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Record W2030932363 · doi:10.1021/jp057135h

Heat-Treated Fe/N/C Catalysts for O<sub>2</sub> Electroreduction:  Are Active Sites Hosted in Micropores?

2006· article· en· W2030932363 on OpenAlexaff
Frédéric Jaouen, Michel Lefèvre, Jean‐Pol Dodelet, Mei Cai

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2006
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsCatalysisCarbon blackCarbon fibersMicroporous materialInorganic chemistryElectrochemistryNitrogenAmmoniaNoble metalChemistryOxygenAmmonia productionOxygen reduction reactionMaterials scienceChemical engineeringPhysical chemistryOrganic chemistryElectrodeComposite number

Abstract

fetched live from OpenAlex

Limited availability of platinum is a potential threat to fuel cell commercialization. Since the 1970s, alternative catalysts to the electrochemical reduction of oxygen have been obtained from heat treatment at T > 600 degrees C of carbon with a non-noble metal and a source of nitrogen atoms. However, the process by which the heat treatment activates these materials remains an open question. Here, we report that the activation process of carbon black and iron acetate heat-treated in NH(3) comprises three consecutive steps: (i) incorporation of nitrogen atoms in the carbon, (ii) micropore formation through reaction between carbon and ammonia, and (iii) completion of active sites in the micropores by reaction of iron with ammonia. Step (ii) is the slowest. Moreover, the microporous surface per mass of catalyst controls the macroscopic activity when enough nitrogen atoms are incorporated in the structure of the carbon support. These facts should help in determining the structure of the active sites and in identifying methods to increase the site density of such catalysts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations613
Published2006
Admission routes1
Has abstractyes

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