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Record W2244389138 · doi:10.1149/ma2014-01/11/566

Characterization of Micro-Porous Layer of Gas Diffusion Layer of a PEM Fuel Cell Via X-Ray Tomographic Microscopy

2014· article· en· W2244389138 on OpenAlexaff
Silvia Odaya, Ryan Philips, A.B. Phillion, Mina Hoorfar

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPorosityMaterials scienceLayer (electronics)Proton exchange membrane fuel cellCharacterization (materials science)Gaseous diffusionSurface finishChemical engineeringSurface roughnessComposite materialFuel cellsNanotechnology

Abstract

fetched live from OpenAlex

Abstract The gas diffusion layer (GDL) performs key functions within the fuel cell, providing access for hydrogen and oxygen gas to flow to and from the catalyst layer, conducting electricity and heat, and removing water byproduct. The GDL is commonly treated with a micro-porous layer (MPL) on the catalyst side to minimize contact resistance by reducing the roughness of the contact surface. The MPL also reduces mass transport losses and hence improves water management in the cell operated at high current densities [1]. This paper describes a method to characterize the GDL/MPL porous structure and their interface using X-ray micro-computed tomographic (µ-CT) microscopy. µ-CT is a relatively new tool that is used for acquiring the internal three-dimensional structure of both fully dense and porous materials [2]. In this work, the structure of the MPL/GDL is evaluated via µ-CT for various GDL samples (such as Toray TGP060 and EP40) loaded with different MPL loadings. Figure 1 shows a 3D image of the EP40 T12 GDL without the MPL (CBT 18). While previous studies have also examined the MPL layer using this technique (e.g. [1,3]), this is the first to examine the GDL/MPL interface, as well as the surface characteristics of the MPL. A new method is presented for separating, or segmenting, various phases (GDL, PTFE, and MPL). Using this methodology, the surface roughness of the MPL, and the porosity within the GDL is identified. The results show that there is considerable overlap between the GDL and MPL. Also, while the MPL has a smooth surface in comparison to the GDL, it remains relatively rough. The correlation between PTFE content, thickness of the MPL, and porosity will be discussed. References 1. Z. Fishman, A. Bazylak (2011). Journal of The Electrochemical Society, Vol. 158, B846-B851. 2. C. Puncreobutr, P.D. Lee, R.W. Hamilton, A.B. Phillion (2012). JOM, Vol. 64, pp:89-95. 3. A. Pfrang, S. Didas, G. Tsotridis (2013). Journal of Power Sources, Vol. 235, pp:81-86. Figures Figure 1: EP40 T12 GDL specimen without MPL (CBT18). The PTFE can be seen between the carbon strands.

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)0.000
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.191
Teacher spread0.186 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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