Ion-Molecule Clustering in Differential Mobility Spectrometry: Lessons Learned from Tetraalkylammonium Cations and their Isomers
Bibliographic record
Abstract
Differential mobility spectrometry (DMS) can distinguish ions based upon the differences in their high- and low-field ion mobilities as they experience the asymmetric waveform applied to the DMS cell. These mobilities are known to be influenced by the ions' structure, m/z, and charge distribution (i.e., resonance structures) within the ions themselves, as well as by the gas-phase environment of the DMS cell. While these associations have been developed over time through empirical observations, the exact role of ion structures or their interactions with clustering molecules remains generally unknown. In this study, that relationship is explored by observing the DMS behaviors of a series of tetraalkylammonium ions as a function of their structures and the gas-phase environment of the DMS cell. To support the DMS experiments, the basin-hopping search strategy was employed to identify candidate cluster structures for density functional theory treatment. More than a million cluster structures distributed across 72 different ion-molecule cluster systems were sampled to determine global minimum structures and cluster binding energies. This joint computational and experimental approach suggests that cluster geometry, in particular ion-molecule intermolecular separation, plays a critical role in DMS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".