Rheological and mechanical comparison of di-and tri-block copolymer imine vitrimers
Bibliographic record
Abstract
• Statistical, AB diblock and ABA triblock methacrylated bio-based prepolymers were made by RAFTwith similar molecular weight and composition. • Triblock networks exhibited improved tensile properties while diblock vitrimers showed lower stress and strain at break. • Compared to statistical vitrimers, both diblock and triblock copolymer based vitrimers showed 60% improved creep resistance. Vitrimers’ tendency to creep over time under stress poses a significant challenge to their widespread industrial use. Recently, nanoscale self-assembly has emerged as a promising route to control creep in vitrimers; however, mechanical robustness of such phase-separated materials requires further enhancement and investigation. Herein, we explored how the prepolymer architecture influences the structure-property relationships in vitrimers made of statistical copolymers, AB hard-soft diblock copolymers, and ABA hard-soft-hard triblock copolymers. Reversible addition fragmentation chain-transfer (RAFT) polymerization was employed to control the length, sequence, and morphology of the precursors. Dynamic imine functionalities were localized within the soft segment, which consisted of a bio-based copolymer of long-chain alkyl methacrylate and aldehyde-functional vanillin methacrylate. Compared to the statistical vitrimers, the triblock copolymer vitrimers exhibited approximately 60 % improved creep resistance, a 400 % increase in hardness, a higher tensile modulus, and ∼ 57 % higher stress at break. In contrast, vitrimers derived from diblock copolymers exhibited much weaker tensile properties with approximately 30 % lower stress at break. Our findings suggest that by controlling the chain architecture in block copolymers and optimizing the order–disorder transition, we can enhance vitrimers’ creep resistance and tune their viscoelastic properties while tailoring their mechanical strength.
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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) | 0.002 | 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".