Quantitative determination of hexamethylene diisocyanate (HDI), 2,4-toluene diisocyanate (2,4-TDI) and 2,6-toluene diisocyanate (2,6-TDI) monomers at ppt levels in air by alkaline adduct coordination ionspray tandem mass spectrometry
Bibliographic record
Abstract
Occupational exposures to isocyanates can lead to occupational asthma. Once sensitized, some workers could react to isocyanate monomers at concentrations below 1% of the Permissible Exposure Limit of 5 ppb in air. Currently available methods are not sufficiently sensitive to adequately evaluate isocyanates present at these levels in workplace air. This article describes a novel method for isocyanate determination allowing the ultratrace quantification in workplace air of hexamethylene diisocyanate, 2,4-toluene diisocyanate and 2,6-toluene diisocyanate monomers. Sampling is performed during a complete workshift at a flow rate of 1 L min(-1) with a cassette containing a 1-(2-methoxyphenyl)piperazine-impregnated 25 mm filter. Analysis is performed using liquid chromatography hyphenated with coordination ionspray tandem mass spectrometry. The analytical method's linearity was measured for a concentration range varying from the limit of detection of 0.04-0.13 ng mL(-1), depending on the monomer, up to approximately 32 ng mL(-1) for every isocyanate monomer, all with correlation coefficients (R(2)) greater than 0.999. The analytical method's lower limit of quantification combined with an adapted sampling strategy allow the quantification of isocyanate monomers down to 0.04 ppt for an 8 h work shift when a lithium adduct is used, which is more than 300 times lower than the most sensitive method currently available. This novel method can be used to confirm the very low level of isocyanate monomers for the safe reassignment of sensitized workers and it is also useful for charting the isocyanate dispersion tail in workplace environments.
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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 | 0.000 |
| 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 | 0.000 |
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 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".