Z
Zhen Li
Researcher at Wuhan University
Publications - 3347
Citations - 95191
Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.
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Direct optical fiber monitor on stress evolution of the sulfur-based cathodes for lithium-sulfur batteries
Ziyun Miao,Yanpeng Li,Xia Xiao,Qizhen Sun,Bin He,Xue Chen,Yaqi Liao,Yi Zhang,Lixia Yuan,Zhi-Ming Yan,Zhen Li,Yunhui Huang +11 more
TL;DR: In this paper , Li-S batteries are competitive for the next-generation energy storage applications, however, soluble polysulfides cause severe shuttle effect and electrolyte abuse. Solid-solid and quasi-solid conversions are effective to address the...
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Chernobyl nuclear accident revealed from the 7010 m Muztagata ice core record
TL;DR: The total activity variation with depth from a 41.6 m Muztagata ice core, measured at 7010 m, recorded not only the 1963 radioactive layer due to the thermonuclear test, but also clearly the radioactive peak released by the Chernobyl accident in 1986 as mentioned in this paper.
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Suspended, Straightened Carbon Nanotube Arrays by Gel Chapping
Chunyan Ji,Hongbian Li,Luhui Zhang,Yu Liu,Yan Li,Yi Jia,Zhen Li,Peixu Li,Enzheng Shi,Jinquan Wei,Kunlin Wang,Hongwei Zhu,Dehai Wu,Anyuan Cao +13 more
TL;DR: Large-scale self-assembly of suspended, straightened, single-walled carbon nanotubes (SWNTs) across regular TiO(2) gel islands are reported, resulting in hybrid structures with tailored and neat morphology, and enhanced photoresponse.
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Adjusting inhomogeneous daily temperature variability using wavelet analysis
TL;DR: In this article, a wavelet-analysis-based homogenization method was developed for detecting and adjusting biases of variability in a daily climate observation series, which are influential in the estimation of climate extremes and relevant trends in the time series.
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A Fast and Accurate Method of Power Line Intelligent Inspection Based on Edge Computing
TL;DR: Experimental results show that the TensorRT optimized RepYOLO algorithm is four times the inference speed of YOLOv5 with a 1.2% increase in accuracy, and a two-stage cascaded method can achieve accurate and real-time pin defect detection on edge devices.