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Record W2186007849 · doi:10.31399/asm.cp.itsc2007p0309

Manufacturing Solid Oxide Fuel Cells with an Axial-injection Plasma Spray System

2007· article· en· W2186007849 on OpenAlex
Zilong Tang, Alan Burgess, Olivera Kesler, Brian J. White, Nir Ben-Oved

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.

Bibliographic record

VenueThermal spray · 2007
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsBC Innovation CouncilUniversity of British Columbia
Fundersnot available
KeywordsMicrostructureMaterials scienceSolution precursor plasma sprayCathodeCoatingElectrolyteAnodeThermal sprayingSolid oxide fuel cellPlasmaPlasma electrolytic oxidationPorosityGas dynamic cold sprayOxideSinteringAtmospheric-pressure plasmaMetallurgyElectrodeChemical engineeringComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract Atmospheric plasma spraying has emerged as a cost-effective alternative to traditional sintering processes for solid oxide fuel cell (SOFC) manufacturing. However, the use of plasma spraying for SOFCs presents unique challenges, mainly due to the high porosity required for the electrodes and fully dense coatings required for the electrolytes. By using optimized spray conditions combined with appropriate feedstocks, SOFC electrolytes and electrodes with required composition and microstructure could be deposited with an axial plasma spray system. In this paper, the challenges for manufacturing SOFC anodes, electrolytes, and cathodes are addressed. The effects of plasma parameters and different feedstocks on coating microstructure are discussed, and examples of optimized coating microstructures are given.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.017
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.250
Teacher spread0.238 · 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