Instrumental Dispersion Evaluation on Supercritical Fluid Chromatography-Medium Vacuum Chemical Ionization Tandem Mass Spectrometry
Bibliographic record
Abstract
Instrumental dispersion in the ion source can severely distort fast chromatographic peaks in supercri cal fluid chromatography (SFC)-mass spectrometry (MS).Despite this importance, the dispersion characteris cs specific to medium-vacuum chemical ioniza on (MVCI) sources have not been quan ta vely inves gated.In this work, we combine targeted experiments with established computa onal toolscomputa onal fluid dynamics (CFD) and electrosta c field simula on-to characterize ion transport in the MVCI flow tube.Arrival profiles of vitamin K1 (VK1) ions monitored by selected ion monitoring consistently showed a reproducible two-component structure consis ng of an early narrow bandwidth ion packet followed by a delayed shoulder.CFD calcula ons reproduce this tailing peak profile, and electrosta c modeling further revealed that applying the same poten al to the MVCI flow tube and inner cylinder generates lateral poten al walls that inhibit long-residence-me ions from entering the skimmer.Introducing an appropriate poten al difference between the MVCI inner cylinder and the skimmer orifice isolates the MVCI flow field from the ion guide region and suppresses long residence-me trajectories, which narrows the VK1 peak width by more than threefold and restores the intrinsic column efficiency of a sub-2-µm SFC column (from the theore cal plate (N)=3120 to 12079).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".