Ground tire rubber (GTR) surface modification using thiol-ene click reaction: Polystyrene grafting to modify a GTR/polystyrene (PS) blend
Bibliographic record
Abstract
In this work, a thiol-ene click reaction was used to graft polystyrene (PS) chains onto the surface of ethylene propylene diene monomer-based ground tire rubber (GTR). A thiol-terminated PS (11 kg mol −1 ) was selected, due to its commercial availability, to modify a postconsumer GTR. The resulting PS-grafted GTR (GTR- g-PS) particles were analyzed via Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS) to detect surface changes on treated samples. An increase in aliphatic and aromatic carbon atoms was observed by FTIR and XPS, while grafted material was observed by SEM on the GTR surface, confirming that grafting took place. Then, composites were prepared from these GTR- g-PS particles and from PS by solution evaporation, at 50 wt%. A significant increase in tensile (20%) and storage moduli (from 80% at 65°C to 510% at 95°C) was observed by dynamic mechanical thermal analysis when compared to composites prepared with untreated GTR. In addition, GTR samples showed improved thermal resistance, as attested by the shift in degradation temperature for 10% mass loss (from 400°C for GTR to 450°C for GTR- g-PS). This increases the possible range of processing temperature and service temperature (applications) for introduction of GTR in other polymer matrices.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".