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39 results on '"Xia, Dabiao"'

19. Study on the Thermal Expansion Behavior of Mg-3RE Alloys and Mg-3Y/SiCp Composites.

Author

Wang, Tong, Wang, Can, Feng, Chaoqi, Guo, Ruizhen, Hu, Wenyi, Xia, Dabiao, Le, Qichi, and Gupta, Manoj

Subjects
LEAD alloys, MAGNESIUM alloys, THERMAL expansion, CASTING (Manufacturing process), THERMAL stability
Abstract

The high coefficient of thermal expansion (CTE) of magnesium alloys leads to poor thermal dimensional stability and limits its application in the electronic field. This work focused on understanding the effect of rare earth (RE) on the thermal expansion behavior of Mg and attempted to tailor the CTE with SiC particulates. Composites were manufactured by gravity casting with ultrasonic dispersion treatment. The effect of 3 wt.% RE (Sm, Nd, Gd, Y) on the microstructure and thermal expansion behavior of the Mg alloys was explored. Adding RE can lead to the formation of RE-containing second phases and marginally decreased the CTE of magnesium. The CTEs of Mg-3Sm, Mg-3Nd, Mg-3Gd and Mg-3Y alloys were 27.3 × 10−6/°C, 27.0 × 10−6/°C, 27.2 × 10−6/°C and 27.8 × 10−6/°C, respectively. The influence of SiCp on the thermal expansion behavior of Mg-3Y was also investigated. The SiCp promoted the precipitation of Mg24Y5 phase in the extruded Mg-3Y/SiCp composites and significantly reduced the CTE. The CTEs of Mg/3SiC, Mg-3Y/1SiC, Mg-3Y/3SiC and Mg-3Y/5SiC were 27.4 × 10−6/°C, 26.8 × 10−6/°C, 26.6 × 10−6/°C and 23.3 × 10−6/°C, respectively. Rule of mixture, the Turner model and the Kerner model were utilized to predict the CTE of the Mg-3Y/xSiCp (x = 1, 3, 5) composites. The predictions of the Kerner model show a better correlation with the experimental results. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Communication—Serialization of Electrochemical Impedance Spectra to Forecast Low-Frequency Impedances via Deep Learning Neural Networks

Author

Cui, Chuanyu, Xu, Long, Gu, Ai, Yang, Hao, Xia, Dabiao, Lu, Qi, Zhao, Congcong, and Guo, Qixun

Abstract

Electrochemical impedance spectroscopy offers valuable insights into interfacial properties but acquiring low-frequency (LF) data can be time-consuming. This work explores time series analysis for forecasting LF impedance based on readily available high-frequency (HF) data. The time series methods like long-short term memory, convolutional neural network, and transformer were studied. Suitable neural network (NN) structure was proposed. The impact of forecasting window size was investigated to determine how much information was necessary for NN to establish useful connection. This approach holds promise for faster, more efficient, and insightful analysis of electrochemical systems.

Published
2024
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