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412 results on '"Lu, Yunxiang"'

55. Application of Nano-Crystalline Diamond in Tribology

Author

Xia, Yue, primary, Lu, Yunxiang, additional, Yang, Guoyong, additional, Chen, Chengke, additional, Hu, Xiaojun, additional, Song, Hui, additional, Deng, Lifen, additional, Wang, Yuezhong, additional, Yi, Jian, additional, and Wang, Bo, additional

Published
2023
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57. CO2 capture through halogen bonding: A theoretical perspective

Author

Li, HaiYing, Lu, YunXiang, Zhu, Xiang, Peng, ChangJun, Hu, Jun, Liu, HongLai, and Hu, Ying

Abstract

Abstract: Halogen bonding interactions between several halogenated ion pairs and CO2 molecules have been investigated by means of density functional theory calculations. To account for the influence of solvent environment, the implicit polarized continuum model was also employed. The bromide and iodide cations of ionic liquids (ILs) under study can interact with CO2 molecules via X⋯O interactions, which become much stronger in strength than those in the complexes of iodo-perfluorobenzenes, very effective halogen bond donors, with CO2 molecules. Such interactions, albeit somewhat weaker in strength, are also observed between halogenated ion pairs and CO2 molecules. Thus, the solubility of CO2 may be improved when using halogenated ILs, as a result of the formation of X⋯O halogen bonds. Under solvent effects, the strength of the interactions tends to be weakened to some degree, with a concomitant elongation of intermolecular distances. The results presented here would be very useful in the design and synthesis of novel and potent ILs for CO2 physical absorption.

Published
2024
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58. Spatiotemporal Evolution of Large-Scale Bidirectional Associative Memory Neural Networks With Diffusion and Delays

Author

Lu, Yunxiang, Xiao, Min, Liang, Jinling, Chen, Jing, Lin, Jinxing, Wang, Zhengxin, and Cao, Jinde

Abstract

In this article, the heterogeneity of the electromagnetic field is taken into account and thus the diffusion effect is introduced into the artificial neural network modeling. The first attempt of a class of large-scale bidirectional associative memory neural networks is provided, incorporating diffusion and delays. Using Coates’ flow diagram is able to efficiently and accurately capture the characteristic equations of large-scale reaction-diffusion neural networks. Furthermore, by tracing the distribution of characteristic roots driven by the time delay, a criterion on the local stability is determined and the critical tipping point caused by Hopf bifurcation is also predicted, respectively. Numerical simulations are eventually conducted to demonstrate the practical implications of the theory. It is shown that spatiotemporal dynamic behaviors of neural networks suggested are significantly affected by the transmission delay, the system scale, the self-feedback coefficient and the diffusivity.

Published
2024
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