Modification of hydrogenated nitrile rubber with N,N′‐bismaleimide‐4,4′‐diphenylmethane to improve its resistance to heat aging and reduce its compression set
Bibliographic record
Abstract
Abstract This paper delves to improve the upper‐limit temperature of hydrogenated nitrile rubber (HNBR) through regulation of the vulcanization process, filler system and cross‐linking additive. The tensile test and scanning electron microscope images showed that the aging at 180°C significantly accelerated the deterioration in the property of HNBR compared with 150°C aging. The results showed that a higher dosage of bis(1‐(tert‐butylperoxy)‐1‐methylethyl)‐benzene curing agent reduced the compression set of HNBR without sacrificing its tensile strength. Carbon black N539‐filled HNBR exhibited high tensile strength, while carbon black N774‐filled HNBR showed high heat resistance and low compression set. When the HNBR sample was reinforced with 20 phr of N539 and 20 phr of N774 carbon black, it achieved low compression set, high tensile strength, and heat resistance simultaneously. Then, N,N′‐bismaleimide‐4,4′‐diphenylmethane (BMI) was introduced into the HNBR composite as a cross‐linking assistant agent. The residual strength of BMI‐modified HNBR was 20.3 MPa (the retention rate of 92.3%), and its compression set was as low as 19.2% after aging at 180°C for 72 h. Even after aging the composite at 180°C for 168 h, its strength retention rate remained at 83.2% (18.3 MPa). The approach provided here increased the upper‐limit temperature range of HNBR to 180°C.
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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".