Glass Chamber Method for Screening of 4,4′-MDI and TCPP Emissions from Foam Joint Sealant
Bibliographic record
Abstract
One-component foam joint sealant is widely used to seal air leaks by homeowners. Various chemicals can be emitted from a foam joint sealant. The goal of this study was to develop a glass chamber method to examine the emissions of 4,4′-methylenediphenyl diisocyanate (4,4′-MDI) and tris(2-chloroisopropyl)phosphate (TCPP) from foam joint sealants. The concentrations of 4,4′-MDI and TCPP were measured during a 24-h chamber test that involved a 3-L chamber operated at 40°C, 20 % relative humidity (RH), 22.2 air changes per hour, and 2 sampling media (glass filter coated with 9-methylaminomethyl anthracene for 4,4′-MDI and sorbent tube filled with Tenax® TA for TCPP). The 4,4′-MDI concentration peaked within 11 min and decayed to below the lowest limit of quantification within 1 h. The TCPP concentration reached a maximum value at approximately 4 h and decayed relatively slowly or stayed almost constant afterward. The 4,4′-MDI concentration after applying foam joint sealant to all the windows of a small house was predicted to be much lower than the reference value of 0.6 μg/m3 by the U.S. Environmental Protection Agency. Conversely, the higher predicted concentrations of TCPP than the measured indoor levels may imply the potential of foam joint sealants as an important source of TCPP in homes. In addition, the test with 4,4′-MDI for method optimization showed that the effect of environmental factors (temperature and RH) as well as the sink effect by interior surfaces could be significant. When a test method is standardized for 4,4′-MDI emissions, these influential factors should be investigated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".