Validation of a laboratory spray generation system and its use in a comparative study of hexamethylene diisocyanate (HDI) evaluation methods
Bibliographic record
Abstract
, respectively, with spatial homogeneity (RSD) of 5.8% and 16.5%. The particle-size distribution (MMAD) of 4 μm was measured using a cascade impactor and an electrical low-pressure impactor. The samples generated were used to correlate the amount of isocyanates collected with scanning electron microscope images of droplets on a filter. Three methods were compared to the reference method-an impinger with a backup glass fibre filter (GFF) and 1,2-methoxyphenylpiperazine (MP) based on ISO 16702/MDHS 25-in six generation experiments: (1) Swinnex cassette 13 mm GFF MP (MP-Swin); (2) closed-face cassette 37 mm GFF (end filter and inner walls) MP (MP-37); and (3) denuder and GFF dibutylamine (DBA) (ISO 17334-1 Asset). The analysis revealed clear trends regarding which sampler sections collected HDI (mainly in the vapor phase) or isocyanurate (exclusively in the particulate phase). The study found no significant bias between the tested methods (MP-Swin, MP-37, and Asset) and the reference method (impinger) for both HDI monomer and isocyanurate. The three tested methods showed limits of agreement beyond the acceptable range of ±30% (95% confidence interval), largely due to data variability, though MP-Swin and MP-37 exhibited lower variability than Asset. The results will be further evaluated in a real-world environment where similar clear coats are used.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".