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Record W2135002001 · doi:10.1115/1.3177448

Numerical Analysis of Water Transport Through the Membrane Electrolyte Assembly of a Polymer Exchange Membrane Fuel Cell

2010· article· en· W2135002001 on OpenAlexaff
Xu Zhang, Datong Song, Qianpu Wang, Cheng Huang, Zhongsheng Liu, A.A. Shah

Bibliographic record

VenueJournal of Fuel Cell Science and Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsBC Innovation CouncilNational Research Council Canada
Fundersnot available
KeywordsWater transportElectrolyteProton exchange membrane fuel cellAnodeMembraneCathodeChemistryChemical engineeringWater flowMaterials scienceEnvironmental engineeringElectrodeEnvironmental science

Abstract

fetched live from OpenAlex

The effects of water transport through membrane electrolyte assembly of a polymer exchange membrane fuel cell on cell performance has been studied by a one-dimensional, nonisothermal, steady-state model. Three forms of water are considered in the model: dissolved water in the electrolyte or membrane, and liquid water and water vapor in the void space. Phase changes among these three forms of water are included based on the corresponding local equilibriums between the two involved forms. Water transport and its effect on cell performance have been discussed under different operating conditions by using the value and the sign of the net water transport coefficient, which is defined by the net flux of water transported from the anode side to the cathode side per proton flux. Optimal cell performance can be obtained by adjusting the liquid water saturation at the interface of the cathode gas diffusion layer and flow channels.

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)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.201
Teacher spread0.197 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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