A combined linear ion trap time‐of‐flight system with improved performance and MS <sup>n</sup> capabilities
Bibliographic record
Abstract
A detailed description of a linear ion trap time-of-flight (TOF) mass spectrometer system, capable of sequential mass spectrometry (MS(n)), is given. Many improvements have been incorporated since the initial description of this system (Rapid Commun. Mass Spectrom. 1998; 12: 1463-1474). The pressure in the trap has been lowered from 7.0 to 1.8 mTorr, resulting in an increase in the mass resolution of ion excitation from 75 to 240. Use of the system for MS(3) is demonstrated. Dipole excitation of the n = 1 harmonic, instead of the n = 0 fundamental frequency of ion motion, is shown to have a higher frequency resolution, f/Deltaf, but lower mass resolution, m/Deltam. Both experiments and modeling demonstrate that at the lower pressure there is less collisional cooling of ions in the axial and radial directions of the trap. The efficiency of trapping is shown to be nearly 100% for periods up to 5 s. The demonstrated mass range for mass analysis has been extended to greater than m/z 16 250. To avoid the formation of adduct ions when trapping protein ions for extended times requires ultra-high vacuum cleanliness conditions, even though the trap operates in the mTorr-pressure range. Upgrading the TOF to a reflectron with higher quality ion optics results in an increase in the mass resolution of the TOF mass spectrometer to about 5000 at m/z 750.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".