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1,407 results on '"Hu, Lihong"'

1. High quality Fe1+yTe synthesized by chemical vapor deposition with conspicuous vortex flow

Author

Lv, Lu, Hu, Lihong, Dong, Weikang, Duan, Jingyi, Wang, Ping, Li, Peiling, Qu, Fanming, Lu, Li, Ye, Zimeng, Zhao, Junhao, Li, Jiafang, Deng, Fang, Liu, Guangtong, Zhou, Jiadong, and Gao, Yanfeng

Subjects
Physics - Applied Physics, Condensed Matter - Materials Science
Abstract

Two-dimensional (2D) materials provide an ideal platform to explore novel superconducting behavior including Ising superconductivity, topological superconductivity and Majorana bound states in different 2D stoichiometric Ta-, Nb-, and Fe-based crystals. However, tuning the element content in 2D compounds for regulating their superconductivity has not been realized. In this work, we report the synthesis of high quality Fe1+yTe with tunable Fe content by chemical vapor deposition (CVD). The quality and composition of Fe1+yTe are characterized by Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and scanning transmission electron microscopy (STEM). The superconducting behavior of Fe1+yTe crystals with varying Fe contents is observed. The superconducting transition of selected Fe1.13Te sample is sharp ({\Delta}Tc = 1 K), while Fe1.43Te with a high-Fe content shows a relative broad superconducting transition ({\Delta}Tc = 2.6 K) at zero magnetic field. Significantly, the conspicuous vortex flow and a transition from a 3D vortex liquid state to a 2D vortex liquid state is observed in Fe1.43Te sample. Our work highlights the tunability of the superconducting properties of Fe1+yTe and sheds light on the vortex dynamics in Fe-based superconductors, which facilitates us to understand the intrinsic mechanisms of high-temperature superconductivity., Comment: 30 pages, 15 figures, accepted by Advanced Functional Materials

Published
2024
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3. Predictions of photophysical properties of phosphorescent platinum(II) complexes based on ensemble machine learning approach

Author

Wang, Shuai, Yam, ChiYung, Chen, Shuguang, Hu, Lihong, Li, Liping, Hung, Faan-Fung, Fan, Jiaqi, Che, Chi-Ming, and Chen, GuanHua

Subjects
Computer Science - Machine Learning, Physics - Chemical Physics
Abstract

Phosphorescent metal complexes have been under intense investigations as emissive dopants for energy efficient organic light emitting diodes (OLEDs). Among them, cyclometalated Pt(II) complexes are widespread triplet emitters with color-tunable emissions. To render their practical applications as OLED emitters, it is in great need to develop Pt(II) complexes with high radiative decay rate constant ($k_r$) and photoluminescence (PL) quantum yield. Thus, an efficient and accurate prediction tool is highly desirable. Here, we develop a general protocol for accurate predictions of emission wavelength, radiative decay rate constant, and PL quantum yield for phosphorescent Pt(II) emitters based on the combination of first-principles quantum mechanical method, machine learning (ML) and experimental calibration. A new dataset concerning phosphorescent Pt(II) emitters is constructed, with more than two hundred samples collected from the literature. Features containing pertinent electronic properties of the complexes are chosen. Our results demonstrate that ensemble learning models combined with stacking-based approaches exhibit the best performance, where the values of squared correlation coefficients ($R^2$), mean absolute error (MAE), and root mean square error (RMSE) are 0.96, 7.21 nm and 13.00 nm for emission wavelength prediction, and 0.81, 0.11 and 0.15 for PL quantum yield prediction. For radiative decay rate constant ($k_r$), the obtained value of $R^2$ is 0.67 while MAE and RMSE are 0.21 and 0.25 (both in log scale), respectively. The accuracy of the protocol is further confirmed using 24 recently reported Pt(II) complexes, which demonstrates its reliability for a broad palette of Pt(II) emitters.We expect this protocol will become a valuable tool, accelerating the rational design of novel OLED materials with desired properties.

Published
2023

33. Discovery of a Novel Orally Bioavailable FLT3-PROTAC Degrader for Efficient Treatment of Acute Myeloid Leukemia and Overcoming Resistance of FLT3 Inhibitors

Author

Wang, Junwei, primary, Rong, Quanjin, additional, Ye, Lei, additional, Fang, Bingqian, additional, Zhao, Yifan, additional, Sun, Yu, additional, Zhou, Haikun, additional, Wang, Dan, additional, He, Jinting, additional, Cui, Zhenzhen, additional, Zhang, Qijian, additional, Kang, Di, additional, and Hu, Lihong, additional

Published
2024
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35. High Quality Fe1+yTe Synthesized by Chemical Vapor Deposition with Conspicuous Vortex Flow

Author

Lv, Lu, primary, Hu, Lihong, additional, Dong, Weikang, additional, Duan, Jingyi, additional, Wang, Ping, additional, Li, Peiling, additional, Qu, Fanming, additional, Lu, Li, additional, Ye, Zimeng, additional, Zhao, Junhao, additional, Li, Jiafang, additional, Deng, Fang, additional, Liu, Guangtong, additional, Zhou, Jiadong, additional, and Gao, Yanfeng, additional

Published
2024
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47. Machine Learning Corrections for DFT Noncovalent Interactions

Author

Li, Wenze, Liu, Jia, Li, Lin, Hu, LiHong, Su, Zhong-Min, Chen, GuanHua, Hull, Robert, Series Editor, Jagadish, Chennupati, Series Editor, Kawazoe, Yoshiyuki, Series Editor, Kruzic, Jamie, Series Editor, Osgood, Richard M., Series Editor, Parisi, Jürgen, Series Editor, Pohl, Udo W., Series Editor, Seong, Tae-Yeon, Series Editor, Uchida, Shin-ichi, Series Editor, Wang, Zhiming M., Series Editor, Shankar, Sadasivan, editor, Muller, Richard, editor, Dunning, Thom, editor, and Chen, Guan Hua, editor

Published
2021
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