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 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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".