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The extraction of both positive and negative ions from a volume cusp H<sup>−</sup> ion source

2024· article· en· W4396919018 on OpenAlexaff
Stéphane Melanson, Anand George, Morgan Dehnel

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsPacific Insight Electronics (Canada)
Fundersnot available
KeywordsIonCusp (singularity)Volume (thermodynamics)Extraction (chemistry)PhysicsChemistryMathematicsChromatographyGeometry

Abstract

fetched live from OpenAlex

Abstract The TRIUMF licensed H− ion source is known to produce 15 mA of H− ions, but some applications, such as cyclotrons, require the injection of both positive and negative ions. Furthermore, when using a tandem accelerator, there would be a benefit in using an ion source that can directly extract H− and He+. A charge exchange chamber would only have to be used with the He+ beam for the production of He−. In this paper, we present the conversion of the H− ion source to allow for the extraction of both positive (He+, H+, H 2 + and H 3 + ) and negative (H− and D−) ions. In the modified design, up to 3 mA of protons and 2 mA of He+ can be extracted when using positive extraction power supplies and up to 5 mA of H− can be extracted when the polarity of the power supplies switched to negative extraction. We study how the ion source parameters affect the proton fraction and we look at the influence of the magnetic filter field in the plasma chamber.

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)0.001
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.228
Teacher spread0.218 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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