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Record W2513153855 · doi:10.1088/0963-0252/25/5/055007

Characterization of magnetically confined low-pressure plasmas produced by an electromagnetic field in argon-acetylene mixtures

2016· article· en· W2513153855 on OpenAlexafffund
G. Al Makdessi, J. Margot, Richard Clergereaux

Bibliographic record

VenuePlasma Sources Science and Technology · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCentre National de la Recherche Scientifique
KeywordsAcetylenePlasmaArgonIonChemistryNucleationAtomic physicsAtmospheric pressureNoble gasPlasma parametersAnalytical Chemistry (journal)Organic chemistry

Abstract

fetched live from OpenAlex

Abstract Dust particles formation was investigated in magnetically confined low-pressure plasma produced in argon-acetylene mixtures. The plasma characteristics were measured in order to identify the species involved in the dust particles formation. Their dependence on the operating conditions including magnetic field intensity, acetylene fraction in the gas mixture and operating pressure was examined. In contrast with noble gases, in the presence of acetylene, the electron temperature increases with the magnetic field intensity, indicating additional charged particles losses in the plasma. Indeed, in these conditions, larger hydrocarbon ions are produced leading to the formation of dust particles in the plasma volume. The observed dependence of positive ion mass distribution and density and relative negative ion density on the operating parameters suggests that the dust particles are formed through different pathways, where negative and positive ions are both involved in the nucleation.

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 score0.002

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 head0.003
GPT teacher head0.193
Teacher spread0.189 · 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
Published2016
Admission routes2
Has abstractyes

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