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Record W2736401642 · doi:10.1149/08008.0409ecst

Phosphoric Acid Distribution Patterns in High Temperature PEM Fuel Cells

2017· article· en· W2736401642 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueECS Transactions · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhosphoric acidProton exchange membrane fuel cellMaterials sciencePorosityStack (abstract data type)Chemical engineeringMembrane electrode assemblyLeaching (pedology)CatalysisDiffusionElectrolyteChemistryElectrodeComposite materialThermodynamicsMetallurgyOrganic chemistryEnvironmental science

Abstract

fetched live from OpenAlex

Phosphoric acid plays a major role in the high temperature PEM fuel cell (HT-PEMFC). Phosphoric acid leaching out of the gas diffusion layer (GDL) into the flow field and the exhaust water, however, corrodes the auxiliaries of the stack and complicates water recycling. The distribution of phosphoric acid inside the catalyst layer determines the extent of the triple-phase boundary and thus directly influences the performance of the cell. In this study, phosphoric acid injection experiments and pore network modeling (PNM) were carried out to explain the dependency of phosphoric acid distribution inside HT-PEMFC gas diffusion electrodes (GDE) on different network parameters. PNM was validated for GDEs and used to predict the percolation. We found that the micro-porous layer (MPL) dominates the flow behavior and prevents phosphoric acid from leaching into the GDL. Furthermore, the presence of cracks inside the MPL strongly influences the saturation inside the catalyst layer (CL).

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.

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 score0.124
Threshold uncertainty score0.849

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 integrity0.0000.000
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 head0.005
GPT teacher head0.185
Teacher spread0.180 · 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