Using differential mobility spectrometry to measure ion solvation: an examination of the roles of solvents and ionic structures in separating quinoline-based drugs
Bibliographic record
Abstract
Understanding the mechanisms and energetics of ion solvation is critical in many scientific areas. Here, we present a methodlogy for studying ion solvation using differential mobility spectrometry (DMS) coupled to mass spectrometry. While in the DMS cell, ions experience electric fields established by a high frequency asymmetric waveform in the presence of a desired pressure of water vapor. By observing how a specific ion's behavior changes between the high- and low-field parts of the waveform, we gain knowledge about the aqueous microsolvation of that ion. In this study, we applied DMS to investigate the aqueous microsolvation of protonated quinoline-based drug candidates. Owing to their low binding energies with water, the clustering propensity of 8-substituted quinolinium ions was less than that of the 6- or 7-substituted analogues. We attribute these differences to the steric hinderance presented by subtituents in the 8-position. In addition, these experimental DMS results were complemented by extensive computational studies that determined cluster structures and relative thermodynamic stabilities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".