Development of a Method for Preservation, Extraction, and Quantitation of 4,4’-Methylenedianiline in Soils of Varied Texture and Organic Matter Content
Bibliographic record
Abstract
Despite the reactivity and degradability of 4,4’-methylenedianiline (4,4’-MDA) with organic matter in soils, the improvement of Brunet et al. method makes it possible to monitor the substance in the environment. In fact, 4,4’-MDA can be found in soils, used for agriculture for example, if 4,4’-methylene diphenyl diisocyanate (4,4’-MDI) based fertilizers and pesticides are used. The possible degradation of 4,4’-MDI-based polyurethane could result in formation of 4,4’-MDA. To have a soil where 4,4’-MDA could be uniformly distributed, an impregnation method was developed. The method was validated using 1 g of the impregnated soil ranging 2,4% to 10% of organic matter, and the addition of both internal and surrogate standard to correct any losses of the substance over the different steps of the method. The limit of detection (LOD) and limit of quantification (LOQ) for the determination of 4,4’-MDA in soil are 0.107 and 0.358 μg/kg (dry weight), respectively, with a dynamic range between 5 and 250 μg/kg. For the intra-day precision, the result was 4,28% and for the inter-day, it was 9,32%. The accuracy was 96,4% and the total recovery obtained was 82.02%. Moreover, the stability of 4,4’-MDA was demonstrated in dry soil samples or in samples completely immersed in methanol over a 14-day period.
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 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 | 0.001 |
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".