A tutorial for the identification and quantitative disentangling of charge storage mechanisms by variable-rate cyclic voltammetry

• Identification and quantitative disentangling of Faradaic diffusion-limited, capacitive, and Faradaic non-diffusion-limited (pseudocapacitive) charge storage mechanism by variable-rate cyclic voltammetry. • Experimental validation of the technique using representative electrode-electrolyte interfaces with known charge storage mechanism. • Provided MATLAB GUI and Python notebook automate identification and disentangling with state-of-charge. • Tutorial for the rational design of energy storage materials with optimized energy and power. A quantitative electrochemical technique is presented for the identification of charge storage mechanisms in energy storage materials using cyclic voltammetry at various scan rates. This approach enables the distinction between Faradaic diffusion-limited, capacitive, and Faradaic non-diffusion-limited (pseudocapacitive) processes based on their characteristic dependencies on time of mass and charge transfer. Unlike traditional qualitative techniques that rely on the shape of voltammograms, the proposed tool determines the relative contributions of each mechanism with high accuracy across a range of electrochemical conditions. Representative Faradaic, capacitive and pseudocapacitive electrode and electrolyte materials were selected to exemplify each mechanism and validate the technique. To facilitate data analysis, a MATLAB graphical user interface was developed in addition to a Python-based Jupyter notebook. The tools automate the extraction of mechanism-specific contributions and visualize their variation with electrode potential, enabling efficient interpretation of large-scale datasets. This tutorial provides a standardized protocol for quantifying charge storage mechanisms in diverse electrode-electrolyte systems. The ability to differentiate and quantify charge storage mechanisms supports the rational design of energy storage materials with tailored energy and power characteristics. The method is broadly applicable to batteries, supercapacitors, and hybrid systems, and offers a foundation for advancing energy storage technologies that require both high performance and mechanistic clarity.