Citation & Credits

DRTxECM is built on top of pyDRTtools. The DRT computation results are the work of pyDRTtools, so you must cite the corresponding original references when you use them. This page sets out clearly what to cite, and when.

Citing DRTxECM itself

DRTxECM does not yet have a formally published paper. If you need to credit the tool you have used, you can cite the software repository itself:

@software{DRTxECM,
  title        = {DRTxECM: A pyDRTtools Extension for CPE
                  Phase-Angle-Aware Equivalent Circuit Modeling},
  author       = {{Linch-Lab}},
  year         = {2026},
  version      = {0.2.0},
  url          = {https://github.com/Linch-Lab/DRTxECM},
  license      = {MIT},
  note         = {Three-stage pipeline: DRT deconvolution,
                  Gaussian peak decomposition,
                  CNLS equivalent circuit fitting}
}

If a paper is published in the future, this page will be updated. You can also simply cite the GitHub repository URL.

References that must be cited when using DRT computation results

The DRT core of DRTxECM is pyDRTtools. The list below matches the official requirements of pyDRTtools, and the numbering follows the DRTxECM README.

1. Foundational theory and discretization method (must cite)

Whenever you use DRT computation results, you should cite this paper. It is the foundation of the RBF discretization and of DRTtools.

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:10.1016/j.electacta.2015.09.097

2. Choice of the regularization parameter

If you use the automatic λ-selection methods GCV, mGCV, rGCV, LC, kf or re-im, please cite this one as well.

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

3–4. Bayesian methods and credible intervals

If you use Bayesian Run, or present Bayesian credible intervals in a paper, please cite these two.

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:10.1016/j.electacta.2015.03.123

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:10.1016/j.electacta.2017.07.050

5. Hilbert Transform (BHT)

If you use the Hilbert Transform button (the BHT method) or the EIS Score, please cite this paper.

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. doi:10.1016/j.electacta.2020.136864

Further reading (GP-DRT and method reviews)

The fGP.py in the package implements GP-DRT (Gaussian process). If you use it, the following references may be consulted:

• 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:10.1016/j.electacta.2019.135316
• 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. doi:10.1149/1945-7111/aba937
• Ciucci, F. (2019). Modeling electrochemical impedance spectroscopy. Current Opinion in Electrochemistry, 13, 132–139. doi:10.1016/j.coelec.2018.12.003
• 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:10.1016/j.electacta.2014.09.058

License

DRTxECM is released under the MIT License.

Specifically, the DRT computation core (basics.py, runs.py, BHT.py, HMC.py, fGP.py, parameter_selection.py, peak_analysis.py, nearest_PD.py) retains the original pyDRTtools implementation; the DRTxECM extensions are in extensions.py, and GUI.py, layout.py and cli.py have been modified. Both are MIT-licensed, so combining and redistributing them is permitted.

The MIT License permits free use, modification and distribution, including commercial use. The only substantive condition is to retain the copyright and license notices.

Acknowledgements

The greatest thanks for making this project possible go to Ciucci Lab (The Hong Kong University of Science and Technology / University of Bayreuth), who developed and released pyDRTtools under the MIT License, as well as the earlier DRTtools. The DRT stage of DRTxECM is entirely their work.

Thanks are also due to the pyDRTtools user community — the issues reported and the discussions made the requirements and boundary conditions of the equivalent-circuit extension clear.

Sponsorship

DRTxECM is completely free and open source, with no ads, no tracking and no paid edition. If it has saved you some time, you are welcome to buy the developer a coffee.

QR code (TWQR / Taiwan Pay)

Scan it with a Taiwanese banking or mobile payment app; no extra sign-up is needed.

What we will not do: lock features behind sponsorship, add ads or tracking, use emotional pressure, or let sponsors jump the queue.