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Record W1989286136 · doi:10.1063/1.1538313

Characterization of neutral, positive, and negative species in a chlorine high-density surface-wave plasma

2003· article· en· W1989286136 on OpenAlexafffund
Luc Stafford, J. Margot, Mohamed Chaker, O. Pauna

Bibliographic record

VenueJournal of Applied Physics · 2003
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIonChemistryPlasmaTorrAtomic physicsDissociation (chemistry)ElectronChlorineDiffusionAnalytical Chemistry (journal)ThermodynamicsPhysics

Abstract

fetched live from OpenAlex

This article reports an experimental study of the influence of the plasma parameters on the concentration of neutral and ion species in a chlorine high-density plasma sustained by surface waves. The investigation focuses on the dependence of the concentrations of Cl, Cl2, Cl+, Cl2+, Cl−, and electrons on the gas pressure in the 0.1 to 10 mTorr range, and on the intensity of a confinement magnetic field. The results show that a high dissociation degree (up to 90%) can be achieved even with a very modest power level (250 W, power density of about 2 mW/cm3), provided the pressure is low enough (i.e., less than 1 mTorr). It was also found that Cl+ is the main positive ion and that electrons are the main negative charge carrier at lower pressure. When the gas pressure is higher than a few mTorr, Cl2+ becomes dominant with Cl− as the negative charge carrier. The behavior of the positive ion and neutral species concentrations is compared to the results of a simple model based on creation–losses rate equations for the various species. It is shown that for a given magnetic field intensity, there is a critical pressure above which diffusion can be neglected in comparison with ion–ion recombination and charge transfer.

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 score0.064
Threshold uncertainty score0.374

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 head0.011
GPT teacher head0.182
Teacher spread0.172 · 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

Citations13
Published2003
Admission routes2
Has abstractyes

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