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Record W2892656821 · doi:10.1063/1.5053328

Improvements to a 13.56 MHz RF powered H− ion source

2018· article· en· W2892656821 on OpenAlexfundno aff
Stéphane Melanson, Anand George, Hamish McDonald, Dave Potkins, Chris Philpott, Morgan Dehnel

Bibliographic record

VenueAIP conference proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCallaghan Innovation
KeywordsIon sourceMaterials scienceIonRF power amplifierIon beamPlasmaIon beam depositionAtomic physicsMagnetIon gunAntenna (radio)OptoelectronicsPhysicsElectrical engineeringNuclear physics

Abstract

fetched live from OpenAlex

D-Pace's 13.56 MHz RF powered H -ion source, a hybrid design between the TRIUMF licensed filament powered volume-cusp ion source 1 and the University of Jyvskyl licensed RF ion source 2 , has been shown to be less efficient than the filament powered ion source, even though both sources use the same body and extraction system 3 . The difference is thought to be due to RF power losses to the outside of the ion source, to the lack of plasma confinement on the back plate of the ion source, and to the absence of a sputtered tantalum coating on the plasma chamber walls. We believe that the lack of confinement on the back plate also causes the RF window to heat, with a maximum temperature measured at 450 C at 3.5 kW of RF power. In this paper, we are investigating the use of a solenoid and permanent magnets behind the antenna and the back plate of the ion source to create a magnetic field that confines the plasma by preventing the electrons from striking the RF window. Furthermore, we present the effect of sputtering a tantalum coating in the plasma chamber of the ion source on the production of H -ions in the RF powered source. Our results show an increase in H -beam current at higher RF powers with a fresh coat of tantalum, and a subsequent decrease in beam current over time.

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 score0.692
Threshold uncertainty score0.824

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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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
Published2018
Admission routes1
Has abstractyes

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