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Record W4250233080 · doi:10.1149/ma2014-01/14/645

Impact of MPL Thickness on Water Management of PEMFC By Synchrotron X-ray Radiography

2014· article· en· W4250233080 on OpenAlexaffabout
Jongmin Lee, Ronnie Yip, Patrick Antonacci, Nan Ge, Toshikazu Kotaka, Yuichiro Tabuchi, Aimy Bazylak

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProton exchange membrane fuel cellMaterials scienceMicroporous materialSynchrotronComposite materialCatalysisOpticsChemistry

Abstract

fetched live from OpenAlex

The polymer electrolyte membrane fuel cell (PEMFC) is an efficient and clean energy conversion device. The performance of PEMFCs at high power operation is governed by product water management. The gas diffusion layer (GDL), a porous carbon fiber sheet, is sandwiched between a catalyst layer and a flow field, providing diffusive pathways for reactant gases and product water. A transient microporous layer (MPL) characterized by sub-micron size pores and hydrophobic contents applied on the GDL or catalyst layer is known to extend limiting current density. Past research [1] has shown that there exists an optimal thickness of MPL. However, the effect of varying MPL thickness has not been studied by visual inspection. Our research group has successfully investigated liquid water dynamic and distribution in an operating PEMFC using synchrotron radiography [2-3]. For this study, a miniature fuel cell with an active area of 0.48cm2and 0.2mm channel and rib width was designed for improved image quality. MPLs of varying thickness (no MPL, 50μm, 100μm, and 150μm) were coated on TGP-H-60 (Toray Industries Inc.). Each fuel cell was operated according to a pre-determined testing scheme on a Scribner 850e fuel cell testing station (Scribner Inc.). Visualizations were performed at the Biomedical Imaging and Therapy Bending Magnet (05B1-1) beamline at Canadian Light Source Inc. (Saskatoon, Canada). Spatial and temporal resolutions were approximately 10μm and 0.3fps, respectively. Image processing was carried out according to the Beer-Lambert law. The processed image provides information on liquid water profile across components of the fuel cell (Figures 1 and 2). For each types of GDL, liquid water content at the interfaces and within microstructures will be analyzed to isolate effect of MPL thickness on water distribution and performance of the fuel cell. References: 1. Jin Hyun Nam, Kyi-Jin Lee, Gi-Suk Hwang, Charn-Jung Kim, Massoud Kaviany, Int. J. Heat and Mass Transfer, 52, 2779-2791 (2009). 2. J. Lee, J. Hinebaugh, A. Bazylak, J. Power Source, 227, 123-130 (2013) 3. J. Hinebaugh, J. Lee, A. Bazylak, J. Electrochem. Society, 159 (12) F826-F830 (2012)

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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.005
GPT teacher head0.206
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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

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