Pyrolysis-GC/MS calibration for environmental quantification of tire tread: Standards and marketplace averaged elastomer subunit profiles
Bibliographic record
Abstract
Pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) enables quantification of tire and road wear particles (TRWP) in environmental matrices, but method refinements are needed to account for elastomer subunit variations. Environmentally distributed elastomers are a composite of formulations from many tire manufacturers, which can be represented by specially prepared reference materials. Thus, this study analyzed cryogenically milled tire tread (CMTT) composite materials blended from United States and European Union market-representative tire mixtures to determine operationally defined styrene ( S t ) and 1,4-butadiene ( B t ) monomer subunit content fractions in synthetic rubber (SR) fractions. Bootstrap resampling with least squares optimization revealed similar B t (0.64–0.73) and S t (0.06–0.09) contents across market composites, though precision decreased with larger particle sizes. Calibration polymer solutions stored at 4 °C maintained stability for three months, with response ratio changes below 15 %. Although alternative internal standards were evaluated, structural similarity between target and calibration polymers proved essential for complex environmental matrices, with up to 20 % quantitation differences observed when using dissimilar standards. This study demonstrates that market-representative composite CMTT materials provide operationally-defined elastomer subunit profiles that account for commercial variability in tire formulations. This approach enables accurate environmental TRWP quantification without requiring individual elastomer characterization. • Market-weighted CMTT analysis reveals similar styrene (6–8 %) and butadiene (63–73 %) content across U.S. and EU treads. • Polymer calibration standards maintain stability when stored at 4 °C for three months with <15 % response changes. • Internal standard structural similarity is critical - up to 20 % bias observed with dissimilar standards.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".