Self-healing, ultra-high impact-resistant elastomer composites based on enhanced non-covalent cross-linked dual dynamic network
Bibliographic record
Abstract
The shear-thickening gel (STG) has emerged as one of the most promising materials in the field of impact resistance due to its strain-rate response characteristics. However, its inability to maintain a fixed geometry under gravitational conditions greatly limits its practical application process. In this study, we present a novel strategy to overcome this limitation by constructing an enhanced non-covalent dual-network elastomer composites (WPSTG) constructed by waterborne polyurethane and STG based on dynamic boron‑oxygen bonds (B O) and hydrogen bonds interaction. WPSTG showed stable physical form and the typical strain rate response characteristics. The dynamic B O bonds and hydrogen bonds synergistically enable WPSTG exceptional mechanical properties with high stretchability (1360 % fracture strain), energy dissipation ability and significantly enhanced crack insensitivity (252 % improvement) under low strain rate. Meanwhile, WPSTG has ultra-high impact strength (735.2 MPa at 7500 s −1 ) which derives from the internal B O bond locking effect. In addition, enhanced dynamic dual-network endowed WPSTG with 92 % self-healing efficiency of the impact resistance strength with 676.4 MPa after healed at 80 °C for 12 h, which has never been reported. It also can effectively protect the balloon from being damaged by the impact of the surgical knife. And a flexible pressure sensor fabricated with WPSTG highlights its potential as next-generation integrated outer skin material of soft electronics.
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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".