Mass selectivity of dipolar resonant excitation in a linear quadrupole ion trap
Bibliographic record
Abstract
RATIONALE: For mass analysis, linear quadrupole ion traps operate with dipolar excitation of ions for either axial or radial ejection. There have been comparatively few computer simulations of this process. We introduce a new concept, the excitation contour, S(q), the fraction of the excited ions that reach the trap electrodes when trapped at q values near that corresponding to the excitation frequency. METHODS: Ion trajectory calculations are used to calculate S(q). Ions are given Gaussian distributions of initial positions in x and y, and thermal initial velocity distributions. To model gas damping, a drag force is added to the equations of motion. The effects of the initial conditions, ejection Mathieu parameter q, scan speed, excitation voltage and collisional damping, are modeled. RESULTS: We find that, with no buffer gas, the mass resolution is mostly determined by the excitation time and is given by R~dβ/dq qn, where β(q) determines the oscillation frequency, and n is the number of cycles of the trapping radio frequency during the excitation or ejection time. The highest resolution at a given scan speed is reached with the lowest excitation amplitude that gives ejection. The addition of a buffer gas can increase the mass resolution. The simulation results are in broad agreement with experiments. CONCLUSIONS: The excitation contour, S(q), introduced here, is a useful tool for studying the ejection process. The excitation strength, excitation time and buffer gas pressure interact in a complex way but, when set properly, a mass resolution R0.5 of at least 10,000 can be obtained at a mass-to-charge ratio of 609.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".