fuelcell: A Python package and graphical user interface for electrochemical data analysis
As the demand for sustainable, carbon-free electricity increases globally, development of electrochemical energy conversion devices is increasing rapidly. These devices include fuel cells, flow batteries, and water electrolysis cells. A wide range of diagnostic experiments is used to assess the performance, durability, and efficiency of electrochemical devices. (Bard & Faulkner, 2001; Newman & Thomas-Alyea, 2004). Among the most commonly used techniques are chronopotentiometry (CP), chronoamperometry (CA), cyclic voltammetry (CV), linear sweep voltammetry (LSV), and electrochemical impedance spectroscopy (EIS) experiments.(Bard & Faulkner, 2001; L. Wang, 2003; Newman & Thomas-Alyea, 2004; Orazem & Tribollet, 2008). Although these experimental protocols have been well-established in the field of electrochemistry, the protocols for analyzing electrochemical data have not been clearly standardized. Standardizing electrochemical data analysis will also aid in applying machine learning frameworks to extract valuable information from electrochemical data sets.