App简介

DRTtools: 一个用于电化学阻抗谱数据分析的应用工具

我们很高兴向大家介绍DRTtools,这是一个基于原始DRTtools的Python版本应用,用于从电化学阻抗谱(EIS)数据计算弛豫时间分布(DRT)。

它包括以下功能:

• 一个直观的GUI,基于Tikhonov正则化计算DRT
• 多个选项用于优化DRT的估计
• 一个采样器,可以确定DRT的可信区间
• 最优选择正则化参数
• Hilbert变换子程序,可以评估和评分数据的质量

我们相信,这个工具箱对于解释EIS数据非常有用。如果你感兴趣,可以在用户指南以及下面的参考文献中找到有关该工具箱功能的详细说明。

致谢

我们要特别感谢DRTtools的作者，德国拜罗伊特大学Francesco Ciucci教授（原港科大）的研究团队，他们开发的DRTtools开源软件已经在学术界广泛应用了近十年。为了让Bohirum的用户可以更方便地访问和使用这个强大的工具,我们将它带到Bohrium平台。 DRTtools遵循MIT开源协议,我们欢迎并感谢社区的贡献和反馈。如果你有任何问题、建议或想参与贡献,请访问原作者的GitHub仓库。

最佳实践

用户可以在侧边栏设置好相关参数后，上传EIS数据文件（csv或txt格式）：

选择好对应的画图内容（Select Plot）后，数据内容即可展示。

而后我们需要选择计算DRT的方式，这里以Simple Run为例：

点击Run Processing，经过短暂的计算后，DRT图即会出现在下方。然后我们可以选择输出DRT结果，在对文件命名完成后，点击 Download DRT 即可实现下载。

参考文献

1. https://github.com/ciuccislab/pyDRTtools

2. Ciucci, F. (2020). The Gaussian process Hilbert transform (GP-HT): Testing the Consistency of electrochemical impedance spectroscopy data. Journal of The Electrochemical Society, 167, 12, 126503. https://doi.org/10.1149/1945-7111/aba937

3. Liu, J., Wan, T. H., & Ciucci, F. (2020).A Bayesian view on the Hilbert transform and the Kramers-Kronig transform of electrochemical impedance data: Probabilistic estimates and quality scores. Electrochimica Acta, 357, 136864. https://doi.org/10.1016/j.electacta.2020.136864

4. Ciucci, F. (2019). Modeling electrochemical impedance spectroscopy. Current Opinion in Electrochemistry, 13, 132-139. doi.org/10.1016/j.coelec.2018.12.003

5. Saccoccio, M., Wan, T. H., Chen, C., & Ciucci, F. (2014). Optimal regularization in distribution of relaxation times applied to electrochemical impedance spectroscopy: ridge and lasso regression methods-a theoretical and experimental study. Electrochimica Acta, 147, 470-482. doi.org/10.1016/j.electacta.2014.09.058

6. Wan, T. H., Saccoccio, M., Chen, C., & Ciucci, F. (2015). Influence of the discretization methods on the distribution of relaxation times deconvolution: implementing radial basis functions with DRTtools. Electrochimica Acta, 184, 483-499. doi.org/10.1016/j.electacta.2015.09.097

7. Ciucci, F., & Chen, C. (2015). Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: a Bayesian and hierarchical Bayesian approach. Electrochimica Acta, 167, 439-454. doi.org/10.1016/j.electacta.2015.03.123

8. Effat, M. B., & Ciucci, F. (2017). Bayesian and hierarchical Bayesian based regularization for deconvolving the distribution of relaxation times from electrochemical impedance spectroscopy data. Electrochimica Acta, 247, 1117-1129. doi.org/10.1016/j.electacta.2017.07.050

9. Liu, J., & Ciucci, F. (2019). The Gaussian process distribution of relaxation times: a machine learning tool for the analysis and prediction of electrochemical impedance spectroscopy data. Electrochimica Acta, 135316. doi.org/10.1016/j.electacta.2019.135316

10. Liu, J., & Ciucci, F. (2020). The deep-prior distribution of relaxation times. Journal of The Electrochemical Society, 167(2), 026506. 10.1149/1945-7111/ab631a

11. A. Maradesa, B. Py, T.H. Wan, M.B. Effat, F. Ciucci, Selecting the Regularization Parameter in the Distribution of Relaxation Times, Journal of the Electrochemical Society, 170 (2023) 030502. Link: https://doi.org/10.1149/1945-7111/acbca4

引用格式

[1] Wan, T. H., Saccoccio, M., Chen, C., & Ciucci, F. (2015). Influence of the discretization methods on the distribution of relaxation times deconvolution: implementing radial basis functions with DRTtools. Electrochimica Acta, 184, 483-499.*

Link: https://doi.org/10.1016/j.electacta.2015.09.097

如果您想添加有关计算岭回归中使用的正则化参数的标准正则化方法的更多详细信息，您还应该引用以下参考文献：

[2] A. Maradesa, B. Py, T.H. Wan, M.B. Effat, F. Ciucci, Selecting the Regularization Parameter in the Distribution of Relaxation Times, Journal of the Electrochemical Society, 170 (2023) 030502.

Link: https://doi.org/10.1149/1945-7111/acbca4

如果您在任何学术著作中展示 DRTtools 生成的贝叶斯可信区间，您还应该引用以下参考文献：

[3] Ciucci, F., & Chen, C. (2015). Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: A Bayesian and hierarchical Bayesian approach. Electrochimica Acta, 167, 439-454.

Link: https://doi.org/10.1016/j.electacta.2015.03.123

[4] Effat, M. B., & Ciucci, F. (2017). Bayesian and hierarchical Bayesian based regularization for deconvolving the distribution of relaxation times from electrochemical impedance spectroscopy data. Electrochimica Acta, 247, 1117-1129.

Link: https://doi.org/10.1016/j.electacta.2017.07.050

如果您使用 DRTtools 来计算Hilbert Transform，您应该引用：

[5] Liu, J., Wan, T. H., & Ciucci, F. (2020).A Bayesian view on the Hilbert transform and the Kramers-Kronig transform of electrochemical impedance data: Probabilistic estimates and quality scores. Electrochimica Acta, 357, 136864.

Link: https://doi.org/10.1016/j.electacta.2020.136864