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Record W2031737416 · doi:10.1002/elan.201100007

Electrocatalyst Supporting Properties of Carbon Sphere Chains

2011· article· en· W2031737416 on OpenAlexafffund
Zéhira Hamoudi, My Alı El Khakani, Mohamed Mohamedi

Bibliographic record

VenueElectroanalysis · 2011
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsElectrocatalystFerrocyanideCarbon fibersCatalysisElectrochemistryMaterials scienceMethanolChemical engineeringNanotechnologyRedoxElectrodeChemistryInorganic chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Solid carbon spheres chains (CSCs) are new carbon materials with unique physicochemical properties and original organization, they present a distinctive opportunity for creating novel multifunctional composites electrodes for a wide range of electrochemical applications. The electrocatalytic supporting of properties of these CSCs‐coated with a model catalyst that is Pt of different morphologies were studied for the oxidation of ferrocyanide to illustrate their use in electroanalytical applications, for the oxidation of methanol and the reduction of oxygen to exemplify their potential use in energy conversion devices such as fuel cells for instance. The very good catalytic supporting properties demonstrated here are due to the fact that CSCs possess multiple‐points of electrical connectivity and excellent dispersions characteristics for the catalyst that allow the latter to be largely accessible for electrocatalytic reactions.

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 score0.003
Threshold uncertainty score0.009

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.175
Teacher spread0.162 · 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

Citations9
Published2011
Admission routes2
Has abstractyes

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