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Record W1999073323 · doi:10.1149/1.2356141

Improvement of Heat-Treated Fe/N/C-Based Catalysts for Oxygen Reduction in PEM Fuel Cells By Using New Carbon Black Powders as Catalyst Supports

2006· article· en· W1999073323 on OpenAlexafffund
Stéphane Ruggeri, Jean‐Pol Dodelet

Bibliographic record

VenueECS Transactions · 2006
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersGeneral Motors of Canada
KeywordsCarbon blackCatalysisCarbon fibersOxygenAmmoniaMethaneAmmonia productionInorganic chemistryChemistryChemical engineeringMaterials scienceOrganic chemistryComposite materialComposite number

Abstract

fetched live from OpenAlex

Several new developmental carbon blacks were prepared at the Sid Richardson Carbon Black Corporation by injecting ammonia, methane or water vapor in the carbon black furnace during their fabrication. These new carbon powders were then used to obtain Fe/N/C catalysts for oxygen reduction reaction (ORR) in the acidic conditions prevailing in PEM fuel cells. The best catalysts were obtained when ammonia and methane were injected in the carbon black furnace during its production. The results are interpreted in terms of structural changes induced in the carbon blacks during their fabrication. These changes influence their subsequent reaction rate with NH3 when the structurally modified carbon black supports, loaded with 0.2 wt% Fe, are heat-treated at 950 degrees Celcius in pure ammonia to obtain the catalysts. This etching reaction produces up to ten times more catalytic sites with these modified carbon blacks than with their respective regular forms.

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 score0.378
Threshold uncertainty score0.992

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 head0.198
Teacher spread0.193 · 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

Citations2
Published2006
Admission routes2
Has abstractyes

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