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Record W2324632967 · doi:10.1149/1.3210563

Iron-based Catalysts for Oxygen Reduction in PEM Fuel Cells: Expanded Study Using the Pore-filling Method

2009· article· en· W2324632967 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 · 2009
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroporous materialCatalysisCarbon blackProton exchange membrane fuel cellCarbon fibersOxygenChemical engineeringMaterials scienceOxygen reduction reactionNitrogenFiller (materials)ChemistryInorganic chemistryComposite materialOrganic chemistryElectrochemistryPhysical chemistryNatural rubber

Abstract

fetched live from OpenAlex

Fe/N/C catalysts for the oxygen reduction reaction (ORR) in PEM fuel cells were synthesized via a new method. A catalyst precursor was first prepared by filling the pores of a microporous carbon black with a mixture of pore-filler (organic molecules) and iron precursor by means of planetary ballmilling. The resulting catalyst precursor was then pyrolysed either in NH3 only or, first in Ar and then in NH3. Various pore-fillers and carbon blacks were investigated. The ORR activity in fuel cell is influenced by (i) the type and mass ratio of the pore-filler and the nominal loading of iron in the catalyst precursor and (ii): the micropore surface area and nitrogen content in the catalyst. The highest kinetic activity obtained in fuel cell at 0.8 V iR-free is 429 A g-1 using 1 wt% nominal iron loading, phenanthroline as the pore-filler and Black Pearls 2000 as the microporous carbon black

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 categoriesnone
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 score0.493
Threshold uncertainty score0.495

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 head0.021
GPT teacher head0.272
Teacher spread0.250 · 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