通过减少对噪声低频数据的依赖来加速电化学阻抗谱测量
Accelerating Electrochemical Impedance Spectroscopy Measurements by Reducing Reliance on Noisy Low-Frequency Data
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中文总结 AI 辅助
研究针对电化学阻抗谱低频测量问题，提出统计推断辅助框架，通过增加高频采样密度减少对低频采样依赖，利用AutoEIS和贝叶斯推断重建全阻抗谱，引入临界频率评估性能，实现加速测量并提供实用框架与应用指导。
中文摘要 AI 辅助
电化学阻抗谱（EIS）是探测电化学系统中动力学和传输过程的有力工具，但低频测量的长采集时间和噪声敏感性限制了其实际应用。本文提出一种统计推断辅助框架，通过增加高频区域采样密度减少对低频采样的依赖。利用AutoEIS和贝叶斯推断，对增强高频数据拟合等效电路模型重建全阻抗谱并量化参数不确定性。引入临界频率评估重建性能。结果表明额外高频采样可提高临界频率并减少总测量时间，具体改善取决于数据噪声水平、添加数据点数和等效电路模型结构。这项工作为设计快速高效的EIS数据采集提供实用框架，并为在实际电化学表征中应用部分频率EIS重建提供指导。
英文摘要
Electrochemical impedance spectroscopy (EIS) is a powerful tool for probing kinetic and transport processes in electrochemical systems, but its practical use is often limited by the long acquisition time and noise sensitivity of low-frequency measurements. Here, we present a statistical inference-assisted framework that reduces the reliance on low-frequency sampling by increasing the sampling density in cleaner high-frequency regions. Using AutoEIS and Bayesian inference, the augmented high-frequency data are fitted to selected equivalent circuit models (ECMs) to reconstruct the full impedance spectrum and quantify parameter uncertainty. To evaluate reconstruction performance, we introduce the critical frequency (fc), defined as the highest cutoff frequency at which the full EIS response can still be reliably recovered. The results show that additional high-frequency sampling can not only shift fc to higher values but also reduce the total measurement time. The specific improvement depends on the noise level of the EIS data, the number of added data points, and the ECM structure. Overall, this work provides a practical framework for designing fast and efficient EIS data acquisition and offers guidance for applying partial-frequency EIS reconstruction in real electrochemical characterization.
