MétaCan
Menu
Back to cohort
Record W4379932556 · doi:10.3389/fenrg.2023.1200603

Measuring the through-plane and in-plane oxygen apparent diffusion coefficients in the gas diffusion layer

2023· article· en· W4379932556 on OpenAlexaff
Keguang Yao, Tiehan Long, Yajun Wang, Xiao‐Zi Yuan, Yao Yao, Haijiang Wang

Bibliographic record

VenueFrontiers in Energy Research · 2023
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDiffusionOxygenFlux (metallurgy)Plane (geometry)Volumetric flow rateOxygen transportMechanicsTorqueGaseous diffusionChemistryMass fluxLimiting oxygen concentrationMaterials scienceFlow (mathematics)Analytical Chemistry (journal)ThermodynamicsPhysicsGeometryChromatographyMathematics

Abstract

fetched live from OpenAlex

Accurately predicting oxygen mass transport resistance and current distribution in fuel cells requires significant knowledge of oxygen diffusion coefficients in both in-plane and through-plane directions. However, there are few methods for measuring in-plane oxygen diffusion coefficients, which are important parameters for simulating oxygen flux distribution in gas diffusion layers (GDLs). In this study, we establish measurement methods and calculations for both in-plane and through-plane oxygen diffusion coefficients. Using in-house designed cells, we measure the in-plane and through-plane oxygen apparent diffusion coefficients of a commercial carbon paper (AvCarb EP40) at various torques and gas flow rates. We also simulate oxygen flux distributions in the GDL under each torque and gas flow rate. Our results show that the channel part is the major contributor to total oxygen flux at high torque conditions and that an increase in torque leads to a decrease in the contribution from the land part. Simulation results also suggest that a higher gas flow rate and lower torque contribute to a more uniform distribution of oxygen flux in 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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.038
GPT teacher head0.266
Teacher spread0.228 · 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 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Energy ResearchSame topicFuel Cells and Related MaterialsFrench-language works237,207