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Record W2976314926 · doi:10.1088/1361-6463/ab4777

Interpretation of artifacts in Fourier transform infrared spectra of atmospheric pressure dielectric barrier discharges: relationship with the plasma frequency between 300 Hz and 15 kHz

2019· article· en· W2976314926 on OpenAlexafffund
Natalia Milaniak, P. Audet, Peter R. Griffiths, F. Massines, Gaétan Laroche

Bibliographic record

VenueJournal of Physics D Applied Physics · 2019
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité LavalHôpital Saint-François d'Assise
FundersNatural Sciences and Engineering Research Council of CanadaCHIST-ERAAgence Nationale de la Recherche
KeywordsInfraredSpectral lineDielectric barrier dischargeFourier transform spectroscopyPlasmaFourier transformAbsorption (acoustics)Low frequencyInterferometryInfrared spectroscopyPlasma oscillationWavenumberExcitationAtmospheric pressureDielectricFourier transform infrared spectroscopyMaterials scienceChemistryOpticsPhysicsOptoelectronics

Abstract

fetched live from OpenAlex

Abstract This article describes the occurrence of a phenomenon that is observed while recording mid-infrared (4000–700 cm −1 ) absorption spectra of dielectric barrier discharges sustained at frequencies ranging from 300 Hz to 15 kHz. This phenomenon is observed as the presence of very sharp spikes in the spectrum, for which the wavenumber depends on both the high voltage frequency used to generate the discharge and the velocity of the moving mirror of the interferometer (which in turn determines the interferogram sampling frequency). While it is well known that the consumption of gas precursor within plasmas can be followed, we demonstrate that Fourier transform infrared spectroscopy also makes it possible to monitor frequencies and coupling of excitation mechanisms occurring in the plasma.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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 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

Citations5
Published2019
Admission routes2
Has abstractyes

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