Dual Cross‐linked Vinyl Vitrimer with Efficient Self‐Catalysis Achieving Triple‐Shape‐Memory Properties
Bibliographic record
Abstract
Abstract As an emerging class of dynamic cross‐linked network, vitrimers have attracted much attention due to the combination of mechanical advantages of thermosets and recyclability of thermoplastics at an elevated temperature. In particular, most vitrimers with multi‐shape memory properties usually involve more than one thermal transition or molecular switch, which might pose a challenge for facile sample fabrication and potentially limits their applications. In pursuit of a more universal and simple route, utilizing commercially available and inexpensive reagents to prepare shape‐memory vitrimers with dual cross‐linked network from vinyl monomer‐derived prepolymers is reported here. Copolymerization of desired vinyl monomers gives prepolymers containing carboxyl and zinc carboxylate groups, which are later converted into vitrimers in a single step by post‐curing with diglycidylether of bisphenol A. The Zn2+ ions can not only act as physical crosslinking points through ionic coordination interactions, thus providing the triple‐shape‐memory properties, but also play the role of catalyst to activate transesterification in the dynamic covalent network. This new self‐catalyzed vitrimer has excellent transesterification efficiency, triple‐shape‐memory properties, and can be sufficiently healed and reprocessed at an elevated temperature. The proposed molecular design of self‐catalyzed materials opens a new avenue toward commercially relevant fabrication of high‐performance vitrimers with multiple shape‐memory properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".