Trajectory calculations of space‐charge‐induced mass shifts in a linear quadrupole ion trap
Bibliographic record
Abstract
RATIONALE: If too many ions are stored in a linear quadrupole ion trap, space charge causes the oscillation frequencies to decrease. Ions therefore appear at higher apparent mass-to-charge ratios in a mass spectrum. To further understand this process, we have used trajectory calculations of ions to determine mass shifts. METHODS: Two models of the ion cloud are used. The first assumes that the acceptance of the quadrupole is uniformly filled with ions. The second assumes that the ions have a thermal distribution trapped in the effective potential. Both give analytical descriptions of the field from space charge. Ion trajectories are calculated with and without space charge. Oscillation frequencies are determined with a Fourier transform. Shifts in oscillation frequency with space charge are then used to calculate mass shifts. RESULTS: Both ion cloud models give similar results. More diffuse ion clouds or ion clouds that have higher temperatures produce lower electric fields near the center of the trap and hence lower mass shifts. Space charge produces a nonlinear field. As a result, the discrete resonant frequencies of ions in a pure quadrupole field become distributions of frequencies. Comparisons with experiments show agreement for reasonable values of the parameters of the two ion cloud models. CONCLUSIONS: This relatively simple method for calculating the effects of space charge shows (i) that the spread of oscillation frequencies reduces mass resolution with axial ejection and (ii) that mass shifts are reduced with ion clouds with greater spatial extents or higher ion temperatures.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".