Indoor boundary layer chemistry modeling
Bibliographic record
Abstract
Ozone (O3) chemistry is thought to dominate the oxidation of indoor surfaces. We consider the hypothesis that reactions taking place within indoor boundary layers result in greater than anticipated hydroxyl radical (OH) deposition rates. We develop models that account for boundary layer mass-transfer phenomena, O3-terpene chemistry and OH formation, removal, and deposition; we solve these analytically and by applying numerical methods. For an O3-limonene system, we find that OH flux to a surface with an O3 reaction probability of 10−8 is 4.3 × 10−5 molec/(cm2 s) which is about 10 times greater than predicted by a traditional boundary layer theory. At very low air exchange rates the OH surface flux can be as much as 10% of that for O3. This effect becomes less pronounced for more O3-reactive surfaces. Turbulence intensity does not strongly influence the OH concentration gradient except for surfaces with an O3 reaction probability >10−4. Although the O3 flux dominates OH flux under most conditions, OH flux can be responsible for as much as 10% of total oxidant uptake to otherwise low-reactivity surfaces. Further, OH chemistry differs from that for ozone; therefore, its deposition is important in understanding the chemical evolution of some indoor surfaces and surface films.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".