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Record W2014763929 · doi:10.1149/2.062201jes

Highly Durable Graphene Nanosheet Supported Iron Catalyst for Oxygen Reduction Reaction in PEM Fuel Cells

2011· article· en· W2014763929 on OpenAlexafffund
Ja‐Yeon Choi, Drew Higgins, Zhongwei Chen

Bibliographic record

VenueJournal of The Electrochemical Society · 2011
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrocatalystCatalysisGrapheneNanosheetMaterials scienceSelectivityChemical engineeringPyrolysisElectrochemistryInorganic chemistryChemistryNanotechnologyElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

A novel NPMC using pyrimidine-2,4,5,6-tetramine sulfuric acid hydrate (PTAm) as a nitrogen precursor and graphene nanosheets as catalyst supports was prepared and characterized. We investigate the effect of different pyrolysis temperatures on the catalysts' ORR activity along with detailed surface analysis to provide insight regarding the nature of the ORR active surface moieties. The NPMC sample heat treated at 800°C was found to display optimal ORR activity and H2O selectivity, specifically, an onset potential of 0.853 V vs RHE, a half-wave potential of 0.682 V vs RHE, and a H2O selectivity of ca. 99.9%. High stability through an accelerated durability testing (ADT) protocol was demonstrated and attributed to the high graphitic content of the catalyst support material. This novel NPMC demonstrates promising electrocatalyst activity and superior durability over commercial Pt/C catalyst for ORR under the studied conditions, rendering graphene nanosheets as an ideal replacement to traditional nanostructured carbon support materials.

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)0.001
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 score0.004
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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

Citations52
Published2011
Admission routes2
Has abstractyes

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