Enhancing Analytical Performance in Cyclic Voltammetry: An Open-Source Tool for Signal Deconvolution

Cyclic voltammetry (CV) is a cornerstone of electrochemical analysis, yet the accurate determination of Faradaic peak heights is often compromised by overlapping signals and complex background currents. Traditional analysis relying on linear baseline subtraction is highly inaccurate, particularly for systems with multiple redox processes or interfering species. This work introduces a powerful and accessible automated fitting algorithm that uses semiderivative analysis to deconvolve complex voltammograms, suitable for linear diffusion controlled experiments conducted with planar working electrodes. The method employs flexible Pearson IV distributions to model a wide range of Faradaic peak shapes and introduces a novel piecewise function to accurately fit and subtract both capacitive and background electrolysis currents. The algorithm's efficacy is demonstrated on three challenging experimental systems: the reversible redox probe [Ru(NH₃)₆]Cl₃ in the presence of interfering oxygen reduction, the sequential ligand reductions of [Ru(bpy)₃](PF₆)₂ featuring heavily overlapping peaks, and the quantitative analysis of SO₂ obscured by a large oxygen reduction signal. The results show a dramatic improvement in accuracy and signal deconvolution over the conventional methods. To promote broad adoption, a user-friendly program and its Python source code have been made freely available to the electrochemistry community.