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Record W2016047245 · doi:10.1021/ie901711y

Two-Dimensional Property Distributions, Ohmic Losses, and Power Consumption within a Fuel Cell Polymer Electrolyte Membrane

2010· article· en· W2016047245 on OpenAlexaff
Venkateshwar Rao Devulapalli, Aaron Phoenix

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCathodic protectionAnodeElectrolyteCathodeOhmic contactMembraneMaterials sciencePolymerNafionMechanicsAnalytical Chemistry (journal)ChemistryComposite materialElectrochemistryChromatographyPhysicsPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

The average and localized water content, current, ohmic loss, and power consumption distributions within a Nafion 117 polymer electrolyte membrane (PEM) were modeled in two dimensions to determine the influence of the cathodic flowfield plate on the properties and performance of a fuel cell. The membrane model was decoupled from the cathode and anode by using fixed, assumed anodic boundary conditions and cathodic boundary conditions from literature indicative of operation at three channel:land configurations (1:1, 2:1, and 4:1) and three nominal cathode overpotentials (NCOs) (0.3, 0.5, and 0.7 V). The results suggested that a 1:1 configuration maintained the membrane in a more-hydrated state that was better able to moderate changes in ohmic loss and power consumption due to changes in the NCO. Detailed distributions within the membrane showed complex anisotropy, including the existence of localized maxima along anodic boundaries, cathodic boundaries, and even along both anodic and cathodic boundaries under the same operating conditions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

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 head0.023
GPT teacher head0.258
Teacher spread0.235 · 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 designSimulation or modeling
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
Published2010
Admission routes1
Has abstractyes

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