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Record W3008503028 · doi:10.35848/1347-4065/ab78eb

Effect of growing nanoparticle on the magnetic field induced filaments in a radio-frequency Ar/C <sub>2</sub> H <sub>2</sub> discharge plasma

2020· article· en· W3008503028 on OpenAlexaff
Surabhi Jaiswal, Mohamad Menati, Lénaïc Couëdel, Vincent Holloman, Vijay Rangari, Edward Thomas

Bibliographic record

VenueJapanese Journal of Applied Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDust and Plasma Wave Phenomena
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPlasmaMagnetic fieldNanoparticleElectrodeRadio frequencyMaterials scienceMagnetic nanoparticlesCapacitive sensingParticle-in-cellField (mathematics)Atomic physicsChemical physicsAnalytical Chemistry (journal)Molecular physicsNanotechnologyChemistryPhysicsElectrical engineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Growth of nanoparticles in plasmas is an emerging topic of research due to its numerous implications in industrial, and fusion plasmas applications. In this paper, effect of a magnetic field induced filaments on the growing nanoparticles and vice versa has been investigated. The experiment has been performed in a capacitive coupled radio-frequency Ar/C 2 H 2 discharge. The magnetic field affect the plasma dynamics and confined it within the electrodes. At a very high magnetic field ( B ≥ 1 T) a stationary or moving filamentary structures are formed between the electrodes that are aligned along the magnetic field. These filamentary structures are found to be suppressed during nanoparticle growth. A particle in cell simulation has been performed to understand the suppression of these filamentary structure.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.211
Teacher spread0.200 · 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 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

Citations10
Published2020
Admission routes1
Has abstractyes

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