Block Copolymer Boronic Ester Vitrimers: Balancing Self‐Healing and Creep Through Self‐Assembly
Bibliographic record
Abstract
ABSTRACT Vitrimers offer notable advantages such as enhanced recyclability and self‐healing, but their susceptibility to creep remains a barrier to their widescale adoption. Here, we incorporated hard‐soft AB block copolymers into dynamic networks. Nitroxide mediated polymerization was used to synthesize prepolymers containing boronic acid functionality of either statistical or AB diblock architecture. We then formed four different boronic ester networks by blending a statistical diol‐functional prepolymer with either statistical, AB diblock copolymers, or a small‐molecule diboronic ester. The resulting networks include: N‐S (statistical/statistical), N‐B1 (statistical/diblock), N‐B2 (statistical/diblock), and N‐X (statistical/small molecule cross‐linker). Phase‐separated microdomains were confirmed in blocky N‐B1 and N‐B2 by small‐angle X‐ray scattering. Mechanical tests showed improved tensile strength and hardness in N‐B1 and N‐B2 compared with the homogeneous N‐S. Notably, creep was reduced five‐fold in blocky N‐B2 compared with homogeneous N‐S. Although lower stress relaxation was observed in the blocky networks, stress was still fully recovered with characteristic relaxation times < 33 s. All the networks retained their mechanical properties through recycling and exhibited self‐healing at ambient conditions. The results highlight the potential of incorporating small amounts (< 10 wt%) of phase‐separated microdomains in vitrimer networks to enhance their rheo‐mechanical properties.
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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".