Recyclable and Self-Healing Natural Rubber Vitrimers from Anhydride-Epoxy Exchangeable Covalent Bonds
Bibliographic record
Abstract
Dynamic covalent networks (DCNs) contain exchangeable covalent bonds that can undergo dynamic structural changes under external stimuli. Employment of DCNs in elastomers instead of static cross-links provides a pathway for designing reprocessable and recyclable rubbers. Vitrimers are examples of DCNs that utilize associative covalent bond-exchanging chemistry to keep the total number of cross-links constant, making them recyclable, reprocessable, and self-healing. This study primarily investigated the design of a natural rubber (NR) vitrimer via anhydride-epoxy dynamic cross-linking using a scalable process and benign reagents, such as maleic anhydride (MA) or bisphenol A diglycidyl ether (DGEBA), which can be reprocessed and self-heal with heat stimuli. Reactive melt mixing was employed to synthesize the vitrimers, and the reaction success was confirmed by using various chemical analysis approaches. The rubber vitrimer possesses a high activation energy (139.7 kJ/mol) and low freezing topology temperature (65 °C), demonstrating a robust exchange network. The NR vitrimers could undergo multiple rounds of reprocessing, unlike peroxide- or sulfur-cured NR, due to their robust dynamical cross-linking networks generated by the adaptable covalent bonds. Moreover, the NR vitrimers exhibited unprecedented self-healing capabilities to maintain their original mechanical characteristics. The recyclability of NR is a significant achievement in reducing post-consumer rubber waste and virgin material utilization. The self-healing functionality is also appealing in applications that require on-site assembly or repair as well as to help increase the lifespan of the elastomers.
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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 | 0.001 |
| 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".