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Record W2144722104 · doi:10.1149/05801.1409ecst

Rationalizing Catalyst Inks for PEMFC Electrodes Based on Colloidal Interactions

2013· article· en· W2144722104 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
FundersCanada School of Energy and EnvironmentNatural Sciences and Engineering Research Council of CanadaEnergy Foundation
KeywordsDLVO theoryvan der Waals forceColloidChemical engineeringParticle (ecology)Materials scienceAqueous solutionDielectricPolymerElectrolyteChemistryElectrodePhysical chemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A preliminary kinetic model was developed for polymer electrolyte membrane fuel cell (PEMFC) catalyst inks in order to understand their particle stability. The Derjaguin Landau Verwey Overbeek (DLVO) model containing van der Waals attractive and electrostatic repulsive interaction energy was applied to the aqueous ink dispersions, while a modified DLVO type interaction containing a Coulombic term instead of the electrostatic term was applied to the non-aqueous dispersions. Solvents were compared based on their particle size distribution and stability ratios. Results show that the carbon black particles are stable in a higher dielectric medium whereas they tend to aggregate in a lower dielectric medium. A low ionic concentration for the aqueous medium also helped to improve the ink stability by providing a better electrostatic particle repulsion. Experiments conducted with ethyl acetate, iso-propanol and deionized water agree with the model predictions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.205
Teacher spread0.197 · 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