A Colorimetric Method for Naked-eye Detection of 6PPD in Rubber Products and Wastes
Bibliographic record
Abstract
N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) has received increasing attention due to its ubiquitous environmental occurrence and the extreme aquatic toxicity of its quinone oxidation product. Given 6PPD's application as an antioxidant in a wide array of rubber products and wastes, cost-effective measurement of 6PPD is important for product and waste management. We herein developed a naked-eye colorimetric method for the rapid measurement (<10 min) of 6PPD in rubber products and wastes with low cost (<$1 per sample). The inspiration for this method stems from the observation of the formation of red-colored products when 6PPD is exposed to oxidants, at concentrations as low as 0.65 mg/L. The remarkable selectivity of the method was evaluated by 15 other structurally diverse phenols and anilines. LC-UV and mass spectrometry results corroborated N-1,3-dimethyl butyl-N’-phenyl quinone diamine (6QDI) as the primary oxidation product. Interestingly, we discovered that the protonated form of 6QDI, rather than its neutral counterpart, was the red-colored reaction product with λmax = 490 nm. We further validated the method by applying it to the measurement of 6PPD in a wide array of rubber products and wastes, with 6PPD detected in 22 out of 50 samples. Strong agreement was observed between the colorimetric method and LC-MS measurements, with the highest concentrations detected in car tire rubber. In summary, this study introduces a rapid and cost-effective approach for rapid screening of 6PPD in rubber products and wastes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".