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Record W3081068307 · doi:10.1149/1945-7111/abb173

In-Plane Transport in Water Electrolyzer Porous Transport Layers with Through Pores

2020· article· en· W3081068307 on OpenAlexafffund
Pascal J. Kim, ChungHyuk Lee, Jason Keonhag Lee, Kieran F. Fahy, Aimy Bazylak

Bibliographic record

VenueJournal of The Electrochemical Society · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOverpotentialMass transportPorosityElectrochemistryMass transferWater transportChemistryPorous mediumSynchrotronChemical engineeringPlane (geometry)Materials scienceElectrodeChromatographyWater flowPhysical chemistryOpticsGeometryOrganic chemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

The mass transport in porous transport layers (PTLs) with pores exclusively in the through-plane direction was investigated using concurrent in operando X-ray radiography and electrochemical performance analysis. We observed via synchrotron X-ray imaging that through pores situated under the lands are inaccessible to liquid water. We thereby observed the limited in-plane mass transport that takes place in PTLs with pores exclusively in the through-plane direction. Additionally, a higher content of product gas was observed with the use of the PTL with through pores under both the channels and the lands (PTL Ch,L ) when compared to the PTL with through pores only under the channels (PTL Ch ). This oxygen gas accumulation behaviour corresponded to the higher mass transport overpotential of the PTL Ch,L compared to the PTL Ch . Finally, the limited in-plane mass transport in the PTL with through pores led to a relatively dehydrated catalyst layer, which was exhibited through higher ionic resistances as a function of increasing current density.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.003
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.177
Teacher spread0.172 · 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 teacher head, 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".

Quick stats

Citations35
Published2020
Admission routes2
Has abstractyes

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