Fast autoxidation of unsaturated lipid films on indoor surfaces
Bibliographic record
Abstract
Organic films containing unsaturated lipids are widespread, yet their oxidation pathways with associated impacts on contaminant lifetimes and human exposure remain poorly explored under indoor environmental conditions. This study demonstrates that UVA radiation and radical exposure drive rapid autoxidation of thin films of methyl linolenate (ML) and canola oil (which contains polyunsaturated triglycerides), primarily producing organic hydroperoxides. For ML films this fast chemistry occurs at the same rate under entirely dark, genuine indoor conditions as it does when the films are exposed to significantly higher •OH radicals in a flow reactor. Both •OH and organic radicals are detected within the oxidized films, propagating fast autoxidation in dark indoor environments with minimal sensitivity to the radical initiation rate. When mixed into the films, bisphenol A is hydroxylated, illustrating potential transformation pathways for toxic organic contaminants. This study uncovers insights into lipid autoxidation processes under environmental conditions and underscores their potential health impacts. Organic films containing unsaturated lipids are widespread, but their oxidation pathways remain poorly explored under indoor environmental conditions. Here, the authors demonstrate that UVA radiation and radical exposure drive rapid autoxidation of thin films of methyl linolenate and canola oil, primarily producing organic hydroperoxides. This autoxidation chemistry occurs under dark indoor conditions.
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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".