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Record W3015096400 · doi:10.3390/plasma3020005

Spatially-Resolved Spectroscopic Diagnostics of a Miniature RF Atmospheric Pressure Plasma Jet in Argon Open to Ambient Air

2020· article· en· W3015096400 on OpenAlexafffund
Florent Sainct, Antoine Durocher‐Jean, Reetesh Kumar Gangwar, Norma Yadira Mendoza Gonzalez, Sylvain Coulombe, Luc Stafford

Bibliographic record

VenuePlasma · 2020
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de MontréalMcGill University
FundersUniversité de MontréalNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsRotational temperatureAtomic physicsElectron temperatureArgonAtmospheric pressurePopulationPlasmaRadiative transferEmission spectrumJet (fluid)ChemistryPlasma diagnosticsElectron densityAnalytical Chemistry (journal)Materials scienceSpectral lineOpticsPhysics

Abstract

fetched live from OpenAlex

The spatially-resolved electron temperature, rotational temperature, and number density of the two metastable Ar 1 s levels were investigated in a miniature RF Ar glow discharge jet at atmospheric pressure. The 1 s level population densities were determined from optical absorption spectroscopy (OAS) measurements assuming a Voigt profile for the plasma emission and a Gaussian profile for the lamp emission. As for the electron temperature, it was deduced from the comparison of the measured Ar 2 p i → 1 s j emission lines with those simulated using a collisional-radiative model. The Ar 1 s level population higher than 10 18 m − 3 and electron temperature around 2.5 eV were obtained close to the nozzle exit. In addition, both values decreased steadily along the discharge axis. Rotational temperatures determined from OH(A) and N 2 (C) optical emission featured a large difference with the gas temperature found from a thermocouple; a feature ascribed to the population of emitting OH and N 2 states by energy transfer reactions involving the Ar 1 s levels.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.271
Teacher spread0.253 · 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.

Study designObservational
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

Citations15
Published2020
Admission routes2
Has abstractyes

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