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Record W2044929303 · doi:10.1021/jp036602x

Effect of Redox State of PtRu Electrocatalysts on Methanol Oxidation Activity

2003· article· en· W2044929303 on OpenAlexaff
Aislinn H. C. Sirk, Josephine M. Hill, Sherman Kung, Viola Birss

Bibliographic record

VenueThe Journal of Physical Chemistry B · 2003
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMethanolCatalysisRedoxCyclic voltammetryThermogravimetric analysisChemistryOxidation stateNafionDirect methanol fuel cellInorganic chemistryElectrocatalystAcetic acidElectrochemistryCatalytic oxidationMethanol fuelNuclear chemistryOrganic chemistryElectrodePhysical chemistryAnode

Abstract

fetched live from OpenAlex

PtRu is a promising catalyst for methanol oxidation in direct methanol fuel cells. However, the most active Pt:Ru ratio and oxidation state of the Ru component are still under investigation. PtRu black was obtained from Johnson Matthey, and the as-received catalyst was treated with either hydrogen or oxygen at elevated temperatures to alter the oxidation state. The samples were characterized by cyclic voltammetry (CV), thermogravimetric analysis (TGA), transmission electron microscopy (TEM), and X-ray diffraction (XRD) to confirm their redox states and for correlation with their methanol oxidation activity. All of the characterization techniques support successful oxidation and reduction of the PtRu catalyst. The methanol oxidation activity was measured, and the sequence at 25 °C was found to be reduced > as-received > oxidized ≫ strongly oxidized. The effect of the drying regime and the dispersing agent for the catalysts was also investigated, and it was found that samples supported using acetic acid were more active than those supported by Nafion, but were less stable and more susceptible to change in catalyst state from heat gun drying.

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 score0.007
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.237
Teacher spread0.233 · 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

Citations76
Published2003
Admission routes1
Has abstractyes

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