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Record W2562675815 · doi:10.1080/02670844.2016.1259731

Microstructure evaluation of CO-222/SiC coating produced by the plasma spraying method

2016· article· en· W2562675815 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

VenueSurface Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsÉcole de Technologie SupérieureUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceMicrostructureIndentation hardnessCoatingAlloyThermal sprayingScanning electron microscopeGas dynamic cold sprayComposite numberMetallurgyPhase (matter)Silicon carbideAtmospheric-pressure plasmaOxideCarbideComposite materialPlasma

Abstract

fetched live from OpenAlex

A composite coating containing 10 wt-% of SiC and 90 wt-% CO-222 super alloy was coated with atmospheric plasma spray at two different currents on the surface of the 304 stainless steel. The aim of this research is to address the microstructure and phase formation during the plasma spray of Co-222/SiC composite coatings. The phase analysis, microstructure, surface morphology and microhardness of Co-222/SiC feedstock powder and the coating were characterised by X-ray diffraction, field emission scanning electron microscopy and Vickers microhardness. The results demonstrated that the outer surface of SiC particles was decarburised and the SiO 2 phase was formed at the high temperature of plasma gun. Also, Cr 2 O 3 oxide was formed owing to the reaction of Cr from Co-222 super alloy with O 2 . By the formation of oxide phases and degradation of silicon carbide, the microhardness of the composite was reduced, as compared to CO-222 super alloy.

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 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 score0.240
Threshold uncertainty score0.820

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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