Modeling Ozone Removal to Indoor Materials, Including the Effects of Porosity, Pore Diameter, and Thickness
Bibliographic record
Abstract
We develop an ozone transport and reaction model to determine reaction probabilities and assess the importance of physical properties such as porosity, pore diameter, and material thickness on reactive uptake of ozone to five materials. The one-dimensional model accounts for molecular diffusion from bulk air to the air-material interface, reaction at the interface, and diffusive transport and reaction through material pore volumes. Material-ozone reaction probabilities that account for internal transport and internal pore area, γ(ipa), are determined by a minimization of residuals between predicted and experimentally derived ozone concentrations. Values of γ(ipa) are generally less than effective reaction probabilities (γ(eff)) determined previously, likely because of the inclusion of diffusion into substrates and reaction with internal surface area (rather than the use of the horizontally projected external material areas). Estimates of γ(ipa) average 1 × 10(-7), 2 × 10(-7), 4 × 10(-5), 2 × 10(-5), and 4 × 10(-7) for two types of cellulose paper, pervious pavement, Portland cement concrete, and an activated carbon cloth, respectively. The transport and reaction model developed here accounts for observed differences in ozone removal to varying thicknesses of the cellulose paper, and estimates a near constant γ(ipa) as material thickness increases from 0.02 to 0.16 cm.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".