Bibliographic record
Abstract
The COVID-19 pandemic has substantially increased the public awareness of indoor environments. Despite the global effort in reducing primary indoor air pollutants, especially emissions related to solid fuel combustion, there remains insufficient monitoring and understanding of secondary pollutant generation via indoor chemical reactions. In particular, the multiphase oxidation that occurs between airborne species and reactive material on indoor surfaces is an important contributor that can also impact occupant health and wellbeing. Transported from outside, ozone is the most important indoor oxidant. It rapidly reacts with unsaturated lipids that are commonly found on indoor surfaces contaminated by cooking oils and human skin lipids. However, such reactions have not been comprehensively characterized, especially in complex indoor environments. Here, I initially studied the heterogeneous ozonolysis of a pure representative lipid (triolein) in a controlled reactor. With mass spectral and quantitative NMR methods, our results indicate that triolein decays rapidly with ozone exposure, with stable secondary ozonides (SOZs) the major condensed-phase products. Known as the Criegee mechanism, the reaction products are strongly dependent on ambient relative humidity. Specifically, the SOZ molar yield peaks at ~80% under dry conditions, regardless of the ozone mixing ratio, whereas water vapor significantly reduces its formation and favors the release of volatile organic compounds (VOCs). This is due to water scavenging the Criegee Intermediates, and the resulting α-hydroxyhydroperoxides (α-HHPs) decompose into aldehydes and reactive H2O2. A kinetic multilayer model (KM-GAP) which implements this set of chemical reactions accurately simulates the yields of major products under indoor relevant conditions. After studying fundamental mechanisms, I characterized the chemical fate of commercial cooking oil on genuine indoor surfaces. While SOZs are the major products when oils are exposed to air, low-ozone dark locations lead to the slow formation of hydroperoxides, which cannot be explained by the Criegee mechanism. Additionally, indoor direct sunlight drives lipid peroxidation whose products are potential toxins. Overall, my indoor sampling studies indicate that autoxidation and/or photooxidation mechanisms of unsaturated oils also play an important role in indoor oxidative surface chemistry, depending on their rates relative to ozonolysis.
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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".