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1,057 results on '"Wang, Zhenxin"'

1. Large-area, freestanding single-crystal gold of single nanometer thickness

Author

Pan, Chenxinyu, Tong, Yuanbiao, Qian, Haoliang, Krasavin, Alexey V., Li, Jialin, Zhu, Jiajie, Zhang, Yiyun, Cui, Bowen, Li, Zhiyong, Wu, Chenming, Wang, Zhenxin, Liu, Lufang, Li, Linjun, Guo, Xin, Zayats, Anatoly V., Tong, Limin, and Wang, Pan

Subjects
Physics - Optics
Abstract

Two-dimensional single-crystal metals are highly sought after for next-generation technologies. Here, we report large-area (>10^4 {\mu}m2), single-crystal two-dimensional gold with thicknesses down to a single-nanometer level, employing an atomic-level-precision chemical etching approach. The ultrathin thickness and single-crystal quality endow two-dimensional gold with unique properties including significantly quantum-confinement-augmented optical nonlinearity, low sheet resistance, high transparency and excellent mechanical flexibility. By patterning the two-dimensional gold into nanoribbon arrays, extremely-confined near-infrared plasmonic resonances are further demonstrated with quality factors up to 5. The freestanding nature of two-dimensional gold allows its straightforward manipulation and transfer-printing for integration with other structures. The developed two-dimensional gold provides an emerging platform for fundamental studies in various disciplines and opens up new opportunities for applications in high-performance ultrathin optoelectronic, photonic and quantum devices.

Published
2023
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17. Optimized MALDI-TOF MS Strategy for Characterizing Polymers

Author

Wang, Zhenxin, Zhang, Quanqing, Shen, Huali, Yang, Pengyuan, and Zhou, Xinwen

Subjects
Analytical Chemistry, Chemical Sciences, Physical Chemistry, MALDI-TOF MS, polymer, polyvinylpyrrolidone K12, polymer polyol KPOP-5040, polyether polyol DL-4000, polyethylene glycol 6000, Chemical sciences
Abstract

In recent years, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) plays an essential role in the analysis of polymers. To acquire a more reliable strategy for polymer profiling, we characterized four representative polymers including polyethylene glycol 6000, polyvinylpyrrolidone K12, polymer polyol KPOP-5040, and polyether polyol DL-4000. The preparation methods of these four polymer samples have been optimized from six aspects, including matrix, cationization reagent, solvent, mixing ratio of cationization reagent to polymer, mixing ratio of matrix to polymer, and laser intensity. After investigating the effects of seven commonly used matrices on the ionization efficiency of four polymers, trans-2-[3-(4-tert-butylphenyl)-2-methyl-2-propenylidene] malononitrile (DCTB) was found to be the only matrix suitable for the analysis of all the four polymers. Our experimental results suggested that different polymers showed a certain preference for different cationization reagents. For example, the polymer polyol KPOP-5040 was suitable for sodium iodide as the cationization reagent, while polyvinylpyrrolidone K12 was more suitable for silver trifluoroacetate (AgTFA). For the choice of solvent, tetrahydrofuran is a reagent with rapid evaporation and a wide range of dissolution which can achieve the best results for the analysis of four polymers. The optimized method was successfully applied to the identification of DSPE-PEG-NH2 with different polymerized degrees. This MALDI-TOF strategy potentially provided the supplementary function through the polymer's application in biomedical and visible probing.

Published
2021

24. Based on Internet of Things Platform Using NB-IoT Communication Low-Power Weather Station System

Author

Wang, Zhenxin, Deng, Zhi, Xu, Ke, Zhang, Ping, Liu, Tao, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Qian, Zhihong, editor, Jabbar, M.A., editor, and Li, Xiaolong, editor

Published
2022
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30. Outline Objects using Deep Reinforcement Learning

Author

Wang, Zhenxin, Sarcar, Sayan, Liu, Jingxin, Zheng, Yilin, and Ren, Xiangshi

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Image segmentation needs both local boundary position information and global object context information. The performance of the recent state-of-the-art method, fully convolutional networks, reaches a bottleneck due to the neural network limit after balancing between the two types of information simultaneously in an end-to-end training style. To overcome this problem, we divide the semantic image segmentation into temporal subtasks. First, we find a possible pixel position of some object boundary; then trace the boundary at steps within a limited length until the whole object is outlined. We present the first deep reinforcement learning approach to semantic image segmentation, called DeepOutline, which outperforms other algorithms in Coco detection leaderboard in the middle and large size person category in Coco val2017 dataset. Meanwhile, it provides an insight into a divide and conquer way by reinforcement learning on computer vision problems.

Published
2018

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