Theoretical Study Synchronized Reverse Scan Collision-Induced Dissociation in Digital Linear Ion Trap
Bibliographic record
Abstract
The effectiveness of collision-induced dissociation (CID) in ion trap mass spectrometry (ITMS) is limited by a low-mass cutoff and weak fragmentation yields. Theoretically, the q value is optimized to balance the fractional product ion mass range with adequate energy deposition to improve fragment ion detection in the CID process; however, many promising technologies still depend on the traditional sinusoidal waveform-driven IT. Additionally, traditional CID-based multistage mass spectrometry (MS n ) experiments on ITMS rely on complex and time-consuming “tuning” to optimize CID for a particular ion. The digital ion trap (DIT) has a very promising application field in MS n analysis, because of its many unique features. Herein, we conducted a theoretical and experimental investigation of a developed synchronized reverse scan–CID (SRS-CID) using a digital linear ion trap. Specifically, (1) simulations and experiments demonstrated that in the SRS-CID, ions were sequentially scanned from high to low m / z value via the resonance excitation point ( q excitation ), producing multiple fragment ions without the need to know the m / z value or complex radiofrequency (rf) tuning of each product ion. The simulations demonstrated that the heating rate in the SRS-CID could reach 0.022 eV/μs. The experiments demonstrated that the optimal reverse scan speed was −0.053 ns/step. (2) We preliminary increased the period by a fixed value ( T step ) to control q excitation to study the molecule fragmentation approach. Different mass spectra were obtained by controlling t excitation with a fixed T step . (3) This paper introduces the phase space method to study the motion trajectories of precursor ions and daughter ions. The calculations used and the entire program were uploaded to GitHub. (4) Changing the duty cycle to advantageously shift q excitation improved the heating rate (0.033 eV/μs) in SRS-CID. Overall, we demonstrated the effectiveness of the developed SRS-CID technique in fragment ion analysis via theoretical derivation, simulation, and experimentation. Furthermore, DIT mass spectrometry was advantageous in tandem mass spectrometry analysis by facilitating modulation of the driving rf period.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".