31 Development and Characterization of a Generation System for Airborne Diisocyanates
Bibliographic record
Abstract
Abstract Diisocyanates are fast-reacting semi-volatile compounds and one of the main components in the production of polyurethane-based materials. They are known as a predominant causal agent of occupational asthma. Discrepancies within well-documented and validated methods were observed and factual explanations of these discrepancies are not yet available. A more in-depth investigation of isocyanate measurement methods' efficiencies should be undertaken. The objective of this study was to develop a system capable of generating diisocyanate vapor and fine particle atmospheres with control of parameters such as concentration levels and vapor/particle partitioning. Two generation approaches were used: nebulization and heating. A controlled air flow (RH and T°) was combined to the generator's effluent in a mixing chamber and transferred, downstream, to an exposure chamber. A splitter collected eight samples simultaneously. The isocyanate measurement method used a glass fibre filter with 9-methylaminomethyl-anthracene (GF+MAMA). The particles were analyzed using a particle analyzer (Fidas Frog) and the particle size distribution of the isocyanates was obtained using a Marple impactor. Diphenylmethane-4,4'-diisocyanate (MDI) was generated with both generation approaches. The system was able to generate stable concentrations over time at different levels (5 to 50 µg/m3). Intratest variability was lower than 10% (RSD). By cross-referencing the data obtained by the particle counter and the Marple impactors, it was possible to document the particle-size distribution and vapor/particle partitioning of the MDI generated within the parameters used. The versatility of this system makes it a highly promising tool for assessing the performance of different diisocyanate measurement methods.
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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) | 0.000 | 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".