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Record W2328313701 · doi:10.1021/jp204848p

Heat-Treated Nonprecious Catalyst Using Fe and Nitrogen-Rich 2,3,7,8-Tetra(pyridin-2-yl)pyrazino[2,3-<i>g</i>]quinoxaline Coordinated Complex for Oxygen Reduction Reaction in PEM Fuel Cells

2011· article· en· W2328313701 on OpenAlexaff
Jason Wu, Wenmu Li, Drew Higgins, Zhongwei Chen

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2011
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCatalysisX-ray photoelectron spectroscopyQuinoxalinePyrolysisChemistryNitrogenInorganic chemistryNuclear chemistryCarbideChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Pyrolyzed Fe/N/C catalysts were synthesized using a newly designed and synthesized 2,3,7,8-tetra(pyridin-2-yl)pyrazino[2,3- g ]quinoxaline (TPPQ) organic compound as the nitrogen-containing ligand. The structure of TPPQ was deliberately designed to discourage the agglomeration of Fe during heat treatment as well as to provide a concentrated source of nitrogen. Catalysts were prepared by first coordinating TPPQ with Fe, forming Fe–TPPQ complexes, followed by impregnation onto carbon black (KJ600) and pyrolysis at 900 °C. Catalysts with 0.5%, 1%, 2%, 4%, and 8% initial iron content were prepared, and their physical characteristics were determined by X-ray diffraction, transmission electron microscopy, and X-ray photoelectron spectroscopy analysis. Electrocatalytic activity toward the oxygen reduction reaction was evaluated and compared for all catalysts. The best performing catalyst was found to be the catalyst using 2% initial iron content. Evidence of iron metal and carbide particle formation was found for catalysts with initial iron content higher than 2%.

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 categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.243
Teacher spread0.218 · 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.

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

Citations47
Published2011
Admission routes1
Has abstractyes

Explore more

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