Acetoin is a precursor to diacetyl in e-cigarette liquids
Bibliographic record
Abstract
Use of the e-liquid flavourings diacetyl and acetyl propionyl has raised concerns that they might cause respiratory diseases amongst vapers. Product surveys show that these compounds, plus a less toxic alternative, acetoin, are widely used in e-liquids. We have investigated the chemistry of acetoin, acetyl propionyl and diacetyl in e-liquids. They are reactive, with concentrations falling substantially over time. Acetyl propionyl is the most reactive, diacetyl less so, and acetoin significantly more stable. Their reactivity is pH-enhanced when nicotine is present in the e-liquid. Of major concern, we found that acetoin generates diacetyl in e-liquids. We found diacetyl formation in all acetoin-containing e-liquids, but it is not an acetoin-contaminant. Diacetyl concentrations were proportional to acetoin content, grew over time, and formation was accelerated by nicotine. E-liquids stored for up to 18 months contained significant diacetyl, and reduced acetoin levels, showing that acetoin is a long-term diacetyl source. Other reaction pathways operate, and we advance mechanisms to explain this area of e-liquid chemistry. Acetoin use in e-liquids is an inevitable source of diacetyl exposure for e-cigarette users. Acetoin, acetyl propionyl and diacetyl are avoidable hazards for vapers, and we recommend e-liquid manufacturers move away from their use in e-liquid formulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".