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Record W2103064301 · doi:10.1002/fuce.200700037

Polyamide/Polystyrene Blend Compatibilisation by Montmorillonite Nanoclay and its Effect on Macroporosity of Gas Diffusion Layers for Proton Exchange Membrane Fuel Cells

2007· article· en· W2103064301 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFuel Cells · 2007
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials sciencePolystyreneProton exchange membrane fuel cellPorosityPolymerComposite materialPolyamideChemical engineeringMembraneGaseous diffusionPolymer chemistryFuel cellsChemistry

Abstract

fetched live from OpenAlex

Abstract This work deals with a new route to modify polymer blend morphology in order to improve the porosity of gas diffusion layers (GDLs) for proton exchange membrane fuel cells (PEMFCs). First, electrically conductive polymer‐based blends were carefully formulated using a twin‐screw extrusion process. Blend electrical conductivity was ensured by the addition of high specific surface area carbon black and synthetic graphite flakes. Final GDL porosity, in particular its macroporosity, was generated by melt blending polyamide 11 (PA11) matrix with polystyrene (PS) followed by PS extraction with tetrahydrofuran (THF) solvent at room temperature. In order to improve GDL porosity by the optimisation of PS dispersion in the PA11 matrix, PA11/PS blends were compatibilised by the addition of 2 wt.‐% of clay. It was observed that both macroporosity and pore size distribution were beneficially modified after blend compatibilisation. Final GDL conductivity of about 1.25 S cm –1 , a porosity of 53% and a specific pore surface area of 75 m 2 g –1 were achieved.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.208
Teacher spread0.202 · 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