Thermally Labile Self‐Healable Branched Gel Networks Fabricated by New Macromolecular Engineering Approach Utilizing Thermoreversibility
Bibliographic record
Abstract
Abstract A new approach based on macromolecular engineering through thermoreversibility is reported to fabricate the engineered gel networks of thermally labile branched polymers exhibiting robust self‐healing. This approach centers on the synthesis of linear polymers having Diels–Alder cycloadducts in the backbones (DALPs) through A2 + B2 step‐growth polymerization of a difunctional furan and a difunctional maleimide. Reactive mixtures of the resulting DALP with a polyfuran at elevated temperature allow for the formation of engineered gel networks through random dissociation of backbone DA linkages of the DALPs by retro‐Diels–Alder reaction, followed by their reconstruction in the presence of polyfuran (with functionality > 2) by Diels–Alder reaction. Optimizing the ratio of furan to DA linkages in the reactive mixtures yields thermally labile networks exhibiting excellent thermoreversibility. Effective self‐healing demonstrated with reconstruction from two separate pieces and complete void filling on surface cuts as well as recovery of healing viscoelasticity suggest that the new macromolecular engineering approach offers versatility toward the development of thermally mendable thermosets.
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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".