Litcius/Paper detail

Performance Losses and Current-Driven Recovery from Cation Contaminants in PEM Water Electrolysis

Elliot Padgett, Anthony Adesso, Haoran Yu, Jacob A. Wrubel, Guido Bender, Bryan S. Pivovar, Shaun M Alia

2024Journal of The Electrochemical Society37 citationsDOIOpen Access PDF

Abstract

Water contaminants are a common cause of failure for polymer electrolyte membrane (PEM) electrolyzers in the field as well as a confounding factor in research on cell performance and durability. In this study, we investigated the performance impacts of feed water containing representative tap water cations at concentrations ranging from 0.5–500 μ M, with conductivities spanning from ASTM Type II to tap-water levels. We present multiple diagnostic signatures to help identify the presence of contaminants in PEM electrolysis cells. Through analysis of polarization curves and impedance spectroscopy to understand the origins of performance losses, we found that a switch from the acidic to alkaline hydrogen evolution mechanism is a key factor in contaminated cell behavior. Finally, we demonstrated that this mechanism switching can be harnessed to remove cation contaminants and recover cell performance without the use of an acid wash. We demonstrated near-complete recovery of cells contaminated with sodium and calcium, and partial recovery of a cell contaminated with iron, which was further investigated by post-mortem microscopy. The improved understanding of contaminant impacts from this work can inform development of strategies to mitigate or recover performance losses as well as improve the consistency and rigor of electrolysis research.

Topics & Concepts

ElectrolysisContaminationElectrolysis of waterElectrolyteTap waterPolymer electrolyte membrane electrolysisDielectric spectroscopyPolarization (electrochemistry)Chemical engineeringChemistryElectrolytic cellEnvironmental scienceMaterials scienceEnvironmental engineeringElectrodeElectrochemistryBiologyEcologyEngineeringPhysical chemistryFuel Cells and Related MaterialsElectrocatalysts for Energy ConversionMachine Learning in Materials Science