A comparative study of the cure characteristics, processability, mechanical properties, ageing, and morphology of rice husk ash, silica and carbon black filled 75 : 25 NR/EPDM blends
Bibliographic record
Abstract
Abstract The performance of rice husk ash (RHA), obtained by burning rice husks, as a filler for natural rubber (NR)/ethylene–propylene–diene monomer (EPDM) blends was investigated. For comparison purposes, two commercial reinforcing fillers, silica and carbon black were also used. A fixed 75 : 25 blend ratio (wt %) of NR and EPDM was prepared using a two‐stage conventional mixing procedure. Filler loading was varied from 0 to 60 parts per hundred of resin (phr) at 15 phr intervals. The results indicated that RHA offers processing advantages over silica and carbon black. The use of RHA as an additional filler provided almost no improvement in the tensile strength and abrasion resistance of the 75 : 25 NR/EPDM blends. The ozone resistance of the blends was inferior to those obtained from the addition of RHA. However, RHA was better in resilience property than that of silica and carbon black. Scanning electron micrographs revealed that the dispersion of RHA filler in the rubber matrix is discontinuous, which in turn generates weak structure when compared with carbon black and silica. According to these observations, RHA could be used as a diluent filler for the 75 : 25 NR/EPDM blend, while silica and carbon black can be used as a reinforcing filler. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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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".