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Record W4312269364 · doi:10.31399/asm.cp.itsc2011p0581

Yttrium Silicate Coatings by Sol Precursor Plasma Spraying for Environmental Barrier Applications

2011· article· en· W4312269364 on OpenAlexaff
A. Quet, L. Bianchi, Émilien Darthout, F. Gitzhofer, J. L. Leluan, F. Rebillat

Bibliographic record

VenueThermal spray · 2011
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceAmorphous solidYttriumMicrostructureCrystallizationSilicateChemical engineeringCeramicPlasmaThermal sprayingThermal barrier coatingComposite materialMetallurgyCoatingOxideChemistry

Abstract

fetched live from OpenAlex

Abstract Yttrium silicates are among the candidates for protection of silicon-based ceramics in high temperature and moist environments due to chemical and mechanical compatibility with substrate, low volatility and moisture resistance. Here we reported on the development of yttrium silicate coatings by sol precursor plasma spraying. The use of a sol feedstock allowed easy composition flexibility. The microstructure and the structure of as-sprayed and heat-treated coatings were investigated. Finer microstructure was obtained compared to micrometric powder plasma spraying traditionally used to produce environmental barrier coatings (EBC). XRD analyses on as-sprayed coatings revealed amorphous or crystalline layers depending on plasma parameters. In EBC application, a volume change from crystallization or phase transformation was envisaged to be damaging due to induced stresses and fully crystalline phases are a key durability requirement for EBC from conventional plasma spraying. Yttrium silicates are characterized by an important polymorphism and the ability to form amorphous coatings. Therefore, special attention was so paid to the amorphous degree of the coatings.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.018
Threshold uncertainty score1.000

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)0.0040.001

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 head0.018
GPT teacher head0.225
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2011
Admission routes1
Has abstractyes

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