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2,057 results on '"Zhang, Xiaoyuan"'

271. Preoxidation and Prilling Combined with Doping Strategy to Build High-Performance Recycling Spent LiFePO4Materials

Author

Li, Xiangnan, Tang, Xinyu, Ge, Ming, Zhou, Qibin, Zhang, Xiaoyuan, Liu, Wenfeng, Zhang, Huishuang, Xie, Haijiao, Yin, Yanhong, and Yang, Shuting

Abstract

Direct regeneration has gained much attention in LiFePO4battery recycling due to its simplicity, ecofriendliness, and cost savings. However, the excess carbon residues from binder decomposition, conductive carbon, and coated carbon in spent LiFePO4impair electrochemical performance of direct regenerated LiFePO4. Herein, we report a preoxidation and prilling collaborative doping strategy to restore spent LiFePO4by direct regeneration. The excess carbon is effectively removed by preoxidation. At the same time, prilling not only reduces the size of the primary particles and shortens the diffusion distance of Li+but also improves the tap density of the regenerated materials. Besides, the Li+transmission of the regenerated LiFePO4is further improved by Ti4+doping. Compared with commercial LiFePO4, it has excellent low-temperature performance. The collaborative strategy provides a new insight into regenerating high-performance spent LiFePO4.

Published
2024
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273. Facile Synthesis of Dual-Network Polymer Hydrogels with Anti-Freezing, Highly Conductive, and Self-Healing Properties.

Author

Jin, Yuchen, Zhao, Lizhu, Jiang, Ya, Zhang, Xiaoyuan, and Su, Zhiqiang

Subjects
POLYMER networks, SOLUTION (Chemistry), HYDROGELS, POLYMERIZATION, METHYLCELLULOSE, MODULUS of elasticity
Abstract

We report the synthesis of poly(acrylamide-co-acrylic acid)/sodium carboxy methyl cellulose (PAMAA/CMC-Na) hydrogels, and subsequent fabrication of dual-network polymer hydrogels (PAMAA/CMC-Na/Fe) using as-prepared via the salt solution (FeCl3) immersion method. The created dual-network polymer hydrogels exhibit anti-swelling properties, frost resistance, high conductivity, and good mechanical performance. The hydrogel swells sightly when immersed in solution (pH = 2~11). With the increase in nAA:nAM, the modulus of elasticity experiences a rise from 1.1 to 1.6 MPa, while the toughness undergoes an increase from 0.18 to 0.24 MJ/m3. Furthermore, the presence of a high concentration of CMC-Na also contributes to the enhancement of mechanical strength in the resulting hydrogels, ascribing to enhanced physical network of the hydrogels. The minimum freezing point reaches −21.8 °C when the CMC-Na concentration is 2.5%, owing to the dissipated hydrogen bonds by the coordination of Fe3+ with carboxyl (-COO−) in CMC-Na and PAMAA. It is found that the conductivity of the PAMAA/CMC-Na/Fe hydrogels gradually decreased from 2.62 to 0.6 S/m as the concentration of CMC-Na rises. The obtained results indicates that the dual-network hydrogels with high mechanical properties, anti-swelling properties, frost resistance, and electrical conductivity can be a competitive substance used in the production of bendable sensors and biosensors. [ABSTRACT FROM AUTHOR]

Published
2024
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276. A residual graph convolutional network for setting initial flow field in computational fluid dynamics simulations.

Author

Zhang, Xiaoyuan, Sun, Guopeng, Zhang, Peng, Wang, Yueqing, Zhang, Jian, Deng, Liang, Lin, Jie, and Chen, Jianqiang

Subjects
*COMPUTATIONAL fluid dynamics, *ARTIFICIAL neural networks, *GRAPH neural networks, *DEEP learning, *RESEARCH personnel
Abstract

The computational cost of computational fluid dynamics (CFD) simulation is relatively high due to its computational complexity. To reduce the computing time required by CFD, researchers have proposed various methods, including efficient time advancement methods, correction methods for discrete control equations, multigrid methods, reasonable initial field setting methods, and parallel methods. Among these methods, the initial field setting method can provide significant performance improvements, but there is little work on it. Existing CFD industrial software typically uses inflow conditions for the initial flow field or applies empirical methods, which can cause instability in the CFD calculation process and make convergence difficult. With the rapid development of deep learning, researchers are increasingly attempting to replace CFD simulations with deep neural networks and have achieved significant performance improvements. However, these methods still face some challenges. First, they can only predict the computational flow field on regular grids. They cannot directly make predictions for irregular grids such as multi-block grids and unstructured grids, so the final flow field can only be obtained through interpolation and similar methods. Second, although these methods have been claimed to provide high accuracy, there is still a significant gap in performance with CFD and they cannot yet be applied to real scenarios. To address these issues, we propose a Residual Graph Convolutional Network for Initial Flow Field Setting (RGCN-IFS) in CFD simulations. This method converts the grid into a graph structure and uses an improved graph neural network to predict the flow field. In this way, we can predict the flow field on any type of grid. More importantly, this method does not directly replace CFD simulations, but it rather serves in an auxiliary role, providing appropriate initial flow fields for the CFD calculations, improving the convergence efficiency while ensuring calculation accuracy, and directly bridging the accuracy gap between intelligent surrogate models and CFD simulations. [ABSTRACT FROM AUTHOR]

Published
2024
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