Physicochemical properties of tire-derived para-phenylenediamine quinones - A comparison of experimental and computational approaches
Bibliographic record
Abstract
Para-phenylenediamine (PPD) compounds are added to tire rubber at percent levels to sacrificially react with oxidants for prolonged service life. Recently, the PPD transformation product N-(1,3-dimethylbutyl)-N’-phenyl-p-phenylenediamine quinone (6PPDQ) has been identified in roadway runoff as a potent toxicant for coho salmon (Oncorhynchus kisutch). As 6PPD may be phased out in favour of alternative PPDs, understanding the physicochemical properties of their corresponding quinones is important for predicting their environmental fate, distribution, and toxicity. Here, we present an experimentally determined log KOW for 6PPDQ (4.0 ± 0.2) as well as water solubility values for 6PPDQ and five structural analogues (3.2 – 170 µg/L). The water solubilities were several orders of magnitude lower than those predicted by EPI Suite and OPERA, popular Quantitative Structure Activity Relationship (QSAR) programs. We also report octanol-water and air-water partition ratios for PPDQs using Density Functional Theory (DFT) and QSAR approaches. Both methods provided similar rank ordering of compounds. We found that DFT tends to underestimate log KOW values, while QSAR models provide a better agreement with experimental results. Conversely, QSAR models provided poorer predictions of log KAW values than DFT. We discuss the strengths and limitations of both computational approaches, the need for more experimentally derived values, and caution researchers interpreting predicted physicochemical properties, particularly for emerging contaminants for which QSARs may be insufficiently parameterized.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".