Evaluation of Octyltetramethyldisiloxane‐Containing Ethylene Copolymers as Composite Lubricant for High‐Density Polyethylene
Bibliographic record
Abstract
A series of octyltetramethyldisiloxane‐containing ethylene copolymers, poly(ethylene‐co‐OO7) (abbreviated as E‐co‐OO7), are prepared from vanadium catalyzed copolymerization of ethylene with 1‐oct(7‐en)yl‐3‐octyl‐1,1,3,3‐tetramethyldisiloxane (or OO7) macromonomer. The copolymers containing different silicone contents are employed as composite lubricants for high‐density polyethylene (HDPE). The influences of the silicone content in the copolymer and that of the added copolymer amount in the blend on the bulk and surface properties of the blends are systematically investigated. The results show that E‐co‐OO7 exhibits superior overall performance in comparison with conventional lubricants silicone masterbatch and polyethylene wax. Compared to HDPE control, 10 wt% E‐co‐OO7 addition increases the melt flow rates by 49%, increases the elongation at break from 740 to 860%, increases the water‐contact angle from 90° up to 108°, lowers the coefficient of friction from 0.072 to 0.049, lowers the specific wear rate from 11 × 10−3 to 5.6 × 10−3 mm3 Nm−1. The impact strength and high temperature thermal stability are also slightly improved. The measured Si/C atomic ratios demonstrate the sufficient silicone enrichment on surface of the blends. E‐co‐OO7 with 23.5 SiOSi per 1000 C gives the best internal lubrication, and E‐co‐OO7 with 29.7 SiOSi per 1000 C gives the best external lubrication. image
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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".