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Record W2044505144 · doi:10.1115/1.2971044

Effect of Processing Conditions on Curvature of Anode/Electrolyte SOFC Half-Cells Fabricated by Electrophoretic Deposition

2009· article· en· W2044505144 on OpenAlexaff
Michael Lankin, Kunal Karan

Bibliographic record

VenueJournal of Fuel Cell Science and Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicElectrophoretic Deposition in Materials Science
Canadian institutionsQueen's University
Fundersnot available
KeywordsAnodeMaterials scienceNon-blocking I/OElectrolyteElectrophoretic depositionSinteringYttria-stabilized zirconiaChemical engineeringOxideConductivitySubstrate (aquarium)Layer (electronics)Composite materialMetallurgyCubic zirconiaChemistryCoatingElectrodeCeramicOrganic chemistry

Abstract

fetched live from OpenAlex

Thin-electrolyte anode-supported solid oxide fuel cells (YSZ/NiO–YSZ) were fabricated for intermediate-temperature operation using electrophoretic deposition (EPD). During cosintering, the half-cells were observed to warp—an undesirable characteristic—due to mismatch in the sintering rates. The influence of the temperature for anode presintering—a key processing step—on the curvature of the half-cells induced by sintering was investigated over 700–1400°C. It was found that the maximum curvature occurred for an anode presintered at 900°C, while the minimum was observed at 1200°C. Anode presintering temperature was also found to affect the rate of electrophoretic deposition. At low presintering temperatures, the rate of EPD increased due to the enhancement in substrate (anode) electronic conductivity as a result of an increased percolating network of NiO. Further increases in presintering temperature, however, resulted in a decrease in the EPD rate due to the formation of a surface layer with poor electronic conductivity as a result of NiO diffusion from the NiO-YSZ anode to the sintering crucible.

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)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.001
GPT teacher head0.199
Teacher spread0.197 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

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Same venueJournal of Fuel Cell Science and TechnologySame topicElectrophoretic Deposition in Materials ScienceFrench-language works237,207