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Record W4405588878 · doi:10.1149/ma2024-02432957mtgabs

Simulation of in-Situ Chemical Membrane Degradation in the Presence of Fe and Ce Cations in Reinforced Fuel Cell Membranes

2024· article· en· W4405588878 on OpenAlexaboutno aff
Nitish Kumar, Olivia C. Lowe, Amin Bahrami, Yixuan Chen, Francesco P. Orfino, Monica Dutta, Michael Lauritzen, Erin Setzler, Alexander Agapov, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2024
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMembraneIn situDegradation (telecommunications)Materials scienceFuel cellsChemical engineeringChemistryEngineeringOrganic chemistryElectronic engineeringBiochemistry

Abstract

fetched live from OpenAlex

A PEM fuel cell’s durability and lifetime are affected by various degradation phenomena, among which chemical degradation of PEM material exerts the most damage. Chemical degradation of PEM occurs when reactive radicals such as hydroxyl (·OH) and hydroperoxyl (·OOH) are generated from the decomposition of hydrogen peroxide. The radicals chemically attack the main and side chains of the perfluorosulfonic acid-based PEM and change its internal chemical structure. This leads to excessive membrane thinning and pinhole formation that causes higher gas crossover and electrode shorting inside the PEM fuel cell1. The generation of harmful radicals accelerates in the presence of Fenton’s metal, such as iron (Fe)2. Evaluation of PEM fuel cell chemical degradation durability is performed using long-term pure chemical accelerated stress test (AST) under high temperature (T), low relative humidity (RH), and open circuit voltage (OCV) conditions3. Physical characterization of a membrane sample used in a fuel cell can help to visualize and assess damage incurred during an AST. These results, however, fail to encompass the formation rate of radicals and intermediate degradation products generated during the AST3. To overcome these issues, computational fluid dynamics models are developed that simulate the evolution of degraded products and predict the lifetime of PEM fuel cells4,5. The present work aims to develop an in-situ one-dimensional (1-D) transient chemical degradation model that describes reaction-transport phenomena within the membrane electrode assembly (MEA) in the presence of Fe and Ce cations. This work considers mechanically reinforced GORE-SELECT® 15 µm baseline (MemA) and chemically and mechanically reinforced 12 µm (MemB-Ce) membranes. The developed in-situ model simulates pure chemical AST operating conditions with variable Fe ion loading and follows the computational algorithms originally presented by Wong and Kjeang4. The model successfully predicted that side chain scission is followed by main chain unzipping, as reported in the literature6. The evaluation of the change in ionomer structure by chemical degradation was done by computing the temporal concentration profile of intermediate degraded ionomer species, hydrogen peroxide, and membrane thickness reduction. The 1-D model also encapsulates the concentration profile of Fe2+ and Fe3+ ions across the PEM and predicts their temporal distribution throughout the simulated AST duration under OCV conditions. It was observed that the MemA thickness decreased rapidly in the presence of Fe ions, as shown in Figure 1. The computational parameters and results were calibrated and validated using in-house AST results for MEAs with embedded Fe metallic particles, which were shown to completely dissolve during conditioning and hence treated as uniformly distributed Fe cations with an initial concentration of 900 ppm at the beginning-of-life7. The model identified a limiting Fe ion concentration limit of 100 ppm that accentuates the chemical attack. Experimentally, a low Fe concentration of 10 ppm was determined in the baseline MemA, which also reduced the membrane thickness within 1125 h of operation, as shown in Figure 1. It is known that cerium (Ce) ions can scavenge harmful radicals and increase the lifetime of PEM fuel cells8. The model, therefore, simulates the effectiveness of Ce ions in the MemB-Ce membrane and the temporal distribution of Ce3+/Ce4+ ions and shows a lower rate of membrane thickness reduction, as shown in Figure 1. The modelling framework was, therefore, able to predict PEM fuel cell lifetime in conjunction with the transport behaviour of both harmful and beneficial cations as well as their interactions during the chemical degradation process at OCV conditions. Keywords: PEM fuel cell, fuel cell durability, chemical degradation model, in-situ modelling, iron, cerium Acknowledgements This research was supported by the Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Western Economic Diversification Canada, Ballard Power Systems, and W.L. Gore & Associates. It was also undertaken in part thanks to funding from the Canada Research Chairs program. References J. Healy et al., Fuel Cells, 5, 302–308 (2005). S. Kundu, L. C. Simon, and M. W. Fowler, Polym. Degrad. Stab., 93, 214–224 (2008). Y. Singh, F. P. Orfino, M. Dutta, and E. Kjeang, J. Electrochem. Soc., 164, F1331–F1341 (2017). K. H. Wong and E. Kjeang, J. Electrochem. Soc., 161, F823–F832 (2014). R. B. Ferreira, D. S. Falcão, and A. M. F. R. Pinto, Int. J. Hydrogen Energy, 46, 1106–1120 (2021). L. Ghassemzadeh, K. D. Kreuer, J. Maier, and K. Müller, J. Phys. Chem. C, 114, 14635–14645 (2010). N. Kumar et al., ECS Meet. Abstr., MA2023-02, 1919–1919 (2023). S. M. Stewart, D. Spernjak, R. Borup, A. Datye, and F. Garzon, ECS Electrochem. Lett., 3, F19–F22 (2014). Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.225
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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