Development of a method for extraction and determination of 4,4′-methylenedianiline in soils by solid-phase extraction and UPLC-MS-MS
Bibliographic record
Abstract
The 4,4′-methylenedianiline (MDA) substance is an important chemical intermediate which is produced in very large tonnages, the majority of which is consumed in its conversion to 4,4′-methylenediphenyl diisocyanate (MDI). The MDA substance has known adverse effects which can be associated with human and environmental exposure. The growing number and volume of applications of polyurethane formulations containing MDI in the open environment and in agriculture has led to increased concern about indirect exposure to MDA in the environment, where it may occur as a potential degradation product of these polyurethane materials. This method employs ultra performance liquid chromatography coupled to tandem mass spectrometry. A recovery of (101.1 ± 5.2) % of MDA was demonstrated for samples prepared by spiking known amounts of MDA to a representative sandy loam surface soil. The overall method was adjusted to a deliver a dynamic MDA detection range from 5 to 250 μg/kg MDA load (dry wt.) in soils. The accuracy of the method was evaluated at 87%, while intra- and inter-day precision were 9% and 8%, respectively. When coupled with an integrated soil sampling strategy and a solid-liquid extraction protocol validated across a wide variety of soil types, the developed method will prove a powerful tool for definitively quantifying the occurrence (or absence) of MDA in both targeted and exploratory soil monitoring programs.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.002 |
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".