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Record W2795044380 · doi:10.1149/2.0271806jes

Correlation of Changes in Electrochemical and Structural Parameters due to Voltage Cycling Induced Degradation in PEM Fuel Cells

2018· article· en· W2795044380 on OpenAlexaff
Alexander Kneer, Jasna Janković, Darija Susac, Andreas Pütz, Nadja Wagner, Mayank Sabharwal, Marc Secanell

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of AlbertaAutomotive Fuel Cell Cooperation (Canada)
Fundersnot available
KeywordsPlatinumProton exchange membrane fuel cellElectrochemistryMaterials scienceElectrodeTransmission electron microscopyDegradation (telecommunications)Chemical engineeringDissolutionCatalysisCurrent densityAnalytical Chemistry (journal)OxygenChemistryNanotechnologyElectronic engineeringChromatography

Abstract

fetched live from OpenAlex

Membrane electrode assemblies were degraded by voltage cycling in hydrogen/air atmosphere. The impact of degradation on fuel cell performance was measured by various electrochemical characterization techniques. Loss of electrochemically active surface area was correlated to kinetic voltage losses at low current density as well as losses at high current density due to oxygen transport limitations. It was found that the oxygen transport resistance scales proportionally to the inverse of normalized platinum surface area. The change in the catalyst layer structural properties due to voltage cycling was visualized by electron microscopy. A new method of calculating changes in platinum loading of degraded samples by transmission electron microscopy is presented and shows redistribution of platinum in the catalyst layer due to platinum dissolution.

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 score0.344

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 head0.211
Teacher spread0.204 · 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

Citations99
Published2018
Admission routes1
Has abstractyes

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