Impact of Physical Properties on Ozone Removal by Several Porous Materials
Bibliographic record
Abstract
Models of reactive uptake of ozone in indoor environments generally describe materials through aerial (horizontal) projections of surface area, a potentially limiting assumption for porous materials. We investigated the effect of changing porosity/pore size, material thickness, and chamber fluid mechanic conditions on the reactive uptake of ozone to five materials: two cellulose filter papers, two cementitious materials, and an activated carbon cloth. Results include (1) material porosity and pore size distributions, (2) effective diffusion coefficients for ozone in materials, and (3) material-ozone deposition velocities and reaction probabilities. At small length scales (0.02-0.16 cm) increasing thickness caused increases in estimated reaction probabilities from 1 × 10(-6) to 5 × 10(-6) for one type of filter paper and from 1 × 10(-6) to 1 × 10(-5) for a second type of filter paper, an effect not observed for materials tested at larger thicknesses. For high porosity materials, increasing chamber transport-limited deposition velocities resulted in increases in reaction probabilities by factors of 1.4-2.0. The impact of physical properties and transport effects on values of the Thiele modulus, ranging across all materials from 0.03 to 13, is discussed in terms of the challenges in estimating reaction probabilities to porous materials in scenarios relevant to indoor environments.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".