Differential Mobility Spectrometry of Ketones in Air at Extreme Levels of Moisture
Bibliographic record
Abstract
Abstract The performance of a differential mobility spectrometer was characterized at ambient pressure and ten values of water vapor concentration, from 1.0 × 102 to 1.7 × 104 ppm using a homologous series of seven ketones from acetone to 2-dodecanone. Dispersion plots at 30 °C with separation fields from 35 to 123 Td exhibited increased alpha functions for the hydrated proton, protonated monomers, and proton bound dimers with increased moisture levels. Increases in the level of moisture were accompanied by decreased quantitative response with progressive suppression in the formation of the proton bound dimer first and then protonated monomer. Product ions for 2-octanone at 7 ppb were not observed above a moisture level of 4.0 × 103 ppm, establishing a limit for observation of analyte ion formation. The observation limit increased from 1.1 × 103 ppm for acetone to 5.7 × 103 ppm for 2-dodecanone. These findings demonstrate that ketones can be determined with a differential mobility spectrometry (DMS) analyzer near room temperature in the presence of elevated levels of moisture expected with the use of membrane inlets or headspace sampling of surface or ground waters. Moisture levels entering this DMS analyzer employed as an environmental monitor should be kept at 1.0 × 103 ppm or below and quantitative studies for individual ketones should be made at a fixed moisture level.
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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".