen Ota, Koichi Hasegawa, Shinobu Division of Advanced Science and Technology Japan Advanced Institute of Science and Technology Asahidai Ishikawa Nomi 9231292 Japan Center for Innovative Distance Education and Research Japan Advanced Institute of Science and Technology Asahidai Ishikawa Nomi 9231292 Japan

With the rapid development of the internet in the past decade, it has become increasingly important to extract valuable information from vast resources efficiently, which is crucial for establishing a comprehensive digital ecosystem, particularly in the context of research surveys and comprehension. The foundation of these tasks focuses on accurate extraction and deep mining of data from scientific documents, which are essential for building a robust data infrastructure. However, parsing raw data or extracting data from complex scientific documents have been ongoing challenges. Current data extraction methods for scientific documents typically use rule-based (RB) or machine learning (ML) approaches. However, using rule-based methods can incur high coding costs for articles with intricate typesetting. Conversely, relying solely on machine learning methods necessitates annotation work for complex content types within the scientific document, which can be costly. Additionally, few studies have thoroughly defined and explored the hierarchical layout within scientific documents. The lack of a comprehensive definition of the internal structure and elements of the documents indirectly impacts the accuracy of text classification and object recognition tasks. From the perspective of analyzing the standard layout and typesetting used in the specified publication, we propose a new document layout analysis framework called Compartment and Text Blocks Refinement (CTBR). Firstly, we define scientific documents into hierarchical divisions: base domain, compartment, and text blocks. Next, we conduct an in-depth exploration and classification of the meanings of text blocks. Finally, we utilize the results of text block classification to implement object recognition within scientific documents based on rule-based compartment segmentation. For the experiment, we used the well-known ACL format proceeding articles as experimental data for the validation experiment. The experiment shows

关键词： Compartment segmentation Machine learning Object recognition Rule-based Scientific documents Text block refinement

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Enhancing microwave absorption performance of MoS_(2)by synergistic effect of Fe doping

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Journal of advanced Ceramics 2025年第3期14卷 79-90页

作者： Yuefeng Yan Guangyu Qin Kaili Zhang Boshi Gao Guansheng Ma Xiaoxiao Huang Yu Zhou National Key Laboratory of Precision Welding&Joining of Materials and Structures Harbin Institute of Technology Harbin 150001China School of Materials Science and Engineering Harbin Institute of Technology Harbin 150001China MIIT Key Laboratory of Advanced Structural-Functional Integration Materials&Green Manufacturing TechnologyHarbin Institute of TechnologyHarbin 150001China

Owing to its unique two-dimensional structure and tunable electronic properties,MoS_(2)has emerged as a promising electromagnetic wave(EMW)-absorbing material that can be extensively combined with various other substances to construct effective EMW ***,research on cation substitution doping in MoS_(2)remains relatively limited,which impedes the design and development of high-performance MoS_(2)-based EMW absorbing *** this study,MoS_(2)was synthesized with various concentrations of doped Fe via a facile hydrothermal *** thoroughly investigated the effects of Fe doping,which induced lattice distortion and collapse,triggered a 1T‒2H phase transition,and led to the formation and evolution of second *** modulation of phase transitions,coupled with doping-induced lattice defects that enhance polarization and interfacial polarization from second phases,enabled the Fe-doped MoS_(2)samples to exhibit remarkable EMW absorption ***,the sample FM3 achieved an effective absorption bandwidth(EAB)of 5.1 GHz and a minimum reflection loss(RLmin)of−60.6 dB,underscoring the critical role of Fe doping in increasing the EMW absorption *** research provides valuable pathways and unique insights for the advancement of transition metal dichalcogenides(TMDs)as high-performance EMW-absorbing materials.

关键词： microwave absorption Fe-doped MoS_(2) dielectric property impedance characteristic second phase

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Composition and state prediction of lithium-ion cathode via convolutional neural network trained on scanning electron microscopy images

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npj Computational Materials 2024年第1期10卷 2337-2345页

作者： Jimin Oh Jiwon Yeom Benediktus Madika Kwang Man Kim Chi Hao Liow Joshua C.Agar Seungbum Hong Department of Materials Science and Engineering Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Korea ICT Creative Laboratory Electronics and Telecommunications Research Institute(ETRI)218 Gajeong-roYuseong-guDaejeon 34129Korea Department of Mechanical Engineering and Mechanics Drexel University3141 Chestnut StreetPhiladelphiaPA 19104USA KAIST Institute for NanoCentury(KINC) Korea Advanced Institute of Science and Technology(KAIST)Daejeon 34141Korea Department of Smart Mobility Engineering Advanced Institute of Science and TechnologyKyungpook National UniversityDaegu 41566Korea

High-throughput materials research is strongly required to accelerate the development of safe and high energy-density lithium-ion battery(LIB)applicable to electric vehicle and energy storage *** artificial intelligence,including machine learning with neural networks such as Boltzmann neural networks and convolutional neural networks(CNN),is a powerful tool to explore next-generation electrode materials and functional additives.

关键词： neural networks artificial

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Unveiling the chemistry behind the electrolytic production of hydrogen peroxide by oxygenated carbon

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Journal of Energy Chemistry 2024年第9期96卷 49-58页

作者： Pan Xiang Kunshang Yang Qihao Yang Yang Gao Wenwen Xu Zhiyi Lu Liang Chen Zhiming Wang Ziqi Tian Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610054SichuanChina Key Laboratory of Advanced Fuel Cells and Electrolyzers Technology of Zhejiang Province Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201ZhejiangChina Institute for Advanced Study Chengdu UniversityChengdu 610106SichuanChina

Oxygenated carbon materials exhibit outstanding electrocatalytic performance in the production of hydrogen peroxide(H2O2)through a two-electron oxygen reduction *** nature of the active functional group and underlying reaction mechanism,however,remain ***,a comprehensive workflow was established to identify the active sites from the numerous possible *** common hydroxyl group at the notched edge demonstrates a key role in the two-electron *** local chemical environment weakens the binding of OOH intermediate to substrate while enhancing interaction with solution,thereby promoting the H_(2)O_(2)*** increasing pH,the intramolecular hydrogen bond between OOH intermediate and hydroxyl decreases,facilitating OOH ***,the rise in selectivity with increasing potential stems from the suppression of the four-electron *** active site was further validated through *** by theoretical understanding,optimal performance was achieved with high selectivity(>95%)and current density(2.06 mA/cm^(2))in experiment.

关键词： First-principles calculation Oxygen reductionreaction Hydrogen peroxide production Fixed-potential method Oxygenated carbon materials

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Corrosion of Duplex Stainless Steel Manufactured by Laser Powder Bed Fusion: A Critical Review

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Acta Metallurgica Sinica(English Letters) 2024年第4期37卷 587-606页

作者： Yiqi Zhou Decheng Kong Ruixue Li Xing He Chaofang Dong Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing100083China Shanghai Key Laboratory of Advanced High-Temperature Materials and Precision Forming School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China

Laser powder bed fusion (LPBF) is a commonly used additive manufacturing (AM) method for efficiently producing intricate geometric components. This investigation examines factors such as pores, cellular structure, grain size, and inclusions from the manufacturing process that contribute to the corrosion resistance of LPBF DSS. Furthermore, the as-built LPBF duplex stainless steel (DSS) is primarily ferrite due to the rapid cooling process. Therefore, the transformation of ferrite to austenite after various heat treatments in LPBF DSS and its corresponding corrosion resistance are presented. Additionally, a new mixed powder method is proposed to increase the austenite content in the as-built LPBF DSS. This review also focuses on the passivation capability and pitting corrosion performance in LPBF and conventional DSS. This article summarizes the variations in microstructure between as-built and heat-treated LPBF DSS, with their impacts on corrosion resistance, offering insights for manufacturing highly corrosion-resistant LPBF DSS.

关键词： Laser powder bed fusion Duplex stainless steel Microstructure Heat treatment Passivation Pitting corrosion

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