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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".