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Record W2329003779 · doi:10.1149/1.2356155

64-Electrode PEM Fuel Cell Studies of CO-Tolerant Hydrogen Oxidation Catalysts

2006· article· en· W2329003779 on OpenAlexafffund
David A. Stevens, Joshua Rouleau, R. E. Mar, Radoslav Atanasoski, Alison Schmoeckel, Mark K. Debe, J. R. Dahn

Bibliographic record

VenueECS Transactions · 2006
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie University
KeywordsOverpotentialProton exchange membrane fuel cellRutheniumHydrogenCatalysisInorganic chemistryHydrogen productionMolybdenumChemistryMaterials scienceElectrodeElectrochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The hydrogen oxidation catalytic performance of a ternary composition spread (Pt1-xRux)1-yMoy sample has been determined in a 64-electrode proton exchange membrane fuel cell. Linear gradients of the three elements were sputter deposited onto 3M's nano-structured thin film support through a shadow mask to produce 64 different, electrically isolated, catalyst compositions. CO stripping voltammetry showed that pre-adsorbed CO was removed at lower potentials as the ruthenium content increased. The presence of Mo generated a redox couple, broadened the CO stripping peak and reduced its intensity. In the presence of CO in reformate, the hydrogen oxidation overpotential decreased as the ruthenium content increased, in agreement with previous experimental studies reported in the literature on Pt1-xRux binary alloys. At intermediate ruthenium content, the overpotential could be further reduced through the incorporation of some molybdenum. At high Ru/Mo content, the hydrogen oxidation overpotential was high, presumably due to the resultant low Pt content.

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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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

Citations5
Published2006
Admission routes2
Has abstractyes

Explore more

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