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Record W2165282073 · doi:10.1002/fuce.200500205

Temperature Dependence of Oxygen Reduction Catalyzed by Cobalt Fluoro‐Phthalocyanine Adsorbed on a Graphite Electrode

2006· article· en· W2165282073 on OpenAlexafffund
Chaojie Song, Lei Zhang, Jiujun Zhang, David P. Wilkinson, R. Baker

Bibliographic record

VenueFuel Cells · 2006
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British ColumbiaBC Innovation CouncilNational Research Council Canada
FundersNational Research Council Canada
KeywordsElectrocatalystCatalysisCobaltCyclic voltammetryChemistryElectrochemistryInorganic chemistryAdsorptionPhthalocyanineMethanolElectrodeGraphitePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The graphite electrode surface onto which cobalt(II) 1,2,3,4,8,9,10,11,15,16,17,18,22,23,24,25‐hexadecafluoro‐29H,31H‐phthalo‐cyanine (abbreviated as CoIIHFPC) is adsorbed displays a strong electrocatalytic activity toward O2 reduction. The electrochemical response of surface adsorbed CoIIHFPC is investigated at different pHs and temperatures by cyclic voltammetry. The kinetics of the catalyzed O2 reduction at different temperatures, measured by cyclic voltammetric and rotating disk electrode methods, is analyzed and a corresponding reaction mechanism is proposed. A two‐electron/two‐proton process is found to be the dominating pathway for CoIIHFPC catalyzed O2 reduction. The increase in temperature, from 20 to 70 °C, enhances the reduction rate significantly. The presence of methanol has no effect on its catalytic activity towards O2 reduction. The implications of using this non‐noble electrocatalyst for the cathode reaction in low temperature fuel cells, including direct organic, metal‐air fuel cells, etc., are discussed in this paper.

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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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

Citations39
Published2006
Admission routes2
Has abstractyes

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