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Record W1973549990 · doi:10.1021/jp809987g

Pt/Carbon Catalyst Layer Microstructural Effects on Measured and Predicted Tafel Slopes for the Oxygen Reduction Reaction

2009· article· en· W1973549990 on OpenAlexafffund
Dustin Banham, Jeff N. Soderberg, Viola Birss

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2009
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTafel equationElectrocatalystCatalysisPorosityNafionOhmic contactExchange current densityMaterials scienceChemistryElectrolysisElectrolysis of waterChemical engineeringLayer (electronics)Inorganic chemistryComposite materialElectrodeElectrochemistryElectrolytePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The important effect of the physical and morphological properties of porous cathodes on the oxygen reduction reaction (ORR) Tafel slope, which is normally viewed as a mechanistic parameter, was investigated using a variety of Pt/carbon (Nafion) catalyst layers in room temperature O 2 -saturated sulfuric acid solutions. Consistent with previous theoretical predictions, it was found experimentally that increasing the pore length, the catalyst layer resistance, and the exchange current density, and decreasing the pore diameter, all serve to cause the Tafel slope to be larger than its mechanistically predicted value, thus leading to performance loss. Theoretical Tafel slopes, calculated using a model for the migration-induced distribution of potentials in an electroactive porous layer and employing the catalyst layer properties examined experimentally, showed very good agreement with the measured Tafel slopes. These results reveal the importance of minimizing the ohmic resistance of porous electrocatalyst layers, of relevance in a wide range of applications, e.g., in fuel cells, electrolysis processes, batteries, etc.

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 score0.011
Threshold uncertainty score0.397

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 head0.220
Teacher spread0.214 · 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

Citations70
Published2009
Admission routes2
Has abstractyes

Explore more

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