To investigate the application of our deconvolution approach to multidimensional impedance datasets, we first generated synthetic two-dimensional impedance data using the distant 2×ZARC model . Nyquist plots of the resulting synthetic experimental impedances are presented in Figure (a) and (b). To recover the DRTs, we applied in two distinct configurations: first, employing only the entropy regularization term (effectively setting λ τ = λ Ψ = 0 while retaining λ); and second, incorporating all three regularization parameters (λ, λ τ , and λ Ψ ). The deconvolved DRTs and the corresponding impedance fits are displayed in the lower and upper panels of Figure , respectively, contrasting the results obtained with and without the additional differentiation-based regularization terms. Our analysis revealed that the DRTs deconvolved using all three regularization parameters exhibited closer agreement with the exact DRT (Figure )) compared to those obtained using only entropy regularization (Figure ). Conversely, the impedance fits achieved comparable accuracy in both configurations, closely matching the exact impedance spectra. To better visualize the impact of the experimental conditions on the recovered DRTs, contour plots are presented in Figure . These plots demonstrate that the incorporation of λ τ and λ Ψ in enabled a smoother representation of the DRT variations as a function of experimental conditions, as evidenced by the smooth contour lines in Figure (b).