Bibliographic record
Abstract
Abstract I. Introduction 000 II. Linear Multipoles 000 A. Multipole Fields 000 1. Multipole Potentials 000 2. Ion Motion in 2D Multipole Fields 000 3. Ion Motion in Quadrupole Fields 000 4. Ion Motion in Higher Multipole Fields 000 5. Linear Ion Guides, Collision Cells, and Traps 000 6. Collisional Cooling 000 7. Ion Excitation in the Presence of a Collision Gas 000 8. Ion Isolation 000 9. Trap Capacity 000 A. Mass Discrimination Effects 000 III. Linear Traps Combined with Other Mass Analyzers 000 A. Linear Traps Combined with 3D Paul Traps 000 B. Linear Ion Traps Combined with TOF Mass Analyzers 000 C. Linear Traps Combined with FTICR 000 IV. Linear Traps as Mass Spectrometers 000 A. Fourier Transform Mass Spectrometry in a Linear Quadrupole Ion Trap 000 B. Linear Quadrupole Ion Trap Mass Spectrometer with Radial Ion Ejection 000 C. Linear Quadrupole Ion Trap Mass Spectrometer with Axial Ejection 000 V. Summary and Outlook 000 Acknowledgments 000 List of Symbols 000 References 000 Linear ion traps are finding new applications in many areas of mass spectrometry. In a linear ion trap, ions are confined radially by a two‐dimensional (2D) radio frequency (RF) field, and axially by stopping potentials applied to end electrodes. This review focuses on linear ion trap instrumentation. Potentials and ion motion in linear multipole fields and methods of ion trapping, cooling, excitation, and isolation are described. This is followed by a description of various mass discrimination effects that have been reported with linear ion traps. Linear ion traps combined in various ways with three‐dimensional (3D) traps, time‐of‐flight (TOF) mass analyzers, and Fourier transform ion cyclotron resonance mass spectrometers are then given. Linear ion traps can be used as stand alone mass analyzers, and their use for mass analysis by Fourier transforming image currents, by mass selective radial ejection, and by mass selective axial ejection are reviewed. © 2004 Wiley Periodicals, Inc., Mass Spec Rev
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.023 |
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".