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ciuccislab authored Jul 27, 2024
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Expand Up @@ -43,47 +43,8 @@ pip install pyDRTtools
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**How to cite this work?**


[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

if you want to add more details about standard regularization methods for computing the regularization parameter used in ridge regression, you should also cite the following references:

[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

if you are presenting the *Bayesian credible intervals* generated by the pyDRTtools in any of your academic works, you should cite the following references also:

[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

if you are using the pyDRTtools to compute the *Hilbert Transform*, you should cite:

[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
Just write to [email protected] or [email protected]

**How to get support?**

Just write to [email protected] or [email protected]

# References:
1. 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](https://doi.org/10.1149/1945-7111/aba937)
2. 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](https://doi.org/10.1016/j.electacta.2020.136864)
3. Ciucci, F. (2019). Modeling electrochemical impedance spectroscopy. Current Opinion in Electrochemistry, 13, 132-139. [doi.org/10.1016/j.coelec.2018.12.003](https://doi.org/10.1016/j.coelec.2018.12.003)
4. 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](https://doi.org/10.1016/j.electacta.2014.09.058)
5. 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](https://doi.org/10.1016/j.electacta.2015.09.097)
6. 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](https://doi.org/10.1016/j.electacta.2015.03.123)
7. 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](https://doi.org/10.1016/j.electacta.2017.07.050)
8. 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](https://doi.org/10.1016/j.electacta.2019.135316)
9. 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](https://iopscience.iop.org/article/10.1149/1945-7111/ab631a/meta)
10. 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

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