Communication-Serialization of Electrochemical Impedance Spectra to Forecast Low-Frequency Impedances via Deep Learning Neural Networks

被引：0

作者：

Cui, Chuanyu ^{[1
]}

Xu, Long ^{[1
]}

Gu, Ai ^{[2
]}

Yang, Hao ^{[1
]}

Xia, Dabiao ^{[1
]}

Lu, Qi ^{[1
]}

Zhao, Congcong ^{[1
]}

Guo, Qixun ^{[1
]}

机构：

[1] Jihua Lab, Div Mat Sci & Technol, Hefei 528000, Peoples R China

[2] China Elect Prod Reliabil & Environm Testing Res I, Guangzhou 511370, Peoples R China

来源：

JOURNAL OF THE ELECTROCHEMICAL SOCIETY | 2024年 / 171卷 / 08期

关键词：

electroanalytical electrochemistry; electrochemical engineering; electrochemical impedance spectroscopy; machine learning;

D O I：

10.1149/1945-7111/ad6c0c

中图分类号：

O646 [电化学、电解、磁化学];

学科分类号：

081704 ;

摘要：

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.

引用

页数：4

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