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Record W2128929764 · doi:10.1149/2.007301jes

On the Determination of PEM Fuel Cell Catalyst Layer Resistance from Impedance Measurement in H<sub>2</sub>/N<sub>2</sub>Cells

2012· article· en· W2128929764 on OpenAlexaff
Dzmitry Malevich, Barath Ram Jayasankar, Ela Halliop, Jon G. Pharoah, Brant A. Peppley, Kunal Karan

Bibliographic record

VenueJournal of The Electrochemical Society · 2012
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsQueen's University
Fundersnot available
KeywordsNyquist plotElectrical impedanceCapacitanceMaterials scienceDouble-layer capacitanceCharacteristic impedanceAgglomerateEquivalent circuitProton exchange membrane fuel cellAnalytical Chemistry (journal)ChemistryComposite materialVoltageCatalysisDielectric spectroscopyElectrical engineeringElectrochemistryElectrodeChromatography

Abstract

fetched live from OpenAlex

The commonly employed the H2/N2 cell impedance method for the determination of PEMFC catalyst layer ionic resistance often results in significant deviations from the predicted idealized homogeneous catalyst layer properties. In this study, the effect of the distribution of resistance and capacitance within the thickness of the CL is modeled to examine whether it explains the observed deviations. It is found that uniformly and non-uniformly distributed CLs show limiting real impedance (vertical line on Nyquist plot) at low frequency, as long as the impedance has no faradaic contribution. However, using this value of real impedance for obtaining the ionic resistance of the catalyst layer leads to significant errors if the system is non-uniformly distributed. It is shown that Nyquist plots with non-vertical mid-frequency regions, resembling experimentally measured H2/N2 cell response, can be generated with a "nested" transmission line circuit (agglomerate model) if there is a significant difference in resistance in different agglomerates.

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 score0.005
Threshold uncertainty score0.532

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 integrity0.0000.001
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 head0.008
GPT teacher head0.185
Teacher spread0.177 · 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

Citations60
Published2012
Admission routes1
Has abstractyes

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