Reprocessable Biobased Statistical and Block Copolymer Methacrylic-Based Vitrimers with a Shape Memory Effect
Bibliographic record
Abstract
Transitioning to a circular bioeconomy requires shifting away from conventional petroleum-based thermosets to recyclable biobased materials. We developed reprocessable and shape-memory biobased vitrimers with controlled architecture and enhanced creep resistance. A series of prepolymers of lignin-based vanillin methacrylate (VMA), with a mixture of vegetable oil-derived methacrylic esters with an average alkyl side chain length of 13 units (C13MA), and glycidyl methacrylate (GMA) were synthesized: statistical copolymer poly(C13MA- co -vMA), block copolymer poly(C13MA- block -VMA), and statistical terpolymer poly(C13MA- co -VMA- co -GMA). Reversible addition–fragmentation chain-transfer (RAFT) polymerization allowed control over the backbone structure of statistical and block copolymers with similar overall molecular weights and compositions. Vitrification via aldehyde-functional VMA units with isophorone diamine enabled dynamic imine crosslinking, while adding the epoxy-functional GMA into the statistical terpolymer precursor enabled hybrid static–dynamic crosslinking networks. Vitrimers showed excellent retention of thermomechanical properties after 4× reprocessing cycles. Microphase separation was confirmed in the “hard–soft” type of block vitrimers. While statistical vitrimers showed comparable tensile properties, self-assembled block vitrimers exhibited poor tensile strength. Additionally, we compared the creep control using microphase separation and a hybrid network. Both methods reduced creep (up to 84%), but hybrid crosslinking caused significantly slower stress relaxation. A temperature-triggered shape memory effect was incorporated into the statistical vitrimer with two shape-programming cycles. Combining C13MA and VMA presents a rich platform for greener vitrimers with highly tunable properties and functionalities.
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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".