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Record W4400806139 · doi:10.1063/5.0211714

Origin and impact of ion heating in the cathode sheath of direct-current argon discharges at moderate pressures

2024· article· en· W4400806139 on OpenAlexafffund
Ji Hwan Mun, M. Muraglia, O. Agullo, C. Arnas, Lénaïc Couëdel

Bibliographic record

VenuePhysics of Plasmas · 2024
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlasmaIonAtomic physicsCathodeDebye sheathArgonPhysicsKinetic energyIon currentCurrent (fluid)PerpendicularElectron temperatureElectric fieldElectronChemistryThermodynamicsClassical mechanics

Abstract

fetched live from OpenAlex

In this article, we analyze the impact of ion dynamics in the sheath of argon DC plasma discharges at moderate pressures (13, 65, and 130 Pa), where the ion mean free path is smaller than the sheath width. Our analysis reveals that the evolution of the ion temperature profile plays a major role in regulating the sheath formation process, influencing plasma species density and ion fluid velocity at the cathode. Through meticulous comparison of simulation data from one-dimensional self-consistent fluid models with Particles-In-Cell 1D3V (one dimension in space and three dimensions in velocity) kinetic models, we demonstrate the necessity of considering ion-neutral collisions in fluid models to accurately simulate the glow discharge. In particular, we emphasize the necessity of self-consistent ion temperature profile calculations, particularly in the sheath region. Notably, even at moderate neutral gas pressures, the ion temperature within the cathode sheath can significantly exceed background gas temperature. Kinetic simulations demonstrate the role of ion-neutral collisions in the progressive spreading of ion velocities in directions perpendicular to the cathode sheath electric field.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.255

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 head0.022
GPT teacher head0.288
Teacher spread0.265 · 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 designSimulation or modeling
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

Citations3
Published2024
Admission routes2
Has abstractyes

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