Poly(Alkylene Ether)s With Nitrile Pendent Groups
Bibliographic record
Abstract
ABSTRACT There is significant interest in elastomers with high thermal and oil resistance. Elastomers such as nitrile butadiene rubber (NBR) are available but have insufficient thermal stability in certain applications due to unsaturation in the polymer backbone. Hydrogenation produces hydrogenated NBR (HNBR), providing improved heat resistance, but the added manufacturing step and expensive catalysts make HNBR prohibitively expensive for many applications. Additionally, the structure of HNBR imparts lower limits to its glass transition temperature (T g ). Poly(alkylene ether) elastomers are potential alternatives to HNBR, requiring only one manufacturing step and avoiding expensive catalysts, but past attempts to investigate these polymers met limited success. Here we describe the preparation of β‐cyanoethyl glycidyl ether and its homopolymerization using the Vanderberg chelate initiator. Careful monomer purification was essential to obtain soluble elastomers, but the polymer still exhibited poor thermal stability due to facile decyanoethylation. We also prepared other poly(alkylene ether) nitrile polymers with improved thermal stability, including one with a T g of −51°C. When the side chain was 7‐cyano‐1‐heptylene, the polymer was semi‐crystalline, resulting in a thermoplastic elastomer. These elastomers could present alternatives to NBR and HNBR and could be investigated in areas such as solid polymer electrolytes in renewable energy applications.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".