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Record W2587248781 · doi:10.1149/2.0891704jes

Key Considerations for High Current Fuel Cell Catalyst Testing in an Electrochemical Half-Cell

2017· article· en· W2587248781 on OpenAlexaff
Blaise A. Pinaud, Arman Bonakdarpour, Lius Daniel, Jonathan Sharman, David P. Wilkinson

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsElectrolyteLimiting currentCatalysisFuel cellsElectrochemistryLimitingElectrodeCurrent (fluid)Constant currentMaterials scienceProton exchange membrane fuel cellCurrent densityDrop (telecommunication)Electrochemical cellComputer scienceChemical engineeringChemistryNanotechnologyElectrical engineeringEngineeringMechanical engineeringTelecommunicationsOrganic chemistry

Abstract

fetched live from OpenAlex

An economical and novel half-cell approach for quick and precise measurement of oxygen reduction catalysts in various practical electrode formats, including GDE and bonded GDE/membrane layers is demonstrated. Fuel cell current densities (∼1 A/cm2) were achieved with the key design considerations clearly outlined, to highlight the challenges in developing such a test platform. Constant current polarizations with IR drop measurement at each step are found to provide a simple, reproducible method of measuring catalyst performance. An optimal electrolyte concentration of 1.0 M HClO4 balances the requirement of non-limiting H+ transport at high currents whilst minimizing the effect of electrolyte impurities, which reduce Pt activity. The half-cell used here can accurately provide the ORR activity of commercial products showing good agreement with measurements made in fuel cell hardware, but at a significantly lower testing cost. This half-cell approach can be used to characterize various types of GDEs and CCMs with different catalyst layers.

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)0.001
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 score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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

Citations107
Published2017
Admission routes1
Has abstractyes

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