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Record W2519716895 · doi:10.1149/07514.0237ecst

Performance Benefits of Multiwall Carbon Nanotubes in the Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layer

2016· article· en· W2519716895 on OpenAlexaff
Jongmin Lee, Rupak Banerjee, Nan Ge, Stéphane Chevalier, Michael G. George, Hang Liu, Pranay Shrestha, Daniel Muirhead, James Hinebaugh, Aimy Bazylak

Bibliographic record

VenueECS Transactions · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProton exchange membrane fuel cellMicroporous materialElectrolyteCarbon nanotubeChemical engineeringMaterials scienceGaseous diffusionPolymerSaturation (graph theory)CathodeWater transportOxygenPorosityMembraneComposite materialChemistryFuel cellsElectrodeOrganic chemistryEnvironmental engineeringWater flow

Abstract

fetched live from OpenAlex

A commercial gas diffusion layer (GDL) with a microporous layer (MPL) containing multiwall carbon nanotubes (MWCNT) exhibited better performance in a polymer electrolyte membrane (PEM) fuel cell than that with a conventional GDL. This performance benefit was attributed to improved oxygen mass transport in the cathode GDL. The operation of the fuel cell was visualized with synchrotron X-ray radiography to measure the liquid water saturation in the two GDLs. A higher liquid water saturation was measured in the operating fuel with the MWCNT-based MPL than with the conventional MPL. But, the MWCNTs induced higher effective porosity within the GDL, allowing for improved overall oxygen transport, and thus the cell performed better even with higher saturation of liquid water 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.173
Teacher spread0.167 · 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 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
Published2016
Admission routes1
Has abstractyes

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