Sorptive Interactions between VOCs and Indoor Materials
Bibliographic record
Abstract
This study was carried out using various materials (carpet, gypsum board, upholstery, vinyl and wood flooring, acoustic tiles, and fruit) that were exposed to eight gaseous volatile organic compounds (VOCs) (isopropanol, MTBE, cyclohexane, toluene, ethylbenzene, tetrachloroethene, 1,2-dichlorobenzene, and 1,2,4-trichlorobenzene) in electro-polished stainless-steel chambers. Dynamic responses in VOC concentrations were used to determine linear adsorption and desorption rate coefficients and equilibrium partition coefficients. A linear adsorption/desorption model was used to effectively describe the interactions between VOCs and indoor surface materials for short-term source events (10 h). Relationships between sorption parameters and chemical vapor pressure and the octanol-air partition coefficient were observed. Carpet was identified as the most significant sorptive sink for non-polar VOCs. Virgin gypsum board was observed to be a significant sink for highly polar VOCs. Sorptive interactions between non-polar VOCs and indoor materials were not affected by variations in relative humidity. However, increases in relative humidity were observed to increase the degree of sorption of isopropanol to carpet.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".