C
Chao Zhang
Researcher at Beihang University
Publications - 4347
Citations - 118320
Chao Zhang is an academic researcher from Beihang University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 3119 publications receiving 84711 citations. Previous affiliations of Chao Zhang include West Virginia University & University of Oklahoma.
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Seed-mediated synthesis of gold tetrahedra in high purity and with tunable, well-controlled sizes.
Yiqun Zheng,Wenying Liu,Tian Lv,Ming Luo,Hefei Hu,Ping Lu,Sang-Il Choi,Chao Zhang,Jing Tao,Yimei Zhu,Zhi-Yuan Li,Younan Xia,Younan Xia +12 more
TL;DR: The current strategy not only enables the synthesis of Au tetrahedra with tunable and controlled sizes but also provides a facile and versatile approach to reducing the symmetry of nanocrystals made of a face-centered cubic lattice.
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Broadband photocarrier dynamics and nonlinear absorption of PLD-grown WTe2 semimetal films
Wenbin Gao,Lei Huang,Jinlong Xu,Yequan Chen,Chunhui Zhu,Zhonghui Nie,Yao Li,Xuefeng Wang,Zhenda Xie,Shining Zhu,Jun Xu,Xiangang Wan,Chao Zhang,Yongbing Xu,Yi Shi,Fengqiu Wang +15 more
TL;DR: In this paper, a centimeter-scale, highly crystalline WTe2 ultrathin film (∼35 nm) was grown by a pulsed laser deposition technique.
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Development of Specific, Irreversible Inhibitors for a Receptor Tyrosine Kinase EphB3.
Alvin Kung,Ying-Chu Chen,Marianne Schimpl,Feng Ni,Jianfa Zhu,Maurice J. Turner,Henrik Molina,Ross Overman,Chao Zhang +8 more
TL;DR: The development of the first potent and specific inhibitors of a single Eph isoform, EphB3, are reported, demonstrating that the autophosphorylation of Eph B3 within the juxtamembrane region occurs in trans using a specific inhibitor.
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Lignocellulosic ethanol production by starch-base industrial yeast under PEG detoxification.
TL;DR: The PEG chemical exo-protection makes the industrial S. cerevisiae yeast directly applicable for the production of cellulosic ethanol with substantially improved productivity and yield, without of the need to use genetically modified microorganisms.
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Supporting Pattern-Preserving Anonymization for Time-Series Data
TL;DR: A novel anonymization model called (k, P)-anonymity for pattern-rich time series is proposed, which publishes both the attribute values and the patterns of time series in separate data forms and can prevent linkage attacks on the published data while effectively support a wide variety of queries on the anonymized data.