DRT inversion was calculated using a home-written Python script , using the imaginary part of the impedance data. The DRT relaxation time vector τ was chosen from 10 -10 s to 10 10 s and contained 500 logarithmically spaced data points. Before calculating the final DRT, the algorithm was performed twice to obtain additional parameters. The first time, an impedance data scaling factor was chosen to normalize the variance of the residuals between the experimental data and the fit, determined by transforming the calculated DRT back into the frequency domain, to a value of one. This stabilized subsequent numerical calculation. The second time, the residuals were used to generate a frequency dependent noise level vector estimation via GPR using the numerical noise estimation algorithm written by Metzen and Lemaitre in scikit , which was applied in a logarithmically scaled sliding window with a length of two decades. The third and final DRT was calculated by using the scaling factor and weights obtained in the previous steps.