Thermoplastic Silicone Elastomers through Self-Association of Pendant Coumarin Groups
Bibliographic record
Abstract
Although there are many benefits associated with thermoplastic elastomeric silicones, very few examples exist: silicone elastomers are normally thermoset materials. We have discovered that the simple incorporation of coumarin groups on linear silicone polymer backbones creates physical silicone polymeric networks that exhibit thermoplastic elastomeric properties in the absence of covalent cross-links. A range of materials was prepared by incorporating four different concentrations of coumarin along the silicone backbone using thermal azide/alkyne cycloaddition reactions: higher coumarin concentrations lead to more tightly cross-linked, higher modulus materials. Intermediate properties could be obtained by mixing silicones with different coumarin loadings in the melt. Physical cross-links arise from 1:1 coumarin complexes. As a consequence, it is possible to reduce cross-link density by adding silicones bearing a single coumarin to an elastomer. The physical interactions between coumarin-triazoles on the silicone polymers could be temporarily overcome thermally as shown by tensile, rheometry and thermal remolding experiments. The simple expedient of grafting coumarin groups, which cross-link reversibly through head-to-tail π-stacking, to silicone chains allows one to tailor the mechanical properties of these thermoplastic elastomers, enhancing their utility.
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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) | 0.000 | 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) | 0.001 | 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".