Covalent Organic Framework-Oriented Chain Growth for High-Performance Polyolefins
Bibliographic record
Abstract
Polyolefins have long dominated materials technology and polymer production; yet enhancing mechanical strength, toughness, and processability in high-performance polyolefins still remains a challenge. Herein, we use minimal quantities of covalent organic frameworks (COFs) to engineer the native aggregate structure of polyethylene (PE). By employing in situ ethylene polymerization, we synthesized high-performance COF-PE composites with unique nanofibrous structures at COF loadings of 0.02 wt %. Specifically, hydroxyl-functionalized imine-based COFs act as macroligands for bis(cyclopentadienyl)zirconium dichloride (Cp 2 ZrCl 2 ), establishing a unique spatial confinement on chain growth. The resulting COF-PE composite exhibits a weight-average molecular weight ( M w ) of up to 240.0 kDa (increasing 118%), a narrow molecular weight distribution ( Đ as low as 1.9), and an elevated melting point ( T m ) of 139.2 °C (4.5 °C higher) compared to pure PE. Moreover, the composite exhibits an outstanding tensile strength of 45.5 MPa and an unprecedented elongation at break of 1832%, outperforming both literature-reported and commercial counterparts. Remarkably, it demonstrates enhanced melt processability above T m, evidenced by a reduced zero-shear viscosity (η 0 ) of 3953 Pa·s. Structural analyses reveal COF rigidity-dependent crystalline reinforcement, featuring thickened lamellae (15.1–17.0 nm) and tunable nanofibrous diameters (123–512 nm). This work demonstrates COF-immobilized catalysts enabling polyolefin nanostructural engineering for simultaneous mechanical enhancement and processing optimization.
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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".