The influence of <scp>N</scp>i/<scp>N</scp>d‐based <scp>Z</scp>iegler–<scp>N</scp>atta catalyst on microstructure configurations and properties of butadiene rubber
Bibliographic record
Abstract
ABSTRACT Neodymium (Nd)‐based Ziegler–Natta catalyst has been well known for preparing polybutadiene rubber (BR) containing high, about 98%, cis−1,4 configuration with extremely low gel content providing superior resistance to low‐temperature fatigue and abrasion. However, its cost is more expensive than a conventional nickel (Ni)‐based catalyst. The Nd‐BR has poor processability with high cold flow due to its high linearity and molecular weight. To compare with a traditional process, the BR produced by Ni‐based catalyst has higher level of branching resulting in the better processability, but it contains medium amount of gel. To balance the catalyst cost and the BR properties, this article reported the influence of a solution containing Ni‐ and Nd‐based Ziegler–Natta catalyst (Ni/Nd) using diethyl aluminum chloride and triethyl aluminum as co‐catalysts on 1,3‐butadiene (BD) conversion and physical properties of the elastomeric materials based on obtained rubber (Ni/Nd‐BR). In the presence of toluene, the increase in the Ni/Nd molar ratio from 0.0/1.0 to 0.4/0.6 yielded Ni/Nd‐BR containing cis−1,4 units of 95%–96% with significantly decreasing both levels of vinyl−1,2 and trans−1,4 configurations from 0.26% to 0.13% and 4.44% to 3.07%, respectively. When cyclohexane was applied as the reaction media, 100% BD conversion was achieved and the Ni/Nd‐BR had very low content of vinyl−1,2 unit (0.07%). The mechanical properties in terms of tensile properties and abrasion resistance of the elastomer based on Ni/Nd‐BR having high cis‐1,4 and relatively higher trans−1,4 configurations were superior to elastomers based on commercial BRs produced by using Ni‐ and Nd‐based catalyst systems. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41834.
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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".