Reprocessable Biobased Statistical and Block Copolymer Methacrylic-Based Vitrimers with a Shape Memory Effect
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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 it