Differential Mobility Spectrometry of Isomeric Protonated Dipeptides: Modifier and Field Effects on Ion Mobility and Stability
Bibliographic record
Abstract
The ability to resolve isomeric protonated dipeptides was investigated with the new technique of differential ion mobility mass spectrometry that uses "modifier" molecules to enhance differential mobility. Two pairs of protonated peptides [glycine-alanine (GlyAla) and alanine-glycine (AlaGly), glycine-serine (GlySer) and serine-glycine (SerGly)] and eight different modifiers (water, 2-propanol, 1,5-hexadiene, 2-chloropropane, chlorobenzene, dichloromethane, acetonitrile, and cyclohexane) were used in the initial study. Separation of the protonated peptides was found to be dependent on the mass and proton affinity of the modifier and combinations of functionalities present in the modifier and the analyte ion. Six of the eight modifiers (water, 2-propanol, chlorobenzene, cyclohexane, dichloromethane, and acetonitrile) were able to separate the protonated isomeric peptide pairs, and generally, modifiers with electron-rich groups performed the best. In the presence of some modifiers, a reduction of ion current was observed under the highest field conditions (>115 Td). Dopant-catalyzed isomerization, likely by proton-transport catalysis, and field-induced fragmentation may have contributed to these losses. Two high vapor pressure modifiers, 1,5-hexadiene and 2-chloropropane, significantly influenced ion formation leading to the formation of stable cluster populations that could be observed in the mass spectrometer. Although not a major concern, both fragmentation and influence of modifier evaporation warrant further studies in order to fully understand and possibly eliminate them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".