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Record W2981022470 · doi:10.4995/ampere2019.2019.9989

GENERATION AND MODELING OF GASEOUS PLASMAS USING MICROWAVE (MW) POWER

2019· article· en· W2981022470 on OpenAlexaff
Michel Moisan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAtomic physicsElectronCollision frequencyPlasmaMicrowavePhysicsElectron cyclotron resonanceComputational physicsNuclear physics

Abstract

fetched live from OpenAlex

In contrast to RF produced plasmas, in the case of microwave plasmas the energy from the electromagnetic (EM) field is communicated only to electrons since ions, being a few thousand times much heavier than electrons, cannot respond to the periodic changes in the direction of the E-field of microwaves (typical frequency range 100 MHz-300 GHz) and therefore cannot gain energy in the EM field. The energy of electrons is essentially transferred to heavy particles either through numerous enough collisions during the E-field period (high enough gas pressures) or through electron-cyclotron resonance (pressures below mTorr) sustaining in this way the gas discharge. This had led to introduce the concept of power absorbed per electron qA and power loss on a per electron basis qL [1]. Under steady-state conditions and when the plasma volume (the volume in which plasma particles recombine and, thus, power is lost) is equal to the volume in which power is absorbed from the MW field, we have the power balance qA = qL, which can be shown to be much informative than the usual global power balance. qA is defined as where n is the electron collision frequency for momentum transfer, w, the wave angular frequency, e/me, the electron charge to mass ratio, and , the mean squared value of the EM E-field. The value of qA (absorbed power) is shown to adjust so as to compensate exactly for qL (power losses), which is thus the dominant power parameter; as a result, the intensity of the maintenance E-field sustaining the discharge comes out as an internal parameter, i.e., it is operator-independent, in contrast to what is generally believed whatever the kind of E-field sustained discharges. Other related features are: i) whenever this can be achieved, the smaller the volume in which power is absorbed with respect to the volume in which it is spent, the higher the intensity of the maintenance E-field: this leads to higher atomic (molecular) excitation rates inside than outside the absorption region (such is the case in micro-discharges); ii) an interesting fact as far as understanding RF and MW discharge properties is concerned is that the value of qL decreases with increasing frequency from the RF domain to that of MWs; iii) similarity laws, initially derived with DC discharges, are generalized to include RF and microwave discharges. For example, qA/p as a function of pR (p is gas pressure and R discharge-tube inner radius) replaces advantageously the widely used E/p vs. pR similarity law since qA is more easily measured than E2 and further it avoids considering the latter as an external parameter, etc.; iv) using the power per electron balance, it can be proved that the EM E-field intensity under electron cyclotron resonance (ECR) condition passes through a minimum, not a maximum, contrary to what is generally claimed; v) the E-field intensity under pulsed regime can be maximized under short enough pulse length and long enough off-time in between.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.221
Teacher spread0.193 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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