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Record W4416601101 · doi:10.1149/ma2025-02401962mtgabs

Characterization of Ionomer Dispersions Towards Improved Catalyst Ink Design for Proton Exchange Membrane Fuel Cells

2025· article· W4416601101 on OpenAlexaffabout
Alexi L. Pauls, Sharon Wong, Amy Yang, Carmen Chuy, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsIonomerProton exchange membrane fuel cellCatalysisMembraneSmall-angle X-ray scatteringSolventLayer (electronics)Membrane electrode assembly

Abstract

fetched live from OpenAlex

A key indicator of coated catalyst membrane (CCM) and membrane electrode assembly (MEA) performance in proton exchange membrane fuel cell systems is the formation of the catalyst-ionomer interface. 1,2 Ionomer coverage and thickness dictate the ion transport to the catalyst material (i.e., platinum, Pt). A thick layer of ionomer can block pores and slow oxygen permeability, resulting in lower electrochemically active surface area (ECSA) and activity whereas a thin layer of ionomer can result in a poor ionic network and slow transport of protons. 3 Further, inconsistencies in the layer can result in inconsistent ion transport throughout the catalyst layer. The conformation of ionomer can influence the formation of the catalyst-ionomer interface and thus the distribution of ionomer within the catalyst layer. 4 Due to both hydrophilic and hydrophobic regions present in ionomer, it is known to change conformation depending on the solvent system used; this can result in rod-like or spherical aggregates of ionomer particles. 5 Water content of the solvent system and pH have been found to correlate to increasing ionomer size and exposure of accessible protons respectively. 6,7 Ionomer conformation has previously been determined through both small-angle X-Ray scattering (SAXS) and small-angle neutron scattering (SANS) which has been used to study the rod-like and spherical shapes of the particles depending on the solvent system used. 8,9 While SAXS and SANS are useful techniques to assess ionomer conformation, further understanding of the size and accessibility of protons is important to elucidate further details on the ionomer-catalyst interface, and thus the effect of ionomer conformation on ionic conductivity and functionality (e.g., coatability, performance) of the catalyst layer. In this work, ionomer dispersions are investigated in both aqueous and mixed alcoholic solvent systems to gain an understanding of the size, conformation and viscosity through dynamic light scattering (DLS), SAXS and rheology. The use of DLS has previously been demonstrated for use of ionomer particles. 10 However, DLS size distribution models are built for spherical, monodispersed nanoparticles and are non-ideal for polymeric materials. 11 This work investigates the challenges, limitations and methods for consistent, reproducible measurements to evaluate size and shape of ionomer dispersions, with supporting techniques of SAXS and rheology. Continued work will investigate the relationship between the morphology of ionomer dispersions and the formation of the ionomer-catalyst interface and improved electrochemical performance. Acknowledgements This work was supported by Mitacs through the Mitacs Elevate program and performed in collaboration with Unilia (Canada) Fuel Cells. References 1) Li, C. et al. Energy Environ. Sci. , 2023 , 16 , 2977 2) Choi, WJ. et al. Korean J. Chem. Eng. 2024 . 3) Woo, S. et al . Curr. Opin. Electrochem. 2020 , 21 , 289-296 4) Yang, D. et al. Int. J. Hydron. Ener. 2021 , 46 , 66, 33300-33313 5) Tarokh, A. et al. Macromolecules , 2020 , 53 , 1 6) Berlinger, S. A. et al. J. Phys. Chem. B , 2018 , 122 , 31 7) Srivastav, H. et al. Langmuir , 2024 , 40 , 13, 8) Bird, A. et al. Adv. Energy Mater. 2025 , 2404242 9) Song, J. et al. Giant , 2024 , 20 , 100332 10) Orfanidi, A. et al. J. Electrochem. Soc. 2018 , 165 , F1254 11) Fischer, K.; Schmidt, M. Biomaterials , 2016 , 98 , 79-91

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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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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 designBench or experimental
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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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