A nanogold sensor test for tire wear chemicals based on the plasmon ruler approach
Bibliographic record
Abstract
The release of tire wear substances in the environment is raising concerns about potential impacts on aquatic ecosystems. The purpose of this study was to develop a quick and inexpensive screening test for the following tire wear substances: 6-phenylphenyldiamine quinone (6-PPD quinone), hexamethoxymethylmelamine (HMMM), 1-3-diphenylguanidine (1,3-DPG), and melamine. A dual strategy consisting of nanogold (nAu) signal intensity and the plasmonic ruler principle was used based on the spectral shift from the unaggregated free-form nAu from 525 nm to aggregated nAu at higher wavelengths. The shift in resonance corresponded to the relative sizes of the tire wear substances at the surface of nAu: 6-PPD (560 nm), HMMM (590 nm), 1,3-DPG (620 nm), and melamine (660 nm) in a concentration-dependent manner. When present in mixtures, a large indiscriminate band between 550 and 660 nm with a maximum corresponding to the mean intermolecular distance of 0.43 nm from the tested individual substances suggests that all compounds indiscriminately interacted at the surface of nAu. An internal calibration methodology was developed for mixtures and biological extracts from mussels and biofilms and revealed a proportional increase in absorbance at the corresponding resonance line for each test compound. Application of this simple and quick methodology revealed the increased presence of melamine and HMMM compounds in mussels and biofilms collected at urban sites (downstream city, road runoffs), respectively. The data also showed that treated municipal effluent decreased somewhat melamine levels in mussels.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".