Determination of unreacted 2,4-toluene diisocyanate (2,4TDI) and 2,6-toluene diisocyanate (2,6TDI) in foams at ultratrace level by using HPLC-CIS-MS-MS
Bibliographic record
Abstract
Isocyanates can cause occupational asthma. By using available HPLC-UVF methods, isocyanates can be quantified only at levels above 1% of the Permissible Exposure Limits (PEL). Once sensitized, workers can react to concentrations below these limits of detection (LOD) making these methods insufficiently sensitive to adequately evaluate trace amounts of isocyanates present in air or in materials at safe levels for sensitized workers. This article describes a novel method for isocyanate analysis allowing the quantification of 2,4TDI and 2,6TDI monomers at very low concentrations using HPLC-CIS-MS-MS. The method's sensitivity increases with a decrease in the alkali radius. The LOD is 0.039 ng mL(-1) for 2,4TDI and 0.100 ng mL(-1) for 2,6TDI in solution when lithium is the alkali adduct, which is 20 times more sensitive than HPLC-UVF method. This new method allows determination in foam at levels of 0.078 ng g(-1) for 2,4TDI and 0.200 ng g(-1) for 2,6TDI respectively, for a 0.5 g foam sample. This is more than 100 times more sensitive than other methods for determining free monomers in solid materials. Analytical reproducibility and precision are better than 92% and 93% for both diisocyanate monomers. The use of HPLC-UVF conventional method failed to detect unreacted isocyanates in foam samples, but TDI monomers were quantified by HPLC-CIS-MS-MS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".