The Protective Effects of CO<sub>2</sub> on Fragile Ions in Differential Mobility Spectrometry
Bibliographic record
Abstract
We explore the protective effects of adding CO 2 to the N 2 carrier gas when we conduct differential mobility spectrometry (DMS) analysis of fragile ions. A selection of fragile analytes of varying chemistries were chosen from our lab inventory and include protonated glycine, methylbenzyl ammonium, methoxybenzylpyridinium, the protonated 2-pentanone dimer, deprotonated GenX (a perfluoroalkyl substance; PFAS), and deprotonated trifluoroacetic acid. By raising the separation voltage or the carrier gas temperature, conditions were set to induce fragmentation of the analyte ions within the DMS collision cell. Subsequently introducing CO 2 into the N 2 carrier gas at concentrations ranging from 10 – 70% mitigated ion fragmentation and resulted in signal intensity gains of multiple orders of magnitude. Interestingly, stabilization of the fragile ions sometimes occurred without introducing significant ionogram peak shifts (i.e., shifts of less than 1 V), indicating that these ions exhibit relatively weak interactions with the CO 2 modifier. Electronic structure calculations yield Gibbs binding energies of ca . – 1 kJ mol –1 under the DMS conditions employed, further supporting the hypothesis that dynamic ion-CO 2 clustering is not the root cause of the observed protective effect. The addition of CO 2 was also found to stabilize noncovalently bound dimers, presumably generated at the ionization source. These results indicate that, in these examples, CO 2 cools the ions in the energetic DMS environment via momentum transfer and energy partitioning, and that introducing CO 2 into DMS gas mixtures could enable the stabilization, separation, and analysis of fragile analytes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".