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Investigation of the Splat Deposition Behavior onto a Composite Coating in Cold Spray

2018· article· en· W2905838795 on OpenAlexaff
Xin Chu, Phuong Vo, Stephen Yue

Bibliographic record

VenueMaterials science forum · 2018
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council CanadaMcGill University
Fundersnot available
KeywordsMaterials scienceCoatingGas dynamic cold sprayComposite numberScanning electron microscopeDeposition (geology)NanoindentationComposite materialProfilometerSubstrate (aquarium)AdhesionMetallurgySurface finish

Abstract

fetched live from OpenAlex

The splat test is usually generated by low feed rate cold spraying of particles onto an as-polished substrate and it can be considered as a monolayer coating deposition. In this study, in order to investigate cold spray deposition mechanisms, Fe splats were sprayed onto the cold-sprayed single component 316L, Fe, and a composite 90Fe coatings. Results showed that although there is only 3.6 vol.% of 316L in the composite 90Fe coating, Fe splats exhibit a much better deposition behavior onto the 90Fe as compared with the single component Fe coating. To explain this observation, Fe splat samples were characterized using the scanning electron microscope (SEM), optical profilometry, splat adhesion tests, and splat nanoindentation. Finally, a preliminary explanation towards the Fe splat deposition behavior onto the composite coating was drawn.

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 score0.061
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.231
Teacher spread0.220 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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