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Record W2052389553 · doi:10.1021/jp065923g

Characterization of Pt Nanoparticles Deposited onto Carbon Nanotubes Grown on Carbon Paper and Evaluation of This Electrode for the Reduction of Oxygen

2006· article· en· W2052389553 on OpenAlexafffund
Dominique Villers, Shuhui Sun, Alessandra Maria Serventi, Jean‐Pol Dodelet, S. Désilets

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2006
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCarbon nanotubeCarbon fibersOxygen reductionCharacterization (materials science)NanoparticleElectrodeMaterials scienceOxygen reduction reactionOxygenReduction (mathematics)NanotechnologyChemical engineeringClark electrodeChemistryElectrochemistryComposite materialOrganic chemistryEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Multiwalled carbon nanotubes (MWCNTs) were grown on the fibers of a commercial porous carbon paper used as carbon-collecting electrodes in fuel cells. The tubes were then covered with Pt nanoparticles in order to test these gas diffusion electrodes (GDEs) for oxygen reduction in H2SO4 solution and in H2/O2 fuel cells. The Pt nanoparticles were characterized by cyclic voltammetry, transmission electron microscopy, and X-ray photoelectron spectroscopy. The majority of the Pt particles are 3 nm in size with a mean size of 4.1 nm. They have an electrochemically active surface area of 60 m2/g Pt for Pt loadings of 0.1-0.45 mg Pt/cm2. Although the electroactive Pt surface area is larger for commercial electrodes of similar loadings, Pt/MWCNT electrodes largely outperform the commercial electrode for the oxygen reduction reaction in GDE experiments using H2SO4 at pH 1. On the other hand, when the same electrodes are used as the cathode in a H2/O2 fuel cell, they perform only slightly better than the commercial electrodes in the potential range going from approximately 0.9 to approximately 0.7 V and have a lower performance at lower voltages.

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 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 score0.017
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.007
GPT teacher head0.204
Teacher spread0.198 · 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

Citations104
Published2006
Admission routes2
Has abstractyes

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