Chain Orientation in Polyethylene Fibers Prepared by Ethylene Nanoextrusion Polymerization
Bibliographic record
Abstract
Abstract Summary: Ethylene nanoextrusion polymerization has been demonstrated to be a novel nanofabrication concept for the preparation of polyethylene (PE) fibers directly from ethylene monomers without any post‐processing procedures. For PE fibers, chain orientation is a critical parameter that affects performance and application of the fibrous materials. In this communication, we report an investigation on chain orientation in PE fibrous samples prepared through nanoextrusion polymerization using a two‐dimensional wide angle X‐ray diffraction (2D WAXRD) technique. Two types of fibrous samples, including individual microfibers and microfiber aggregates, were sampled randomly and studied. For individual PE microfibers, anisotropic diffraction patterns were observed, suggesting chain orientation along the microfiber axial direction. Some microfibers showed the most desired diffraction pattern often found in high‐modulus high‐strength PE fibers. These samples possessed a very high degree of chain orientation along the fiber axis. Owing to a random aggregation of anisotropic microfibers, microfiber aggregates exhibited isotropic diffraction patterns. This work provided further experimental evidence for the proposed nanoextrusion polymerization concept. 2D WAXRD diffraction pattern of a PE microfiber. magnified image 2D WAXRD diffraction pattern of a PE microfiber.
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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) | 0.000 | 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".