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Record W2041375312 · doi:10.1007/s11666-013-0053-2

Effect of Heat Treatment on the Microstructure and Mechanical Properties of Stainless Steel 316L Coatings Produced by Cold Spray for Biomedical Applications

2013· article· en· W2041375312 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Thermal Spray Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council CanadaMcGill University
FundersNational Research Council CanadaSaudi Basic Industries CorporationMcGill University
KeywordsMaterials scienceMicrostructureGas dynamic cold sprayCoatingIndentation hardnessPropellantDuctility (Earth science)PorosityMetallurgyComposite materialThermal sprayingCreep

Abstract

fetched live from OpenAlex

Abstract In this study, the effects of heat treatment on the microstructure and mechanical properties of cold sprayed stainless steel 316L coatings using N 2 and He as propellant gases were investigated. Powder and coating characterizations, including coating microhardness, coating porosity, and XRD phase analysis were performed. It was found that heat treatment reduced porosity, improved inter-particle bonding, and increased ductility. XRD results confirmed that no phase transformation occurred during deposition. Significant increase in UTS and ductility was observed for the annealed specimens obtained with nitrogen propellant, whereas little changes were observed for the helium propellant produced specimen.

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 score0.003
Threshold uncertainty score0.409

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 head0.219
Teacher spread0.213 · 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