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Record W2808197342 · doi:10.1149/2.1141809jes

The Effect of Fuel Electrode Roughness on the Properties of Plasma Sprayed Solid Oxide Cells

2018· article· en· W2808197342 on OpenAlexafffund
Joel Kuhn, Mohit Gupta, Olivera Kesler, Sebastian Björklund

Bibliographic record

VenueJournal of The Electrochemical Society · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrodeMaterials scienceElectrolyteSurface roughnessDeposition (geology)Atmospheric pressureAtmospheric-pressure plasmaAnodeOxideAnalytical Chemistry (journal)Composite materialPlasmaMetallurgyChemistry

Abstract

fetched live from OpenAlex

Solid oxide cell electrolytes fabricated by atmospheric plasma spraying are frequently found to have considerable gas leak rates. Electrode surface roughness is known to have an influence on electrolyte leak rates. A jet of high velocity air, produced with an air knife, was aimed at the plasma plume during fuel electrode deposition to reduce the surface roughness prior to electrolyte deposition. The resulting fuel electrode masses, electrode compositions, and electrode surface roughnesses were measured for varying air knife inlet pressures. Surface asperity populations and maximum heights were significantly reduced using air knife pressures of >6 bar at the expense of deposition efficiency. The nickel volume fraction in the fuel electrode increased slightly with increasing air knife pressure. Open circuit voltages were larger on the smoother fuel electrodes that were produced at higher air knife pressures, but no significant effect of air knife pressure on cell power density could be discerned.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.242
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Electrochemical SocietySame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207