Litcius/Paper detail

Capabilities of a novel electrochemical cell for operando XAS and SAXS investigations for PEM fuel cells and water electrolysers

Marco Bogar, Yu. V. Yakovlev, Simone Pollastri, Roberto Biagi, Heinz Amenitsch, Rodolfo Taccani, Iva Matolı́nová

2024Journal of Power Sources13 citationsDOIOpen Access PDF

Abstract

Catalyst stability is a key issue in current electrochemical devices, such as fuel cells (FCs) and water electrolysers (WEs). While for FCs, the main degradation process limiting catalyst stability have been highlighted, a clear picture is still missing concerning WEs. In this framework, in operando analyses are essential to characterize catalyst degradation over time. As X-Rays constitute the perfect probe for studying catalytic materials, we here present a reversible electrochemical cell designed for operando X-Ray Absorption Spectroscopy and Small and Wide Angle X-Ray Scattering analyses, which was used: (i) to study Pt/C catalyst degradation coupling the evolution of specific electrochemically active surface area (ECSA) with catalyst morphology, supported by the analysis of Pt oxidation state. As a result, an increase of particle (and particle cluster) size is connected to the diminishing of ECSA and to the changes in the fraction of metallic-to-oxidised Pt, underlying that changes mainly develop in the first 2000 cycles of applied stress tests. Finally, (ii) we introduce some preliminary results underlying the change in Ir oxidation state for a standard Ir/IrOX catalyst material for WEs, showing as such a change is not sufficient to induce any remarkable morphological variations within 500 cycles of stress tests.

Topics & Concepts

CatalysisX-ray absorption spectroscopyProton exchange membrane fuel cellElectrochemistryParticle (ecology)Chemical engineeringDegradation (telecommunications)Materials scienceChemistryAbsorption spectroscopyElectrodeOrganic chemistryPhysical chemistryEngineeringTelecommunicationsPhysicsComputer scienceGeologyOceanographyQuantum mechanicsElectrocatalysts for Energy ConversionFuel Cells and Related MaterialsMachine Learning in Materials Science