Synthesis, properties and degradation of polyisobutylene–polyester graft copolymers
Bibliographic record
Abstract
Abstract The development of copolymers is a promising approach for combining the favorable properties of two polymers and obtaining new properties of the combination. In this work, graft copolymers of polyisobutylene (PIB) and polycaprolactone or poly(d,l‐lactide) were synthesized and studied. Amine‐terminated polyesters were synthesized and were grafted onto an activated PIB backbone synthesized from butyl rubber, a copolymer of isobutylene and 2 mol% isoprene. The polyester content was tuned from 15 to 44 wt% by varying the molar mass of the polyester blocks and the number of molar equivalents used in the grafting reaction. The graft copolymers with higher polyester content underwent nanoscale phase separation, as demonstrated by differential scanning calorimetry and atomic force microscopy imaging. This was found to provide enhanced mechanical properties such as increased tensile strength and Young's modulus relative to the starting rubber or physical blends. Despite the significant polyester content of the graft copolymers and the susceptibility of the polyesters to degradation, the graft copolymers underwent negligible mass loss in 5 mol L−1 NaOH over a period of eight weeks. These results suggest that polyesters can be incorporated into PIB to tune and enhance its properties, while maintaining high chemical stability. © 2016 Society of Chemical Industry
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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".