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Record W4235731707 · doi:10.1149/ma2014-02/21/1225

Measurements of Permeability and Effective in-Plane Gas Diffusivity of Gas Diffusion Media Under Compression

2014· article· en· W4235731707 on OpenAlexaff
Prafful Mangal, Mark Dumontier, Nicholas B. Carrigy, Marc Secanell

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsThermal diffusivityKnudsen diffusionMolecular diffusionPorous mediumGaseous diffusionMass diffusivityOxygen transportPermeability (electromagnetism)Limiting currentMaterials scienceDiffusionThermodynamicsChemistryConvectionOxygenMechanicsAnalytical Chemistry (journal)Composite materialPorosityMembraneChromatographyElectrode

Abstract

fetched live from OpenAlex

Mass transport is one of the key factors limiting the performance of polymer electrolyte membrane fuel cells (PEMFC). Mass transport in the gas diffusion layers occurs mainly by molecular diffusion. However, convection might also become important under some circumstances. For example, in serpentine fuel cells, convection might be important at the channel bends [1]. Knudsen diffusion might also have a small role for cases where, due to high PTFE loading and high compression, the average pore size in the GDL is reduced. An experimental set up based on a diffusion bridge has been developed to determine the in-plane permeability and effective molecular diffusivity of gas diffusion layers used in PEMFC both uncompressed and under compression. In order to estimate in-plane permeability, nitrogen is introduced in one channel and passed through the porous sample and the pressure drop is measured. In order to measure in-plane diffusivity, nitrogen and oxygen are passed in separate channels connected only by the porous media. The oxygen concentration is measured in the nitrogen channel using an oxygen sensor. Further, by applying a pressure differential between the channels the ratio of convection and diffusion is modified. In order to estimate in-plane permeability and effective molecular diffusivity from the experiments, several one-dimensional mass transport models are implemented. In this study, a combined Fick’s and Darcy’s model, and the modified binary friction models are implemented and used to estimate the permeability and effective oxygen diffusivity [2]. The experimentally measured pressure drop and the oxygen concentration at different flow rates and differential pressures are used to estimate the in-plane permeability and oxygen effective diffusivity using least square parameter estimation. Using the procedure described above, the effect of compression and PTFE loading on in-plane permeability and effective molecular diffusivity are studied. GDL Toray 090 samples with different PTFE loading, i.e. 0, 10, 20 and 40%, are tested at 4 different compression levels corresponding to a thickness of 250, 225, 200 and 175 μm. Results show that in-plane permeability varies in the range of 2.33 × 10−11 - 0.2 × 10−11 m2 , and in-plane diffusibility (ratio of effective diffusivity to molecular diffusivity) varies in the range of 0.72 – 0.18 for the selected range of compression and GDL samples. Figure 1 shows the GDL permeability for the samples with different PTFE loading at different levels of compression. Figure 2 shows the combined Fick’s and Darcy’s law model fit into the experimental data for a Toray 090 GDL sample at various levels of differential pressure between the oxygen and nitrogen channel at different levels of compression. Figure 3 shows the diffusibility for samples with different PTFE loading at different levels of compression. Results show that in-plane permeability reduces with compression and amount of PTFE in porous media while in-plane diffusivity decreases with compression due to decreasing porosity and decreases with increasing PTFE content. References [1] J. Phroah, Journal of Power Sources, 144, pp. 7782, (2005). [2] L. M. Pant, S. K. Mitra, M. Secanell, International Journal of Heat and Mass Transfer, 58, pp. 70–79, (2013).

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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