Determination of Diphenylamine Antioxidants in Wastewater/Biosolids and Sediment
Bibliographic record
Abstract
Diphenylamine derivatives are widely used as antioxidant additives in vehicle engine oils, commercial/industrial lubricants, and products composed of rubber. Their presence in the environment results primarily from human activity, and there are no known environmental measurements of these substances in any media. In this study, 17 components of three diphenylamine substances, 2-propanone, reaction products with diphenylamine (PREPOD), 1,4-benzenediamine, N, N ′-mixed phenyl and tolyl derivatives (BENPAT), and benzenamine, N -phenyl-, reaction products with styrene and 2,4,4-trimethylpentene (BNST), were identified and quantified from their associated technical mixtures by Fourier transform ion cyclotron resonance mass spectrometry and flame ionization detection, and a method was developed for the determination of their presence in wastewater, biosolids, and sediment samples using gas chromatography-tandem triple-quadrupole mass spectrometry. The methods were applied to the analysis of influent, effluent, and biosolid samples, and the sums of all of the diphenylamine derivative components were 58.3–72 ng L –1, 1.48–27.1 ng L –1, and 226–1202 ng (g of dry weight) −1, respectively. Nine sediment samples collected in Ontario, Canada, contained the sum concentrations of the target compounds ranging from 1 to 1000 ng (g of dry weight) −1 . To the best of our knowledge, this is the first work to report PREPOD, BENPAT, and BNST compounds in environmental samples.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".