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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 OpenAlex
Surabhi Jaiswal, Mohamad Menati, Lénaïc Couëdel, Vincent Holloman, Vijay Rangari, Edward Thomas

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
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