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Record W2193984314 · doi:10.1149/ma2015-01/38/2048

Understanding Transport Phenomena in Perfluorosulfonic-Acid Membranes

2015· article· en· W2193984314 on OpenAlexaff
Adam Z. Weber, Jeff T. Gostick, Ahmet Kusoglu, Andrew R. Crothers

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsInterconnectivityMembraneWater transportProton exchange membrane fuel cellElectrolyteConductivityProton transportChemical physicsProtonPhase (matter)Materials scienceNanoscopic scaleChemical engineeringFuel cellsPolymerBiological systemNanotechnologyChemistryComputer sciencePhysicsComposite materialElectrodeEnvironmental scienceWater flowEnvironmental engineeringEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

One of the keys to optimizing polymer-electrolyte-fuel-cell performance is understanding its transport phenomena. This is especially important for the proton-exchange membrane, which helps to control the water management within the cell, an integral aspect for optimal cell performance. Typical fuel-cell membranes are perfluorosulfonic-acid based and are essentially random copolymers that phase separate into hydrophilic domains for water and ion movement supported by a hydrophobic backbone phase. We have previously shown that water transport and uptake at the nano- and macroscale is similar and relatively fast in membranes, especially at higher humidities.1,2 Once water crosses the membrane interface, time-resolved studies show that changes at the nano and macroscale scales occur almost simultaneously. Unlike water incorporation, proton conduction depends not just on the domains but also their interconnectivity. Time-resolved studies such as that in Figure 1, show that unlike the water incorporation, there is more of a time delay due to reorientation of the domains and the transport pathways. In this talk, such issues will be discussed. In particular is the need to explore the structure-function relationships for the transport properties and the critical aspects that the membrane interface plays in controlling transport. In addition to experimental results, a model methodology will be presented to explore proton conductivity. In the model, we propose a macroscopic treatment that utilizes data on the nanoscale relating to membrane morphology and physically relevant phenomena,3 but in a framework that still allows for easy incorporation into cell-level fuel-cell models. Extensions of the analysis and phenomena to the thin-film regime, which is critical for fuel-cell catalyst layers, will also be presented. Acknowledgements We thank Steve Hamrock, Mike Yandrasits, and Greg Haugen at 3M for providing membranes and discussion; This work made use of facilities at the Advanced Light Source (ALS), supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy. This work was funded by the Assistant Secretary for Energy Efficiency and Renewable Energy, Fuel Cell Technologies Office, of the U. S. Department of Energy under contract number DE-AC02-05CH11231. References 1 Gi Suk Hwang, Dilworth Y. Parkinson, Ahmet Kusoglu, Alastair A. MacDowell, and Adam Z. Weber, ACS Macro Letters, 2, 288-291 (2013). 2 Daniel S. Hussey, Dusan Spernjak, Adam Z. Weber, Rangachary Mukundan, Joseph Fairweather, Eric L. Brosha, John Davey, Jacob S. Spendelow, David L. Jacobson, and Rodney L. Borup, J. Appl. Phys., 112, 104906 (2012). 3 Ahmet Kusoglu, Suchol Savagatrup, Kyle T. Clark, and Adam Z. Weber, Macromolecules, 45 (18), 7467-7476 (2012). 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)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.052
GPT teacher head0.221
Teacher spread0.169 · 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
Published2015
Admission routes1
Has abstractyes

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