MétaCan
Menu
Back to cohort

Application of functional coatings by supersonic thermal plasma flows

2021· article· en· W4200386536 on OpenAlexaboutno aff
V. I. Kuzmin, I. P. Gulyaev, D. V. Sergachev, Boris Palagushkin, O Y Lebedev

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsSupersonic speedPlasma torchThermal sprayingPlasmaNozzleMaterials scienceTorchNialGas dynamic cold sprayDeposition (geology)Jet (fluid)NichromeThermal barrier coatingThermalCoatingComposite materialMechanicsAerospace engineeringThermodynamicsPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract Development of modern high-speed technologies for thermal spraying proves that deposition of high-quality dense coatings requires velocity of sprayed particles to be 600 m/s and above. Plasma spraying is the most versatile and highly productive deposition method of various functional coatings without any limitations on the melting points of the sprayed materials. Present work describes a DC plasma torch designed for operation in a supersonic mode. The supersonic plasma torch features de Laval nozzle, utilization of air as a plasma-forming gas, and annular injection unit for delivery of the powder to the plasma jet. The comparison of NiCr and NiAl coatings deposited both in subsonic and supersonic modes are presented. Methods for further increasing the sprayed particles’velocity and the requirements for their heating temperature are proposed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.206
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Physics Conference SeriesSame topicHigh-Temperature Coating BehaviorsFrench-language works237,207