MétaCan
Menu
Back to cohort
Record W2321306880 · doi:10.1149/1.3484627

Effects of Water Vapor Proportion in Generated Water on Performance of Polymer Electrolyte Fuel Cells under Dry Operating Conditions

2010· article· en· W2321306880 on OpenAlexaff
Xu Zhang, Guilin Hu, Qianpu Wang, Datong Song, Zhongsheng Liu

Bibliographic record

VenueECS Transactions · 2010
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNational Research Council CanadaBC Innovation Council
Fundersnot available
KeywordsWater vaporProton exchange membrane fuel cellRelative humidityElectrolyteAnodeMaterials scienceIsothermal processWater transportCathodeHumidityDry gasChemical engineeringCurrent densityAnalytical Chemistry (journal)ChemistryWater flowCatalysisThermodynamicsElectrodeChromatographyEnvironmental engineeringEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

A one-dimensional, non-isothermal, single-phase, steady-state model is developed for dry operating conditions to investigate the effects of different types of generated water as liquid or vapor in electrochemical reaction on the proton exchange membrane fuel cell (PEMFC) performance, A parameter (Γ) denoting vapor phase fraction in generated water is employed in the model. The agglomerate model with thin film was employed in catalyst layers. Water transport and its effects on cell performance were discussed under different operating conditions such as reactants relative humidity, vapor phase fraction and micro-porous layer thickness. The results indicated that the net water transport coefficients were positive under equal feed gas humidity in both anode and cathode. At high current density, the more the proportion of water vapor in generated water is, the better cell performance is. However, at low current density, the effect of vapor phase proportion on cell performance is not very obvious.

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 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 score0.137
Threshold uncertainty score0.481

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)0.0000.000

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 head0.003
GPT teacher head0.178
Teacher spread0.175 · 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 teacher head, 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

Citations2
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueECS TransactionsSame topicFuel Cells and Related MaterialsFrench-language works237,207