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Record W2986745945 · doi:10.3390/coatings9110746

Influence of Substrate Shape and Roughness on Coating Microstructure in Suspension Plasma Spray

2019· article· en· W2986745945 on OpenAlex

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

VenueCoatings · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceMicrostructureCoatingScanning electron microscopeYttria-stabilized zirconiaComposite materialSubstrate (aquarium)Suspension (topology)Thermal sprayingCubic zirconiaSurface finishSurface roughnessDeposition (geology)Ceramic

Abstract

fetched live from OpenAlex

This study investigated the influence of the substrate shape and roughness on the microstructure of suspension plasma spray (SPS) coatings. For this purpose, an yttria-stabilized zirconia (YSZ) suspension was sprayed on flat and curved stainless-steel substrates by SPS. The suspension was composed of 20 wt.% YSZ particles in ethanol. After spraying, the morphology of the coatings was characterized by scanning electron microscopy (SEM). The results showed that the substrate shape influences the amount of coating material deposited and microstructural features of the coating. The amount of coating material deposited was seen to decrease as the radius of curvature decreased. Finally, the roughness was found to influence the formation of columnar structure.

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 score0.150
Threshold uncertainty score0.899

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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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