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Record W2732593758 · doi:10.1109/tps.2017.2716116

Compact Nanosecond Magnetic Pulse Compression Generator for High-Pressure Diffuse Plasma Generation

2017· article· en· W2732593758 on OpenAlexafffund
M. D. G. Evans, Valentin Baillard, Pablo Diaz Gomez Maqueo, Jeffrey M. Bergthorson, Sylvain Coulombe

Bibliographic record

VenueIEEE Transactions on Plasma Science · 2017
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaSiemensMcGill University
KeywordsMaterials scienceNanosecondPulse generatorPlasmaVoltageGenerator (circuit theory)Pulsed powerPulse (music)OpticsOptoelectronicsElectrical engineeringPhysicsPower (physics)Laser

Abstract

fetched live from OpenAlex

We report on the development of a low-cost, adjustable high-voltage/high-power nanosecond-pulse generator for diffuse plasma generation in a high-pressure gas-discharge cell. The generator produces scalable impulsions of 0-40 kV, at an adjustable pulse-repetition frequency up to 7 kHz. Details pertaining to its working principles, electrical architecture, components, and specifications are presented. Voltage and current pulses are measured for resistive loads of 1.5 kQ to 5 MQ. The energy per pulse along with the generator's overall efficiency is presented as a function of the input voltage. A maximum value of 13.5 mJ/pulse can be delivered to a 3-kΩ load. Our preliminary investigation using a pin-to-plate geometry in air and at pressures up to 2.75 atm [280.5 kPa] demonstrates the production of uniform diffuse plasma volumes. The domain of existence of the diffuse plasma regime is briefly explored, as a function of pressure and voltage pulse amplitude.

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 score0.004

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 head0.033
GPT teacher head0.293
Teacher spread0.260 · 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
Published2017
Admission routes2
Has abstractyes

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