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Record W4385876634 · doi:10.1063/5.0156669

On the polytropic coefficient of negative ions for modeling the sheath and presheath of electronegative plasmas

2023· article· en· W4385876634 on OpenAlexaff
L. Schiesko, St. Lishev, A. Revel, E. Carbone, Tiberiu Minea

Bibliographic record

VenueJournal of Applied Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsPolytropic processIonPlasmaAdiabatic processAtomic physicsIsothermal processChemistryPhysicsDebye sheathThermodynamicsClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

The fluid description is widely used for the multi-dimensional modeling of low temperature plasmas with complex chemistries due to their relative low computational cost. It relies, however, on a series of simplifying assumptions and some truncation of the moment equations for describing the non-equilibrium between the electrons, positive ions, negative ions, and the neutrals. In this paper, the classical assumption of isothermal negative ions is revisited for electronegative plasmas and, more particularly, for the fluid modeling of the transition between the plasma and its sheath. To do so, and in contrast to previous studies, the energy balance equation for the negative ions is also computed, and it allows us to derive the polytropic coefficient γ of the negative ions in addition to one of the positive ions. Strong variations in the sheath and presheath of the negative ions temperature and their polytropic coefficient are observed. The polytropic coefficient is shown to be a strongly varying function of space having for consequence that the negative ions are isothermal only in a very narrow extension of the presheath. For the case considered in this paper, both positive and negative ion flows are nearly adiabatic at the sheath-edge and become adiabatic inside the sheath. This paper shows that classical fluid modeling assumptions need to be verified for each system under consideration, most particularly while modeling the transition from plasma to a wall.

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 score0.109
Threshold uncertainty score0.191

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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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