MétaCan
Menu
Back to cohort
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 OpenAlex
Xu Zhang, Datong Song, Qianpu Wang, Cheng Huang, Zhongsheng Liu, A.A. Shah

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

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.

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)0.001
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 score0.012
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.005
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