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Your search keyword '"Kang, Li‐Xia"' showing total 19 results
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19 results on '"Kang, Li‐Xia"'

6. Collective Quantum Magnetism in Nitrogen-Doped Nanographenes

Author

Zhu, Gucheng, primary, Jiang, Yashi, additional, Wang, Ying, additional, Wang, Bing-Xin, additional, Zheng, Yuqiang, additional, Liu, Yufeng, additional, Kang, Li-Xia, additional, Li, Zhanbo, additional, Guan, Dandan, additional, Li, Yaoyi, additional, Zheng, Hao, additional, Liu, Canhua, additional, Jia, Jinfeng, additional, Lin, Tao, additional, Liu, Pei-Nian, additional, Li, Deng-Yuan, additional, and Wang, Shiyong, additional

Published
2023
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7. Polymerization of Epoxides via Ring-Opening Coupling on Surfaces

Author

Kang, Li-Xia, primary, Liu, Jian-Wei, additional, Li, Shi-Wen, additional, Li, Can, additional, Wang, Ying, additional, Xu, Qian, additional, Hu, Jun, additional, Ding, Honghe, additional, Zhu, Junfa, additional, Wang, Shiyong, additional, Li, Deng-Yuan, additional, and Liu, Pei-Nian, additional

Published
2022
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10. On‐Surface Synthesis of [3]Radialenes via [1+1+1] Cycloaddition

Author

Li, Deng‐Yuan, primary, Wang, Ying, additional, Hou, Xiao‐Yu, additional, Ren, Yin‐Ti, additional, Kang, Li‐Xia, additional, Xue, Fu‐Hua, additional, Zhu, Ya‐Cheng, additional, Liu, Jian‐Wei, additional, Liu, Mengxi, additional, Shi, Xing‐Qiang, additional, Qiu, Xiaohui, additional, and Liu, Pei‐Nian, additional

Published
2022
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14. Shale gas well flowback rate prediction for Weiyuan field based on a deep learning algorithm

Author

Yu Rongze, MA Xin-hua, Sun Yuping, Guo Wei, Zhang Xiaowei, Liu Yuyang, and Kang Li-xia

Subjects
Artificial neural network, business.industry, Shale gas, Deep learning, Data correlation, 02 engineering and technology, 010502 geochemistry & geophysics, Geotechnical Engineering and Engineering Geology, 01 natural sciences, Nonlinear system, Fuel Technology, 020401 chemical engineering, Feedforward neural network, Field based, Artificial intelligence, 0204 chemical engineering, business, Algorithm, Geology, 0105 earth and related environmental sciences
Abstract

The flowback rate of a shale gas well is controlled by many factors, such as geological and engineering factors, which have a certain complexity. For the aforementioned reason, the knowledge-driven method often has difficulty effectively capturing the correlation rules among multiple datasets. As a data-driven algorithm, deep learning has strong advantages in data correlation analysis, nonlinear fitting and other applications. In this paper, an algorithm is set up to forecast the shale gas well flowback rate by using a deep learning algorithm based on the flowback characteristic factors and data from 286 shale gas wells in the Weiyuan field. An algorithm and prediction model of the flowback parameters of the Weiyuan shale gas wells is established to effectively capture their flowback characteristics. First, the general situation of the study area is briefly introduced, and the data used to predict the flowback rate in this area are analyzed. Second, based on the deep learning algorithm, two kinds of deep feedforward neural network structures and a neural network-based prediction method of shale gas well flowback rate are designed. Finally, according to the regional data, the prediction and analysis of the flowback rate of Weiyuan shale gas wells are carried out. The results show that the correlations between 17 factors, such as fracturing section length and shale gas well flowback rate, are not obvious, but that the deep learning method proposed in this paper can effectively and accurately capture the correlations between the data and predict the flowback rate. The results show high accuracy.

Published
2021

19. Two-Dimensional Nonbenzenoid Heteroacene Crystals Synthesized via In-Situ Embedding of Ladder Bipyrazinylenes on Au(111).

Author

Wang Y, Gong WW, Zhao Y, Xing GY, Kang LX, Sha F, Huang ZY, Liu JW, Han YJ, Li P, Li DY, and Liu PN

Abstract

Precisely introducing topological defects is an important strategy in nanographene crystal engineering because defects can tune π-electronic structures and control molecular assemblies. The synergistic control of the synthesis and assembly of nanographenes by embedding the topological defects to afford two-dimensional (2D) crystals on surfaces is still a great challenge. By in-situ embedding ladder bipyrazinylene (LBPy) into acene, the narrowest nanographene with zigzag edges, we have achieved the precise preparation of 2D nonbenzenoid heteroacene crystals on Au(111). Through intramolecular electrocyclization of o-diisocyanides and Au adatom-directed [2+2] cycloaddition, the nonbenzenoid heteroacene products are produced with high chemoselectivity, and lead to the molecular 2D assembly via LBPy-derived interlocking hydrogen bonds. Using bond-resolved scanning tunneling microscopy, we determined the atomic structures of the nonbenzenoid heteroacene product and diverse organometallic intermediates. The tunneling spectroscopy measurements revealed the electronic structure of the nonbenzenoid heteroacene, which is supported by density functional theory (DFT) calculations. The observed distinct organometallic intermediates during progression annealing combined with DFT calculations demonstrated that LBPy formation proceeds via electrocyclization of o-diisocyanides, trapping of heteroarynes by Au adatoms, and stepwise elimination of Au adatoms., (© 2024 Wiley-VCH GmbH.)

Published
2024
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