Increased beam energy as a pathway towards a highly selective and high-flux MR-ToF mass separator
Bibliographic record
Abstract
Many experiments at radioactive ion beam (RIB) facilities suffer from isobaric contamination, i.e. unwanted ions of similar mass. During the last decade, Multi-Reflection Time-of-Flight (MR-ToF) devices have gained remarkable attention for mass separation of short-lived, low-intensity beams of radionuclides at RIB facilities throughout the world. They exceed mass resolving powers m/Δm of 105 within a processing time of some (tens of) milliseconds. Due to space-charge effects, however, the mass separation remains an experimental challenge when many ions are simultaneously confined in the MR-ToF device. This limits the wider application of MR-ToF mass separators at RIB facilities. By performing ion-optical simulations including space-charge effects, we investigate different schemes of ion preparation in a Paul trap upstream of the MR-ToF device as well as MR-ToF operation and study their influence on mass separation and maximal ion flux. The validity of these simulations are benchmarked by time-of-flight and collision-induced fluorescence measurements with a 1.5 keV MR-ToF device. More advanced ion-beam preparation techniques such as the use of laser cooling, buffer-gas cooling at cryogenic temperatures or specific electric-field parameters for ion trapping and ejection from the Paul trap can significantly reduce the processing time needed to reach a given mass resolving power. However, the simulations of these methods also indicate that space-charge effects in the MR-ToF device become relevant at lower ion numbers compared to ’standard’ ion preparation. Thus, the overall amount of mass separated ions per unit of time remains essentially the same. In contrast, the simulations suggest that increasing the kinetic energy of typically just a few kiloelectronvolts in present MR-ToF instruments to 30 keV results in a significant increase of the attainable maximal ion flux.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".