Effects of cashew nut shell liquid as a plasticizer on cure characteristics, processability, and mechanical properties of 50 : 50 NR/EPDM blends: A comparison with paraffin oil
Bibliographic record
Abstract
Abstract 50 : 50 natural rubber (NR) and ethylene–propylene–diene monomer rubber (EPDM) blends were prepared with different contents of cashew nut shell liquid (CNSL), a natural product obtained from the shells of the cashew nut, as a plasticizer. For comparison, a commercial paraffin oil plasticizer was also used. The effect of plasticizer content on the cure characteristics, processability, and mechanical properties such as tensile strength, elongation at break, and Young's modulus before and after ageing was investigated. Scanning electron microscopy (SEM) was used to observe the blend morphology. The results indicated that the CNSL plasticizer resulted in lower Mooney viscosity and lower cure time of the 50 : 50 NR/EPDM blends. The incorporation of CNSL into 50 : 50 NR/EPDM blends improved tensile strength and elongation at break but decreased Young's modulus. On addition of CNSL the resistance of the blends to heat and weathering ageing improved. Scanning electron micrographs revealed that the morphology of the blend plasticized with CNSL is finer and more homogeneous compared with the blend plasticized with paraffin oil. Overall results indicate that CNSL can be used as a cheaper plasticizer to replace paraffin oil in NR/EPDM blends with improved processability and mechanical properties. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007
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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".