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A new strategy for fabrication of unique heterostructured titanium laminates and visually tracking their synchronous evolution of strain partitions versus microstructure 被引量:2
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作者 Hao Ding Xiping Cui +7 位作者 Zhiqi Wang Tao Zhao Yuchen Wang Yuanyuan Zhang Hongtao Chen Lujun Huang Lin Geng Junfeng Chen 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2022年第12期70-81,共12页
Heterostructured(HS)material with extraordinary mechanical properties has been regarded as one of the most promising structural materials.Here,we reported a new strategy for preparing heterostructured pure titanium la... Heterostructured(HS)material with extraordinary mechanical properties has been regarded as one of the most promising structural materials.Here,we reported a new strategy for preparing heterostructured pure titanium laminates that possess a good combination of strength and ductility by combining gradient structure(GS)and heterogeneous lamella structure(HLS).The deformation characteristic versus microstructure evolution of GS/HLS titanium laminates,namely the strain partitions between different-sized grains(480–25μm)was visualized using a scanning electron microscope(SEM)equipped with electron backscattered diffraction(EBSD)mode combined with the digital image correlation(SEM-DIC)with an ultrahigh spatial resolution for the first time.As a result,the hetero-deformation of unique GS/HLS structure by the characteristic of strain partitions could be accurately captured.While the hetero-deformation could result in the hetero-deformation induced(HDI)stress strengthening and HDI hardening,which were regarded as the key reason that the resulting GS/HLS Ti laminates showed a superior combination of strength and ductility.This could promote a more in-depth understanding of the strengtheningtoughening mechanism of heterostructured material. 展开更多
关键词 Gradient structure Heterogeneous lamella structure Digital image correlation Strain partition
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Electronic structure and optical properties of non-metallic modified graphene:a first-principles study 被引量:1
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作者 Jing-tao Huang Yong Liu +3 位作者 Zhong-hong Lai Jin Hu Fei Zhou Jing-chuan Zhu 《Communications in Theoretical Physics》 SCIE CAS CSCD 2022年第3期70-76,共7页
In this paper,the electronic structure and stability of the intrinsic,B-,N-,Si-,S-doped graphene are studied based on first-principles calculations of density functional theory.Firstly,the intrinsic,B-,N-,Si-,S-doped ... In this paper,the electronic structure and stability of the intrinsic,B-,N-,Si-,S-doped graphene are studied based on first-principles calculations of density functional theory.Firstly,the intrinsic,B-,N-,Si-,S-doped graphene structures are optimized,and then the forming energy,band structure,density of states,differential charge density are analyzed and calculated.The results show that Band Si-doped systems are p-type doping,while N is n-type doping.By comparing the forming energy,it is found that N atoms are more easily doped in graphene.In addition,for B-,N-,Si-doped systems,it is found that the doping atoms will open the band gap,leading to a great change in the band structure of the doping system.Finally,we systematically study the optical properties of the different configurations.By comparison,it is found that the order of light sensitivity in the visible region is as follows:S-doped>Si-doped>pure>B-doped>N-doped.Our results will provide theoretical guidance for the stability and electronic structure of non-metallic doped graphene. 展开更多
关键词 GRAPHENE Non-metallic Electronic structure Optical properties Density functional theory
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Relating microstructure to magnetocaloric properties in RE_(36)Tb_(20)Co_(20)Al_(24)(RE=Gd,Dy or Ho)high-entropy metallic-glass microwires designed by binary eutectic clusters method 被引量:4
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作者 Hangboce Yin Jun-Qiang Wang +5 位作者 Yongjiang Huang Hongxian Shen Shu Guo Hongbo Fan Juntao Huo Jianfei Sun 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2023年第18期167-176,共10页
The new high-entropy metallic-glasses(HE-MGs)are designed by using Dy and Ho to replace Gd in Gd_(36)Tb_(20)Co_(20)Al_(24)alloy based on the binary eutectic clusters method.Compared with the equiatomic Gd 25 Tb 25 Co ... The new high-entropy metallic-glasses(HE-MGs)are designed by using Dy and Ho to replace Gd in Gd_(36)Tb_(20)Co_(20)Al_(24)alloy based on the binary eutectic clusters method.Compared with the equiatomic Gd 25 Tb 25 Co 25 Al 25 HE-MG,the non-equiatomic RE_(36)Tb_(20)Co_(20)Al_(24)(RE=Gd,Dy,or Ho)alloys show bet-ter glass-forming ability,which is attributed to the deep binary eutectic compositions used for alloy de-sign.All RE_(36)Tb_(20)Co_(20)Al_(24)alloys undergo second-order magnetic transition.An extreme peak value of magnetic entropy change is obtained as 10.3 J kg^(-1) K-1(5 T)for the Ho_(36)Tb_(20)Co_(20)Al_(24)alloy.In-situ high-energy synchrotron X-ray diffraction was conducted to observe the microstructural difference among non-equiatomic samples at cryogenic temperatures.The results indicate that Gd_(36)Tb_(20)Co_(20)Al_(24)alloy possesses a relatively large average value of the dispersion of local clusters at a low-temperature range.This,com-bined with the critical exponentβclose to 0.5 of Gd_(36)Tb_(20)Co_(20)Al_(24)alloy,leads to its widest working temperature span among non-equiatomic samples.This work successfully establishes the connection be-tween microstructure and magnetocaloric properties of HE-MGs,which is beneficial for understanding the physical mechanism of the magnetocaloric behaviors of HE-MGs. 展开更多
关键词 High-entropy metallic-glass Magnetocaloric effect High energy synchrotron X-ray diffraction Cryogenic temperature
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Machine learning investigation of the effects of elemental doping on the mechanical properties of Fe-Cr-Ni-Al high-entropy alloys
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作者 Jingteng XUE Jingtao HUANG +3 位作者 Zhonghong LAI Nan QU Yong LIU Jingchuan ZHU 《Science China(Technological Sciences)》 2025年第7期66-75,共10页
This study investigates how doping various elements from the periodic table affects the mechanical properties of FeCr-Ni-Al high-entropy alloys using machine learning techniques.Dimensionality reduction was applied to... This study investigates how doping various elements from the periodic table affects the mechanical properties of FeCr-Ni-Al high-entropy alloys using machine learning techniques.Dimensionality reduction was applied to identify 26 representative elements,which were subsequently used to establish doping structure models.The elastic constants and intrinsic mechanical properties of these alloy configurations were evaluated using first-principles calculations.Chemical compositions were converted into physical features,serving as input variables for multiple machine learning algorithms to predict the properties of alloys with common dopants.Furthermore,elements were clustered according to their influence on alloy properties.The results reveal significant variability in the effects of different elements.Notably,Young's modulus and toughness often exhibited opposing trends.For instance,Zn and Co enhanced toughness,whereas Li and Pb led to increased brittleness.Meanwhile,elements such as Pt,Ti,and Mn achieved a favorable balance between stiffness and ductility.Comparisons between experimental data and predicted results confirmed the accuracy of our approach.This method provides theoretical guidance for the compositional design of high-entropy alloys and offers insights into accelerating first-principles calculations. 展开更多
关键词 high-entropy alloy first-principles calculation machine learning DOPING Fe-Cr-Ni-Al system
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Enhancing the magnetocaloric response of high-entropy metallic-glass by microstructural control
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作者 Hangboce Yin Jia Yan Law +5 位作者 Yongjiang Huang Hongxian Shen Sida Jiang Shu Guo Victorino Franco Jianfei Sun 《Science China Materials》 SCIE EI CAS CSCD 2022年第4期1134-1142,共9页
Non-equiatomic high-entropy alloys(HEAs),the second-generation multi-phase HEAs,have been recently reported with outstanding properties that surpass the typical limits of conventional alloys and/or the first-generatio... Non-equiatomic high-entropy alloys(HEAs),the second-generation multi-phase HEAs,have been recently reported with outstanding properties that surpass the typical limits of conventional alloys and/or the first-generation equiatomic single-phase HEAs.For magnetocaloric HEAs,non-equiatomic(Gd_(36)Tb_(20)Co_(20)Al_(24))100−xFex microwires,with Curie temperatures up to 108 K,overcome the typical low temperature limit of rare-earth-containing HEAs(which typically concentrate lower than around 60 K).For alloys with x=2 and 3,they possess some nanocrystals,though very minor,which offers a widening in the Curie temperature distribution.In this work,we further optimize the magnetocaloric responses of x=3 microwires by microstructural control using the current annealing technique.With this processing method,the precipitation of nanocrystals within the amorphous matrix leads to a phase compositional difference in the microwires.The multi-phase character leads to challenges in rescaling the magnetocaloric curves,which is overcome by using two reference temperatures during the scaling procedure.The phase composition difference increases with increasing current density,whereby within a certain range,the working temperature span broadens and simultaneously offers relative cooling power values that are at least 2-fold larger than many reported conventional magnetocaloric alloys,both single amorphous phase or multi-phase character(amorphous and nanocrystalline).Among the amorphous rare-earth-containing HEAs,our work increases the working temperature beyond the typical<60 K limit while maintaining a comparable magnetocaloric effect.This demonstrates that microstructural control is a feasible way,in addition to appropriate compositional design selection,to optimize the magnetocaloric effect of HEAs. 展开更多
关键词 covalent organic framework low-temperature photothermal therapy gambogic acid heat-shock protein 90 lung metastasis
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单原子在铝合金中的扩散迁移行为:可解释机器学习加速第一原理计算方法 被引量:2
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作者 黄敬涛 薛景腾 +7 位作者 李明伟 程源 来忠红 胡津 周飞 曲囡 刘勇 朱景川 《Science China Materials》 SCIE EI CAS CSCD 2024年第4期1140-1149,共10页
本文采用机器学习加速第一原理计算的方法,研究了铝基体中单原子的扩散迁移行为.建立铝基体中三十多种单原子扩散迁移行为的小样本数据集,以原子半径、离子半径和第一电离能等固有参数作为输入特征值,合金原子与空位之间的相互作用能以... 本文采用机器学习加速第一原理计算的方法,研究了铝基体中单原子的扩散迁移行为.建立铝基体中三十多种单原子扩散迁移行为的小样本数据集,以原子半径、离子半径和第一电离能等固有参数作为输入特征值,合金原子与空位之间的相互作用能以及合金原子在铝基体中的扩散势垒作为输出参数.通过相关性分析初步确定描述符与预测目标之间的关系,并利用递归特征消除法确定不同目标的输入特征和描述符数量.通过交叉验证证明所选模型的先进性,并进行微调以优化其性能.为了验证其效率和准确性,CatBoost模型经过了传统算法的严格测试.利用训练有素的模型预测周期表中其他单原子在铝基体中的扩散迁移行为.机器学习加速第一原理计算的结果可为进一步开发新型铝合金提供理论依据. 展开更多
关键词 机器学习 扩散迁移 交叉验证 输出参数 单原子 第一原理计算 原子半径 小样本数据
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