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Record W2921044197 · doi:10.1088/1361-6463/ab0fd4

Time and space-resolved experimental investigation of the electron energy distribution function of a helium capacitive discharge at atmospheric pressure

2019· article· en· W2921044197 on OpenAlexafffund
Jean-Sébastien Boisvert, Florence Montpetit, François Vidal, J. Margot, Luc Stafford

Bibliographic record

VenueJournal of Physics D Applied Physics · 2019
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsUniversité de MontréalInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeliumAtmospheric pressureAtomic physicsElectronSpace (punctuation)Energy (signal processing)PhysicsCapacitive sensingComputational physicsMaterials scienceNuclear physicsMeteorologyQuantum mechanicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Using a collisional radiative model coupled with optical emission spectroscopy (OES) of helium n = 3 levels, the electron temperature (T e ) of an atmospheric-pressure capacitively coupled radiofrequency (AP-CCRF) discharge in helium is determined with space and time resolution. When the AP-CCRF discharge is sustained in the mode, T e varies from 0.2 to 7.2 eV. In this case, high values of T e (>5 eV) occur only during a brief instant (<10 ns) in the high-voltage sheath. When the AP-CCRF discharge is sustained in the mode, T e varies from 0.3 to 0.4 eV during the complete cycle. The physical meaning of these electron temperatures are then analyzed by considering possible departure from the Maxwellian electron energy distribution function (EEDF). As a first approximation to non-Maxwellian distribution functions, a two-parameter EEDF was used to fit the OES data. This approach yields an overpopulation of high energy electrons with respect to the Maxwellian form in the mode and the opposite trend in the mode.

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 score0.019
Threshold uncertainty score0.419

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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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