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Purity-dependent Lorenz number,electron hydrodynamics and electron-phonon coupling in WTe_(2)
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作者 Wei Xie Feng Yang +3 位作者 liangcai xu Xiaokang Li Zengwei Zhu Kamran Behnia 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2024年第8期153-158,共6页
We present a study of electrical and thermal transport in Weyl semimetal WTe_(2)down to 0.3 K.The Wiedemann-Franz law holds below 2 K and a downward deviation starts above.The deviation is more pronounced in cleaner s... We present a study of electrical and thermal transport in Weyl semimetal WTe_(2)down to 0.3 K.The Wiedemann-Franz law holds below 2 K and a downward deviation starts above.The deviation is more pronounced in cleaner samples,as expected in the hydrodynamic picture of electronic transport,where a fraction of electron-electron collisions conserve momentum.Phonons are the dominant heat carriers and their mean-free-path does not display a Knudsen minimum.This is presumably a consequence of weak anharmonicity,as indicated by the temperature dependence of the specific heat.Frequent momentum exchange between phonons and electrons leads to quantum oscillations of the phononic thermal conductivity.Bloch-Grüneisen picture of electron-phonon scattering breaks down at low temperature when Umklapp ph-ph collisions cease to be a sink for electronic flow of momentum.Comparison with semi-metallic Sb shows that normal ph-ph collisions are amplified by anharmonicity.In both semimetals,at cryogenic temperature,e-ph collisions degrade the phononic flow of energy but not the electronic flow of momentum. 展开更多
关键词 thermal conductivity Wiedemann-Franz law electron hydrodynamics electron-phonon coupling
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Strong Pauli paramagnetic effect in the upper critical field of KCa2Fe4As4F2 被引量:1
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作者 Teng Wang Chi Zhang +11 位作者 liangcai xu JinHua Wang Shan Jiang ZengWei Zhu ZhaoSheng Wang JiaNan Chu JiaXin Feng LingLing Wang Wei Li Tao Hu XiaoSong Liu Gang Mu 《Science China(Physics,Mechanics & Astronomy)》 SCIE EI CAS CSCD 2020年第2期124-129,共6页
Recently, 12442 system of Fe-based superconductors has attracted considerable attention owing to its unique double-Fe As-layer structure. A steep increase in the in-plane upper critical field with cooling has been obs... Recently, 12442 system of Fe-based superconductors has attracted considerable attention owing to its unique double-Fe As-layer structure. A steep increase in the in-plane upper critical field with cooling has been observed near the superconducting transition temperature, Tc, in KCa2Fe4As4F2 single crystals. Herein, we report a high-field investigation on upper critical field of this material over a wide temperature range, and both out-of-plane(H∥c, Hc2c) and in-plane(H∥ab, Hc2ab ) directions have been measured.A sublinear temperature-dependent behavior is observed for the out-of-plane Hc2c , whereas strong convex curvature with cooling is observed for the in-plane Hc2ab . Such behaviors could not be described by the conventional Werthamer-Helfand-Hohenberg(WHH) model. The data analysis based on the WHH model by considering the spin aspects reveals a large Maki parameter α=9,indicating that the in-plane upper critical field is affected by a very strong Pauli paramagnetic effect. 展开更多
关键词 12442 upper critical field Pauli paramagnetic effect
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Prognostic for fuel cell based on particle filter and recurrent neural network fusion structure 被引量:3
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作者 Renyou Xie Rui Ma +3 位作者 Sicheng Pu liangcai xu Dongdong Zhao Yigeng Huangfu 《Energy and AI》 2020年第2期4-14,共11页
Fuel cells are considered as one of the most promising candidates for future power source due to its high energy density and environmentally friendly properties,whereas the short lifespan blocks its large-scale commer... Fuel cells are considered as one of the most promising candidates for future power source due to its high energy density and environmentally friendly properties,whereas the short lifespan blocks its large-scale commercializa-tion.In order to enhance the reliability and durability of proton exchange membrane fuel cell,a fusion prog-nostic approach based on particle filter(model-based)and long-short term memory recurrent neural network(data-driven)is proposed in this paper.Both the remaining useful life estimation and the short-term degradation prediction can be achieved based on the prognostic method.For remaining useful life estimation,the particle filter method is used to identify the model parameters in the training phase and the long-short term memory recurrent neural network is used to update the parameters in the prediction phase.As for short-term degradation prediction,the particle filter and long-short term memory recurrent neural network are firstly trained individually in the training phase and then be fused to make predictions in the prediction phase.The proposed fusion structure is validated by the fuel cell experimental tests data,and results indicate that better prognostic performance can be obtained compared with the individual model-based or data-driven method. 展开更多
关键词 Fuel cell PROGNOSTIC Remaining useful life Degradation prediction Machine learning
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