Toward Part-per-Million Precision in the Determination of an Ion’s Collision Cross Section Using Multipass Cyclic Ion Mobility
Bibliographic record
Abstract
In cyclic ion mobility (cIMS), ions are permitted to travel multiple passes around the drift cell, increasing the distance traveled and the relative separation between ions. This study tests the hypothesis that multiple passes around the cell can also result in improved precision when measuring an ion’s mobility and the collision cross section ( TW CCS) derived therefrom. Experiments were performed with a diverse set of compounds, including 16 polycyclic aromatic hydrocarbons using gas chromatographic atmospheric pressure chemical ionization and a set of drug molecules by direct infusion electrospray ionization. The average periodic drift time, viz., the average time required for the ion to travel around the cIMS cell once, shifts dramatically, approaching part-per-million (ppm) precision as the number of passes increases to ∼100. Extrapolation of the precision of the CCS values with respect to the number of passes led to the prediction that the precision will reach 1000 ppm after 50 passes, 100 ppm after 100 passes, and <10 ppm after 150 passes. Experiments wherein the number of passes exceeded 100 produced TW CCS values having within-run precisions ranging between 15 and 117 ppm. The improved precision with an increasing number of passes may be a consequence of mitigating space-charge effects by allowing the ions to occupy a larger region of the cIMS cell. A method is proposed to enable practical measurements of TW CCS with ppm precision and is demonstrated to characterize an unknown drug mixture.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".