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Record W2748694238 · doi:10.1149/2.0861712jes

Multiwall Carbon Nanotube-Based Microporous Layers for Polymer Electrolyte Membrane Fuel Cells

2017· article· en· W2748694238 on OpenAlexafffund
Jason Keonhag Lee, Rupak Banerjee, Michael G. George, Daniel Muirhead, Pranay Shrestha, H. Liu, Nan Ge, Stéphane Chevalier, Aimy Bazylak

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroporous materialProton exchange membrane fuel cellCarbon nanotubeElectrolyteMaterials scienceChemical engineeringPorosityPolymerWater transportComposite materialChemistryFuel cellsElectrode

Abstract

fetched live from OpenAlex

The presence of multiwall carbon nanotubes (MWCNTs) corresponded to a dispersion of carbon black particles in the microporous layer (MPL) of the polymer electrolyte membrane (PEM) fuel cell. The gas diffusion layer (GDL) with a MWCNT-based MPL exhibited larger pores and a higher porosity compared to a conventional GDL, and less MPL intrusion into the GDL substrate was observed with the MWCNTs-based MPL. The GDLs were evaluated in operando in a fuel cell that was customized for concurrent liquid water visualization (synchrotron X-ray radiography) and electrochemical characterization. The MWCNT-based fuel cell exhibited higher power densities and lower mass transport resistances compared to the fuel cell with the conventional GDL; however, a higher liquid water saturation was observed for the MWCNT-based GDL. Although the liquid water saturation in the MWCNT-based GDL was higher, its higher effective porosity led to superior performance compared to the conventional fuel cell. The use of the MWCNTs-based MPL resulted in improved oxygen transport in the fuel cell, particularly at high current densities.

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 score0.013
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.200
Teacher spread0.194 · 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

Citations52
Published2017
Admission routes2
Has abstractyes

Explore more

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