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Record W1998381902 · doi:10.1115/fuelcell2012-91301

Development of Radial Flow Field for Improved Water and Gas Management in PEMFC

2012· article· en· W1998381902 on OpenAlexafffund
Brooks R. Friess, Mina Hoorfar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProton exchange membrane fuel cellCathodeMaterials scienceFlow (mathematics)Pressure dropMechanicsCurrent densityGraphiteElectrical conductorElectrical engineeringComposite materialEngineeringFuel cellsChemical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents an innovative radial flow field design for PEMFC cathode flow plates. This new design, which is in the form of a radial field, replaces the standard rectangular flow channels in exchange for a set of flow control rings. The control rings allow for better flow distribution and use of the active area. The radial flow field was constructed with aluminum and plated with gold for superior surface and conductive properties. These materials were selected based on the results obtained from the performance of the standard flow channels of serpentine and parallel constructed of hydrophilic gold and hydrophobic graphite materials. The innovative radial flow field produces the largest limiting current density compared to other channel designs, even the serpentine flow field. The water removal and mass transport capacity of the radial flow field was proven to be better than parallel and serpentine. This performance increase was achieved while maintaining the pressure drop nearly half of the pressure drop measured in the serpentine flow fields.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.001
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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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".

Quick stats

Citations1
Published2012
Admission routes2
Has abstractyes

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