Open Software and Datasets for the Analysis of Electrochemical Impedance Spectra

Abstract

Extracting quantitative information from electrochemical experiments relies on mathematical models of the system under study. Over the past century, many simplified electrochemical systems have been analyzed mathematically, typically resulting in univariate plotting methods where physicochemical parameters are extracted from linear best-fits of experimental data. This approach includes famously successful methods such as Tafel plots, Nernst plots, Levich, and Koutecký-Levich plots, to name just a few. These graphical methods generally require a half-cell experiment that naturally has, or can be manipulated to have, just one or two dominant physicochemical processes governing the electrochemical response. There are a myriad of ways that voltammetric, amperometric, microelectrode, and hydrodynamic electrode half-cell experiments have been devised to fit this limiting-case paradigm. 1 Even in more complex electrochemical systems where coupled physicochemical processes obscure simple analysis, or in whole-cell systems where both electrodes contribute to the overall electrochemical response, thoughtful experimental design sometimes allows inference of the complex underlying phenomena from the experimental data. 2 In today's era of ubiquitous computing and data-rich experimentation, it is increasingly possible to use more sophisticated mathematical models to qualitatively and quantitatively interpret complex electrochemical experiments. [3][4][5][6][7][8] Across the broader scientific community, the culture around creating open source software and open datasets is a force for accelerating discovery and impact. [9][10][11][12] In this article, we primarily discuss one particular example of a communitydriven, open source software toolkit for analyzing electrochemical impedance spectroscopy (EIS) data. It is important to note that much of what we show here can be done today using commercial or closed-source software; however, by establishing an open framework for EIS analysis, and open datasets for testing models, we believe the convergence between complex physicochemical models and experimental data will be accelerated. A further benefit of open software and open data is that the reproducibility of the scientific literature can be improved.The toolkit we describe here is one of several projects that have come out of The Electrochemical Society's Hack Week and Data Science Sprint events; some of the other projects are briefly discussed in the Outlook section.

3D Printed Thick Reduced Graphene Oxide: Manganese Oxide/Carbon Nanotube Hybrid Electrode with Highly Ordered Microstructures for Supercapacitors

“…The equivalent circuit was one electrolyte resistance and two semicircles with the addition of a semicircle followed by a Warburg tail, which was properly fitted by using Python. [ 45,46 ] The resistances of 3D printed thick rGO:MnO x /CNT (5:3) electrodes and casted rGO:MnO x /CNT (5:3) electrodes were summarized and displayed in the Table 1 . R Ω and R ct of 3D printed 1.2 mm thick electrodes were bigger than 1.6 and 2 mm 3D printed rGO:MnO x /CNT (5:3), which accounted for that more decomposition and passivation layer were produced once the largest capacity was reached for thinner electrodes.…”

Interface Modification of lithium Metal Anode and Solid-state Electrolyte with Gel Electrolyte

“…Equivalent circuit (EC) modeling was performed using the impedance.py package in Python3. 28 Li/Li symmetric cell cycling was performed on Arbin Instruments BT-2000 galvanostat at 55 °C using a 0.05 mA cm −2 current density.…”

Modification of Lithium Metal Anode and Solid-State Electrolyte Interface with a Gel Electrolyte

“…Equivalent circuit (EC) modeling was performed using the impedance.py package in Python3. 26 Li/Li symmetric cell cycling was performed on Arbin Instruments BT-2000 galvanostat at 55 °C using a 0.05 mA/cm 2 current density.…”