Higher‐molecular‐weight hyperbranched polyethylenes containing crosslinking structures as lubricant viscosity‐index improvers
Bibliographic record
Abstract
Abstract As a new grade of polyethylene materials with unique chain architectures, hyperbranched polyethylenes synthesized by chain walking ethylene polymerization have great potential for industrial application as novel viscosity index (VI) improver in lubricant formulation. Although high‐molecular‐weight hyperbranched polyethylenes (weight‐average molecular weight of about 10 5 g/mol) possess high shear stability, their viscosity thickening properties are compromised due to their compact chain architectures. In this work, we aim at improving their viscosity thickening property by increasing polymer molecular weight. A range of hyperbranched polymers of various enhanced molecular weights were synthesized by chain walking ethylene polymerization in the presence of small amounts of 1,4‐butanediol diacrylate as a difunctional crosslinker. The molecular weight dependences of viscosity thickening power and shear stability of these polymers containing crosslinking structures were evaluated. It is found that, with the increase of molecular weight via crosslinking, these polymers showed consistently enhanced viscosity thickening power, but with the reduced shear stability. However, their shear stability was still significantly better compared to linear polymers. POLYM. ENG. SCI., 2010. © 2009 Society of Plastics Engineers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".