Self‐Healing Biobased Thermoreversible Polymer Networks by Photo‐Diels‐Alder Chemistry
Bibliographic record
Abstract
ABSTRACT Poly(furfuryl methacrylate) (poly(FMA)) homopolymers with controlled molecular weights made via ICAR ATRP undergo photo‐crosslinking (λ = 254 nm) with 1,1′‐(methylenedi‐4,1‐phenylene)bismaleimide (BM) by Diels‐Alder (DA) chemistry, and are compared to the same DA reaction under non‐UV initiated, ambient conditions (RT‐DA). FTIR and DSC analysis confirm DA adduct formation and the retro‐DA reaction. Co‐ and terpolymers of FMA with C13MA (alkyl methacrylate with average side length = 13) and isobornyl methacrylate (IBOMA) are prepared to enhance mechanical properties. Poly(C13MA ‐co‐ FMA) samples are used to study curing condition effects and BM loading on crosslinking density and thermal behavior. Varying BM loading (0.05–0.2 BM: FMA) in samples cured by photo‐DA and RT‐DA, UV cured samples showing higher gel content (50% versus 25% for 0.2 BM loading) and two endothermic peaks in the first heating run, unlike RT cured samples which display a single peak. Subsequently, poly(C13MA ‐co‐ IBOMA ‐co‐ FMA) are crosslinked with BM via photo‐DA with resulting networks indicating self‐healing/recyclability. FTIR and DSC results confirm DA and retro‐DA reactions, while scratched samples of crosslinked terpolymers exhibit self‐healing upon heating. Microscopic images show complete scratch healing in just 20 min due to furan‐maleimide adduct formation, facilitated by the presence of flexible low T g poly (C13MA) segments and rigid poly (IBOMA) repeating units.
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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".