Use of the Novel Derivatizing Agent 1,8-Diaminonapthalene With the CIP10 Sampler to Measure 4,4’-Methylene Diphenyl Diisocyanate Atmospheres
Bibliographic record
Abstract
DAN is a novel derivatizing agent for isocyanate sampling which targets total reactive isocyanate group (TRIG). Field evaluations have been conducted for 4,4'-Methylene diphenyl diisocyanate (MDI) sampling using DAN as the derivatizing agent in a CIP10. The perimidone formed, selective of TRIG, was analyzed in laboratory by liquid chromatography coupled to tandem mass spectrometry. Workplaces using MDI-based polyurethane spray foam and MDI wood product binder were studied. Each study compared the data obtained between the CIP10/DAN, the CIP10/MP, and a reference method. As a first evaluation, the CIP10/DAN sampled simulated MDI spray foam atmosphere in parallel with impingers (reference method) and CIP10/MP. The mean of the TRIG concentration values measured was 16% (95% confidence interval [CI], [1, 31]) higher for the CIP10/MP samples as compared to the impinger samples, while the mean TRIG concentrations found were 98% (95% CI [63, 133]) higher for the CIP10/DAN samples as compared to the impinger samples. For sampling done in a real workplace using MDI-based spray foam, the CIP10/DAN method showed lower results than the CIP10/MP method. The presence of hygroscopic DMSO used in the DAN approach is a limitation and may prevent obtaining accurate results in the spray foam atmosphere. CIP10/DAN was also studied in MDI wood product binder atmosphere. Mean TRIG concentrations found were 80% (95% CI [51, 110]) and 79% (95% CI [50, 108]) lower for the CIP10/MP and CIP10/DAN method, respectively, as compared to the reference method (a 13-mm filter coated with MP). In this case, the CIP10 may have reached its limit in this last evaluation where vapors and particles below 1 µm were not collected as efficiently as they were with the reference method.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".