Effects of airflow on VOC emissions from "wet" coating materials: Experimental measurements and numerical simulation
Bibliographic record
Abstract
The impact of airflow on volatile organic compound (VOC) emissions from "wet" materials has long been noticed. However, a comprehensive mass transfer model that can predict such an impact has not been reported. The objective of this research was to fill that gap. First, we measured the VOC emissions of "wet" coating materials (a decane and a wood stain) using a small-scale (0.4 m3) and a full-scale (55 m3) environmental chamber under different airflow conditions. A numerical model was then developed to numerically simulate the "wet" material emissions. The model considers VOC mass transfer in the air and material-air interface, diffusion in the material film, and diffusion in the substrate. The results from experimental measurements indicate that local airflow has impacts on "wet" material emissions, especially during the early stage. The numerical model developed can predict emissions under different flow conditions with reasonable accuracy. Numerical simulations have further confirmed that the emissions from "wet" materials applied to an absorptive substrate are dominated by evaporation at the beginning and internal diffusion afterwards, which had been speculated based on previous experimental data. The numerical model has the potential to simulate "wet" material emissions in actual building environments based solely on the small-scale chamber data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".