Preferential Ion Microsolvation in Mixed-Modifier Environments Observed Using Differential Mobility Spectrometry
Bibliographic record
Abstract
The preferential solvation behavior for eight different derivatives of protonated quinoline was measured in a tandem differential mobility spectrometer mass spectrometer (DMS-MS). Ion-solvent cluster formation was induced in the DMS by the addition of chemical modifiers (i.e., solvent vapors) to the N 2 buffer gas. To determine the effect of more than one modifier in the DMS environment, we performed DMS experiments with varying mixtures of water, acetonitrile, and isopropyl alcohol solvent vapors. The results show that doping the buffer gas with a binary mixture of modifiers leads to the ions binding preferentially to one modifier over another. We used density functional theory to calculate the ion-solvent binding energies, and in all cases, calculations show that the quinolinium ions bind most strongly with acetonitrile, then isopropyl alcohol, and most weakly with water. Computational results support the hypothesis that the quinolinium ions bind exclusively to whichever solvent they have the strongest interaction with, regardless of the presence of other modifier gases.
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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".