Implementation and evaluation of an analytical method for a novel derivatizing agent to measure 4,4'-methylene diphenyl diisocyanate atmospheres
Bibliographic record
Abstract
Accurate measurement of 4,4'-methylene diphenyl diisocyanate (MDI) atmospheres is a challenge since the molecule is both chemically reactive and likely to be present in aerosol form when heated and sprayed because of its low vapor pressure. Meeting this challenge requires optimizing both the sampling device used and the derivatization agent employed to stabilize the isocyanate functional group. This study describes the use of a novel derivatization reagent for isocyanate sampling to address the challenge of MDI aerosol exposure sampling. Like most conventional derivatizing agents for isocyanates, 1,8-diaminonapthalene (DAN) reacts with isocyanate functional groups to form a urea. However, unlike other isocyanate derivatizing agents, the sample workup procedure with DAN includes a second step which yields a single analyte molecule, perimidone, for each isocyanate group. This feature gives DAN the unique ability to assess exposure to "total reactive isocyanate group" (TRIG). The analytical method implemented to quantitate the perimidone uses liquid chromatography coupled with tandem mass spectrometry. Positive mode ionization led to LOD and LOQ of 10 ng/mL and 34 ng/mL, respectively. The dynamic range was from 50-2000 ng/mL (with R(2) ≥ 0.990), which corresponds to TRIG concentrations in air from 0.07-3.04 µg/m(3), assuming 60 min of sampling at 10 L/min (based on use of the CIP-10M sampler). The intra-day and inter-day analytical precisions were <4% for all of the concentration levels tested, and the accuracy was within an appropriate range of 98 ± 2%. Minimal matrix effect was observed, and a total recovery of 109% was obtained. The approach seems to be promising for TRIG measurements and further work is planned to establish DAN method behavior in samplers used for workplace monitoring.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".