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Record W2158474108 · doi:10.1109/plasma.2002.1030506

Non-linear effects in ICP: theory and experiments

2003· article· en· W2158474108 on OpenAlexaff
A. I. Smolyakov, Valery Godyak, Yu. Tyshetskiy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPonderomotive forceLorentz forceHarmonicsPhysicsPlasmaNonlinear systemMechanicsComputational physicsMagnetic fieldClassical mechanicsQuantum mechanicsVoltage

Abstract

fetched live from OpenAlex

Summary form only given. Operation of a low pressure inductively coupled plasma (ICP) at low driving frequency leads to increased values of the RF magnetic field and thus to enhancement of nonlinear effects due to RF Lorentz force typically negligible at 13.56 MHz. In recent years ICP experiments in low frequency regime have demonstrated generation of strong upper harmonics of the electrostatic potential and electric current in the ICP skin layer. A significant modification of the plasma profile by ponderomotive force has been demonstrated in a low frequency ICP, but the ponderomotive force found in experiment appeared to be significantly smaller than that predicted by classical formula for cold plasma. We review recently obtained experimental results on nonlinear effects in a low frequency cylindrical ICP with a planar coil and present a theory of these phenomena. We show that generation of nonlinear harmonics in RF potential and RF current are due to action of different parts of the RF Lorentz force; its potential part gives rise to potential oscillations, while its solenoidal part (existing only in a dissipative plasma) is responsible for generation of nonlinear current. A generalized theoretical model for nonlinear effects in two-dimensional cylindrical ICP, accounting for potential and solenoidal parts of the RF Lorentz force, has been formulated within a magneto-hydrodynamic approach.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.157

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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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 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

Citations1
Published2003
Admission routes1
Has abstractyes

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