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Record W2312242649 · doi:10.1115/fuelcell2012-91084

Measurement of Liquid Water Content Inside the Gas Diffusion Layer

2012· article· en· W2312242649 on OpenAlexaff
Siddiq Hussain Tahseen, Kehan Chen, Mehdi Shahraeeni, Samuel C. M. Yew, Mina Hoorfar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsThermal diffusivityCapillary actionCapillary pressureDiffusionSaturation (graph theory)Materials sciencePermeability (electromagnetism)ConvectionVolume (thermodynamics)Gaseous diffusionMechanicsChemistryComposite materialAnalytical Chemistry (journal)Porous mediumChromatographyThermodynamicsPorosityMembrane

Abstract

fetched live from OpenAlex

The amount of the liquid water present at the gas diffusion layer (GDL) has an impact on the diffusivity, capillary pressure and the permeability which in turn influences convective and diffusive transport. A prodigious amount of research has been conducted to study and measure the different properties (time of breakthrough and capillary pressure versus saturation) associated with the breakthrough condition. However, most of the reported data ignored the impact of expansion of different components in the set-up (such as tubing) and the condition after the time of breakthrough. The focus of this study is to measure the breakthrough pressure and time of breakthrough and hence determine the liquid water content inside the GDL before the time of breakthrough. The measurements are performed for different samples to study the effect of the thickness and hydrophobic contents. The results show that expansion has significant difference in the determination of water volume inside the GDL.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.194
Teacher spread0.154 · 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 routes1
Has abstractyes

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