Overcoming Field Imperfections of Quadrupole Mass Filters with Mass Analysis in Islands of Stability
Bibliographic record
Abstract
We have constructed, and tested as mass filters, linear quadrupoles with added hexapole fields of 4%, 8%, and 12%, with and without added octopole fields. A hexapole field can be added to the field of a linear quadrupole by rotating the two y rods toward an x rod. This also adds an octopole field which can be removed by making the x rods greater in diameter than the y rods. In comparison to conventional quadrupole mass filters these rod sets have severely distorted quadrupole fields, with a mix of both even and odd higher spatial harmonics. They allow evaluating the performance of rod sets with strong geometric and field distortions as mass filters. Conventional mass analysis at the tip of the stability diagram has been compared to mass analysis using islands of stability. The stability islands are produced by applying an auxiliary quadrupole excitation field to the quadrupole. We show that with normal mass analysis at the tip of the stability diagram, the transmission, resolution, and peak shapes are relatively poor in comparison to a conventional rod set. However, the use of islands of stability dramatically improves the resolution and peak shape, and in some cases ion transmission, suggesting that mass analysis with islands of stability may provide a method to overcome a wide range of field imperfections in linear quadrupole mass filters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".