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Record W4294830864 · doi:10.31399/asm.cp.itsc2006p0373

RF Plasma Deposition of Refractory Metals Case Study for Tungsten

2006· article· en· W4294830864 on OpenAlexaff
Ondřej Kovářı́k, Siwen Xue, Xiaobao Fan, Maher I. Boulos

Bibliographic record

VenueThermal spray · 2006
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsTekna Plasma Systems (Canada)Université de Sherbrooke
Fundersnot available
KeywordsTungstenMaterials scienceRefractory metalsParticle (ecology)PlasmaDeposition (geology)Thermal sprayingInductively coupled plasmaSolution precursor plasma sprayPlasma processingSubstrate (aquarium)MetallurgyResidence time (fluid dynamics)Gas dynamic cold sprayParticle sizeTitaniumComposite materialCoatingChemical engineering

Abstract

fetched live from OpenAlex

Abstract The paper presents an integrated study of the effects of RF plasma spray process parameters on the particle melting, particle spheroidisation and acceleration in the plasma, particle-substrate interactions and final deposit properties. Particle temperatures and velocities have been studied, by both experimental and numerical simulation methods, as functions of spray particle diameters. In-flight spheroidisation behavior was also observed by means of a particle capturing technique while splat formation was studied on polished stainless steel substrates. Optimized process parameters were then estimated and used to produce deposits on stationary substrates. Deposit properties, such as splat shape and crystal grain morphologies, apparent densities and deposition efficiencies were observed and processing parameters further optimized. The results obtained indicate that the advantages of the RF inductively coupled plasma spray technique, such as the longer particle residence time in the plasma and “cleanliness” of the process can be efficiently utilized to deposit dense tungsten metal parts or 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 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 score0.009
Threshold uncertainty score0.518

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 head0.013
GPT teacher head0.246
Teacher spread0.233 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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