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Test details

Options
The inductive part of the impedance is not used for the DRT computation. Trimming it can help with visualization.
Narrowing the time constant range can be used to eliminate high-frequency artifacts or to exclude processes fitted to the diffusion part of the impedance.

The discretization basis defines the underlying model used to approximate the DRT. For most cases, the Cole-Cole basis (presuming ZARC elements) is a reasonable choice. More information on the bases can be found here: DOI:10.1016/j.jpowsour.2025.237403.
The Debye basis is what is used in most commonly available DRT methods. Therein, the impedance is reconstructed using ideal RC elements (i.e., Dirac pulses in the relaxation time domain), yielding singular DRT peaks. Thus, regularization (e.g., Tikhonov) is typically required to avoid overfitting.
The Gaussian basis presumes normally-distributed relaxation time constants around a central value. Its shape is controlled by the full-width at half maximum (which is related to the standard deviation). Note: For real-world systems involving porous electrodes, the Cole-Cole basis is typically more accurate.
The Cole-Cole basis assumes ZARC elements (with non-ideal capacitances), making it a great fit for real-world impedance spectra of porous electrodes. The shape is defined by α and regularization is typically not needed.
The Havriliak-Negami relaxation is used to model dispersed, skewed processes. It comprises two shape parameters (α and β), controlling the shape and asymmetry of the basis function, respectively.

Data export
Debye basis does not natively return per-process information. Impedance representations are not yet available for Gauss-type bases, hence no per-process impedance.

About

QuickDRT is a fast and easy-to-use tool (or demo at this point) to compute distribution of relaxation time functions (DRTs) from measured impedance data. In an attempt to make this technique more accessible, it is built on a JavaScript implementation of PyDRT, so it runs without any Python backend in the browser (even on mobile devices). It can also be downloaded (see github.com/robertleonhardt/QuickDRT) for offline use (handy for Gamry computers, though).

The fundamental approach for DRT reconstruction used is based on basis functions; see DOI:10.1016/j.jpowsour.2025.237403 for more details. If this tool is helpful to you, please consider citing our underlying work:

Leonhardt, R., Krug von Nidda, J., Andrae, D., Schmidt, A., Kowal, J., & Tichter, T. (2025). Reconstructing the distribution of relaxation times with analytical basis functions. Journal of Power Sources 652.
Link

For any inquiries, ideas, and questions, feel free to contact me on LinkedIn or via github.com/robertleonhardt/QuickDRT.

Main references & acknowledgements:

Moreover, the following tools & libraries were used for this project:

Supported file types

In the current version, the QuickDRT is created primarily for Gamry-type test data, which are consequently supported natively. If you happen to use data from other devices there is a more generic CSV format, that is supported as well.


Generic CSV file structure compatible with QuickDRT:

#{"date_testbegin_datetime": "06.11.2024 18:10:24", "test_id": "02-02A08", "cell_id": "24-24-006"}
frequency_Hz,z_real_Ohm,z_imag_Ohm
100019.5,0.077595,0.130926
79511.72,0.068182,0.1103418
63105.47,0.0604157,0.0926967
50214.84,0.0541735,0.0776666
39902.34,0.0490697,0.0651172
31699.22,0.0449101,0.0546617
25136.72,0.041423,0.045863
19980.47,0.0384077,0.0386171
15878.91,0.0357307,0.032516
12597.66,0.0329833,0.0268193
10019.53,0.0313455,0.0225724
[...]

Therein, the first comment row can contain any metadata (datetime, cell id, etc.).