Functional self‐healable <scp>EVA</scp> elastomers based on reversible covalent networks: A potential new class of epoxy‐based specialty adhesives
Bibliographic record
Abstract
Abstract Multifunctional elastomers have gained tremendous attention in the material research community. In this study, an epoxy functionalized elastomer poly(ethylene‐ co ‐vinyl acetate‐ co ‐glycidyl methacrylate) (EVA‐GMA) that is commercially available was modified with dynamic covalent chemistry to make it self‐healable and recyclable, as well as to investigate its adhesive properties. EVA‐GMA was modified to a furfuryl‐appended diene elastomer (FA‐EVA‐GMA) and subsequently cross‐linked with bifunctional 1,2,4‐triazoline‐3,5‐dione (bis‐TAD) and bismaleimide (BMI) derivatives via electrophilic substitution (ES) and Diels‐Alder (DA) chemistry, respectively. The ES modification of the elastomer was ambiently completed using bis‐TAD, whereas its maleimide modification required elevated conditions (65 °C) with a longer time of 24 h. The tensile study showed a remarkable improvement in the mechanical strength upon cross‐linking the elastomers. The differential scanning calorimetry (DSC) analysis elucidated the thermoreversible characteristics of both the ES and DA‐derived networks, showing the cleavage of ES and DA conjugates at 135 °C (retro‐ES) and 140 °C (retro‐DA), respectively. The cross‐linked elastomers exhibited significant self‐healing characteristics (with a healing efficiency of ≈ 88%) and monitored using an optical microscope and tensile analysis. Interestingly, the bis‐TAD‐derived and bismaleimide functionalized EVA‐elastomer showed excellent adhesive properties toward the metal surfaces, as analyzed via lap shear test.
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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".