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Record W4396722623 · doi:10.1063/5.0180909

Hyperspectral imaging of a microwave argon plasma jet expanding in ambient air

2024· article· en· W4396722623 on OpenAlexafffund
Fatima Khazem, Antoine Durocher‐Jean, Ahmad Hamdan, Luc Stafford

Bibliographic record

VenueReview of Scientific Instruments · 2024
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCourtois Foundation
KeywordsArgonPlasmaMaterials scienceHyperspectral imagingPlasma diagnosticsEmission spectrumOpticsMicrowaveSpectral linePhysicsAtomic physicsRemote sensing

Abstract

fetched live from OpenAlex

Non-equilibrium plasmas at atmospheric pressure are often characterized by optical emission spectroscopy. Despite the simplicity of recording optical emission spectra in plasmas, the determination of spatially resolved plasma properties (e.g., electron temperature) in an efficient way is very challenging. In this study, spatially resolved optical images of a microwave argon plasma jet expanding into the ambient air are recorded over a wide range of wavelengths using a hyperspectral imaging system based on a tunable Bragg-grating imager coupled to a scientific complementary metal-oxide-semiconductor camera. The system's working principle is detailed, along with the necessary post-processing steps. Further analysis of the spatial-spectral data, including the Abel transform used to determine 2D radially resolved spatial mappings, is also presented. Overall, the proposed approach provides unprecedented cartographies of key plasma parameters, such as argon and oxygen line emission intensities, Ar metastable number densities, and argon excitation temperatures. Considering that all these plasma parameters are obtained from measurements performed in a reasonable time, Bragg-grating-based hyperspectral imaging constitutes an advantageous plasma diagnostic technique for detailed analysis of microwave plasma jets used in several applications.

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 score0.320
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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