Investigation of the competitive rate of derivatization of several secondary amines with phenylisocyanate (PHI), hexamethylene-1,6-diisocyanate (HDI), 4,4′-methylenebis(phenyl isocyanate) (MDI) and toluene diisocyanate (TDI) in liquid medium
Bibliographic record
Abstract
The stabilization of the isocyanate (NCO) groups during workplace sampling is necessary for their subsequent laboratory analysis. Most derivatization reagents are secondary amines. By carrying out a test in which two secondary amines are added to an isocyanate, the relative rates of these reactions can be evaluated. This evaluation is known for a monoisocyanate, phenylisocyanate (PHI), but is being developed for diisocyanates. This study deals with the relative reactivity (RR) of four diisocyanates: hexamethylene 1,6-diisocyanate (HDI), 4,4'-methylenebis(phenyl isocyanate) (MDI), and the ortho and para isomers of toluene diisocyanate (TDI) in addition to PHI, with four secondary amines: 1-(2-methoxyphenyl)piperazine (MOPIP), 9-(N-methylaminomethyl)anthracene (MAMA), 1-(9-anthracenylmethyl)piperazine (MAP), and dibutylamine (DBA). These competitive derivatization reactions are studied in three reaction solvents, namely acetonitrile, toluene, and acetonitrile doped with water (1% v/v). The results show that the order of reactivity, which doesn't change with the isocyanate as well as with the solvent used, is the following: DBA > MAP > MOPIP > MAMA. The relative difference in reactivity is a function of both the isocyanate and the solvent used. Hindered aromatic diisocyanates (TDI and MDI) show a greater difference in reactivity with the derivatization agents. These differences in reactivity are also modified by the solvent used. For example, larger differences are observed in acetonitrile than in toluene, but the introduction of water to acetonitrile, which does not affect the reaction yield, makes these differences smaller.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".