The extraction of both positive and negative ions from a volume cusp H<sup>−</sup> ion source
Bibliographic record
Abstract
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 + , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mi mathvariant="normal">H</mml:mi> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> </mml:msubsup> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msubsup> <mml:mi mathvariant="normal">H</mml:mi> <mml:mn>3</mml:mn> <mml:mo>+</mml:mo> </mml:msubsup> </mml:mrow> </mml:math> ) 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".