Well-Defined Chain-End Functionalized Polyethylenes with Different Macromolecular Architectures
Bibliographic record
Abstract
This study reports the synthesis of a wide library of α,ω-dihydroxy, ω-hydroxy (linear and 3-arm star), and ω-amine functionalized, as well as nonfunctionalized polyethylenes (linear and 3-arm star) via diimide hydrogenation of the corresponding polybutadiene precursors. All polyethylenes were synthesized using anionic polymerization high-vacuum techniques followed by suitable postpolymerization reactions and hydrogenation. Molecular characterization confirmed low 1,2-microstructures in the polybutadiene precursors, resulting in linear low-density polyethylene (LLDPE)-like architectures, after hydrogenation. Additionally, perfectly linear ω-hydroxy polymethylene (equivalent to polyethylene) samples, resembling high-density polyethylene (HDPE) structures, were synthesized via polyhomologation. Thermal analysis and X-ray diffraction measurements showed reduced crystallinity in the functionalized samples, likely due to hydrogen-bonding effects that disturb the crystalline packing. In contrast, the polyethylene synthesized via polyhomologation exhibited higher crystallinity values, resembling the structure of high-density polyethylene (HDPE). These findings underscore the influence of functional groups on polyethylene properties, motivating future studies on their thermal and dielectric behavior.
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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".