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Record W2284451712 · doi:10.1149/06801.1979ecst

Development of a SOFC Performance Model to Analyze the Powder to Power Performance of Electrode Microstructures

2015· article· en· W2284451712 on OpenAlex
Duncan Gawel, Jon G. Pharoah, Steven Beale

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsQueen's University
FundersCompute Canada
KeywordsTriple phase boundaryElectrodeMaterials scienceAnodePorositySolid oxide fuel cellElectrochemistryWork (physics)MicrostructureOxideVolume fractionCurrent densityPower densityPhase (matter)MechanicsComposite materialThermodynamicsPower (physics)ChemistryMetallurgyPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

A computational model that evaluates the triple phase boundary length, normalized effective transport coefficients, and charge production within solid oxide fuel cell electrode microstructures is described. Local charge production, within the electrode, is predicted by coupling the species transport equations with an expression for the electrochemical reaction rate. A particle placement model, employing the drop-and-roll algorithm, is used to generate the electrodes studied in this work. Parametric studies with the computational model are then preformed to investigate the influence of the anode porosity and solid volume fraction on charge production. It was found that charge production is jointly influenced by the reaction site (triple phase boundary) density, and ionic transport within the electrode structures. High current densities are observed in electrodes with low porosities and ionic volume fractions greater than 50%, for equal sized particles.

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.

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 score0.281
Threshold uncertainty score0.477

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 head0.021
GPT teacher head0.271
Teacher spread0.250 · 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