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.
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 | 0.000 | 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".