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Record W4220899809 · doi:10.1088/1361-6595/ac5eca

On the use of ultra-high resolution PIC methods to unveil microscale effects of plasma kinetic instabilities: electron trapping and release by electrostatic tidal effect

2022· article· en· W4220899809 on OpenAlexaff
L. Schiesko, A. Revel, Teofil Minea, E. Carbone

Bibliographic record

VenuePlasma Sources Science and Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMicroscale chemistryElectronPlasmaPhysicsAtomic physicsInstabilityElectric fieldKinetic energyIonTrappingComputational physicsMechanicsClassical mechanicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract Ultra-high resolution particle-in-cell coupled to Monte-Carlo collisions modelling unveils microscale instabilities in non-equilibrium plasmas fulfilling Penrose’s instability criterion. The spontaneous development of ion turbulence in the phase-space generated by charge exchange collisions leads to finite amplitude modulations of the local electric field. The latter are responsible for the trapping of low energy electrons and their transport from the plasma volume to the sheath vicinity. Electrostatic tidal effect occurring near the sheath is responsible for the release of the trapped electrons as a monochromatic bunch, accelerated back towards the source. This instability provides an additional theoretical ground for the anomalous enrichment of low-energy electrons observed by Langmuir probes in similar conditions. The present results demonstrate that marginally fulfilling PIC criteria is insufficient to study the microscale instabilities effects on the electrons dynamics in non-equilibrium low temperature plasmas.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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