Physics Based Electrochemical Impedance Model to Diagnose Pore Level Effects in PEFC Electrodes

Sunday, 9 October 2022: 12:00
Galleria 3 (The Hilton Atlanta)
J. Kaur, T. Kadyk, and M. Eikerling (Forschungszentrum Jülich GmbH)
The performance of the cathode catalyst layer (CCL) in a polymer electrolyte fuel cell (PEFC) is determined by materials properties, which in turn depend on electrode composition and structure. The intricate porous network morphology of the CCL entails voltage losses due to various kinetic and transport properties. The CCL is built from nanometer sized catalyst particles (to maximize the surface area) that are attached to carbon particles. These particles assemble into agglomerates with nanoscale primary pores formed in or between the particles and secondary pores formed between the agglomerates [1]. The latter type of pores are essential for the effective transport of oxygen throughout the layer. Diagnosing the interplay of electrochemical reaction and transport in this structure under different conditions helps materials development by assessing bottlenecks and potentials. Electrochemical impedance spectroscopy (EIS), is extensively used as a noninvasive diagnostic tool for material characterization and to investigate the state of health of the FC [2]. We present a physics-based EIS model to investigate pore level impedance effects [3,4,5]. A water-filled Pt nanopore with a single opening is modeled as a representative element in an electrocatalytically active agglomerate (Fig. 1). The model accounts for proton conduction and oxygen transport in the pore, as well as interfacial effects viz. double-layer charging, oxygen chemisorption and interfacial water formation. Approximate analytical solutions for the impedance response in various limiting cases are derived. Uses of these solutions will be demonstrated in the extraction of transport and kinetic parameters from experimental EIS; rationalizing relationships between structure, properties and EIS responses, to be able to infer electrode structure information from measured EIS spectra; and assessing the state of health of a cell based on characteristic features or fingerprints in the EIS response.

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