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Record W4402154225 · doi:10.31399/asm.cp.itsc2024p0176

Development and Understanding of CMAS Coating on YSZ Using APS Technique

2024· article· en· W4402154225 on OpenAlexaff
Amit Roy, Fadhel Ben Ettouil, R.S. Lima, Christian Moreau

Bibliographic record

VenueThermal spray · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council CanadaConcordia University
Fundersnot available
KeywordsMaterials scienceThermal barrier coatingYttria-stabilized zirconiaCoatingMicrostructurePenetration (warfare)Deposition (geology)MetallurgyNozzleComposite materialChemical engineeringCubic zirconiaCeramic

Abstract

fetched live from OpenAlex

Abstract The ingestion of siliceous particulate debris into the gas turbine engines during operation caused the deposition of so-called CMAS (calcium-magnesium-alumino-silicate) on the hotter thermal barrier coating (TBC) surfaces. The penetration of these particles into the TBC at temperatures above 1200°C caused the loss of strain tolerance and premature failure of the TBCs. To mimic real-world conditions, a commercially available CMAS precursor dust powder was sprayed onto 8YSZ coatings using an atmospheric plasma spraying process. The substrate temperature was maintained at an average of 1100°C and 525°C during spraying. The effect of the spraying parameters on the deposition, microstructure, and composition of the CMAS coatings was investigated. In addition, to understand the CMAS build-up on the high-temperature surfaces, the CMAS splat formation behavior was also analyzed on the polished samples at temperatures ~1100°C. SEM/EDS analyzes were performed to identify and quantify the elements of the CMAS deposits. It was found that the surface temperature, deposition time, and different nozzles could play a significant role in having different phases of CMAS deposits.

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 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 score0.004
Threshold uncertainty score0.494

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 head0.047
GPT teacher head0.264
Teacher spread0.217 · 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
Published2024
Admission routes1
Has abstractyes

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