Synthesis and mechanical properties of diimide‐hydrogenated natural rubber vulcanizates
Bibliographic record
Abstract
Abstract Hydrogenated natural rubber (HNR), providing an ethylene–propylene alternating copolymer, was prepared by the chemical modification of natural rubber latex (NRL) using diimide generated from hydrazine (N2H4) and hydrogen peroxide (H2O2), with copper sulfate (CuSO4) as catalyst. 1H‐NMR analysis indicated that 48% hydrogenation was performed with a mole ratio of N2H4/double bonds = 4 and H2O2/N2H4 = 1.5 at 50°C for 7 h. The obtained HNR was subjected to a sulfur cure by using a conventional milling process. The cure characteristics, mechanical properties before and after heat aging, and abrasion and ozone resistances of HNR vulcanizate were examined and compared with those of natural rubber (NR), ethylene propylene diene terpolymer (EPDM) and 50 : 50 NR/EPDM vulcanizates. The results indicated that the cure rate of 48% HNR showed no significant change when compare to both NR and 50 : 50 NR/EPDM blends, and offered a better processing advantage over EPDM. The mechanical properties and abrasion resistance of a 48% HNR vulcanizate were comparable to those of a NR vulcanizate. Additionally, its heat and ozone resistances were better than those of NR vulcanizate, due to a reduction in the amount of double bonds in the backbone chain. Thus, hydrogenation of NR can lead to a type of rubber that has improved heat and ozone resistances while still maintaining its good mechanical properties. Consequently, it improves the properties of NR for a wide range of applications. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
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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".