On site comparison of the OSHA 42, Asset EZ4-NCO, Iso-Chek, DAN and CIP10 methods for measuring toluene diisocyanate (TDI) at a polyurethane foam factory
Bibliographic record
Abstract
Because of the semi-volatile nature of diisocyanates (being airborne in both physical vapor and particulate phases), their high reactivity and low occupational exposure limits, diisocyanate exposure evaluation has been challenging for industrial hygienists and laboratories. The objective of this study was to compare the toluene diisocyanate (2,4 and 2,6 isomers, TDI) concentration measured by five methods in a flexible polyurethane foam factory using different collection or derivatization approaches. The methods used were: OSHA 42 modified (filter, 1-(2-pyridyl)piperazine) (OSHA), Asset EZ4-NCO (denuder and filter, dibutylamine) (Asset), Iso-Chek (double-filter, 9-(N-methylaminomethyl) anthracene and 1,2-methoxyphenylpiperazine), DAN (filter, 1,8-diaminonaphthalene), and CIP10 (centrifugation, 1,2-methoxyphenylpiperazine). Particle real-time monitoring for concentration and size distribution was performed in parallel to improve the understanding of the potential bias between methods. The comparison study was performed over 3 days, providing 18 replicates for each of the 5 methods. Isocyanate concentrations collected for each sampling method were compared using linear mixed effect modeling. Compared to OSHA, which yielded the highest concentrations overall, the Asset and DAN methods provided the smallest biases (-29% (95% CI [-52;-6]) and -45% (95% CI [-67;-23]), respectively), while the CIP10 and Iso-Chek methods provided the largest biases (-82% (95% CI [-105;-66]) and -96% (95% CI [-118;-75]), respectively). The substantial bias of Iso-Chek and CIP10 seemed to be explained by the predominance of TDI in the form of sub-micron particles that were inadequately captured by these two methods due to their sampling principle, which are particle filtration without derivatizing agent and centrifugation respectively. Asset and DAN performance seemed to decrease as the sampling time increased. While DAN's bias could be related to a reagent deficiency on the filter, the disparities between OSHA and Asset, both considered as reference methods, highlight the fact that the mechanisms of collection, derivation and extraction do not seem to be completely controlled. Finally, an upward trend has been observed between concentrations of particles below 300 nm in size and concentration levels of TDI. It has also been observed that TDI levels increased with the TDI foam index produced at the facility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".