Evaluation of carrier gases for use in high-field asymmetric waveform ion mobility spectrometry
Bibliographic record
Abstract
Effects of carrier gas type (N2, O2, CO2, N2O, and SF6) on changes in the ratio of high- to low-field ion mobility, Kh/K, of cesium, gramicidin S, tetrahexylammonium, heptadecanoic acid, and aspartic acid in fields of up to 67 Td are presented. The theory of the mobility of ions at high E/N in different gases is discussed. Plots of Kh/K as a function of the ionic energy parameter, E/N, for the five ions in each of the gases were derived from experimental data collected using a high-field asymmetric waveform ion mobility spectrometer. The change in the ratio of high- to low-field ion mobility of cesium in carrier gases of O2 and N2 showed excellent agreement with literature values. The behavior of cesium in O2 and N2 is used to illustrate that the ratio Kh/K as a function of effective temperature is invariant with gas type as long as the well depth of the interaction potential significantly exceeds thermal energy. From these results, it appears that the well depth of the interaction potential of the heavier ions studied here, including gramicidin S, tetrahexylammonium, and heptadecanoic acid, with bath gases such as N2 and O2, is shallow relative to thermal energy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".