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Record W4313825190 · doi:10.31399/asm.cp.itsc2012p0793

The Influence of Process Equipment on the Properties of Suspension Plasma Sprayed Yttria-Stabilized Zirconia Coatings

2012· article· en· W4313825190 on OpenAlexaff
Michael Marr, D. Waldbillig, Olivera Kesler

Bibliographic record

VenueThermal spray · 2012
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceYttria-stabilized zirconiaCubic zirconiaCoatingMicrostructureSuspension (topology)PorositySubstrate (aquarium)Plasma torchComposite materialMetallurgyThermal sprayingPlasmaCeramic

Abstract

fetched live from OpenAlex

Abstract Suspension plasma sprayed YSZ coatings were deposited at lab-scale and production-scale facilities to investigate the effect of process equipment on coating properties. The target application for these coatings is SOFC electrolytes, so dense microstructures with low permeabilities were desired. Both facilities had the same torch but different suspension feeding systems, torch robots and substrate holders. These differences meant that the lab facility had higher torch-substrate relative speeds compared to the production facility. When using porous stainless steel substrates with relatively smooth surfaces, permeabilities and microstructures were comparable for coatings from both facilities, and no segmentation cracks were observed. Coating permeability could be further reduced by increasing substrate temperatures during deposition or reducing suspension feed rates. On rougher substrates representative of SOFC cathodes, production facility coatings had higher permeabilities and more segmentation cracks compared to lab coatings. The increased cracking may be due to larger heat impulses with each torch pass at the production facility caused by its lower torch-substrate relative speed. This work highlights some of the challenges associated with scaling up the spray process from the lab to production.

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 score0.010
Threshold uncertainty score0.434

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 integrity0.0000.000
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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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