A comparison of the properties of rice husk ash, silica, and calcium carbonate filled 75 : 25 NR/EPDM blends
Bibliographic record
Abstract
Abstract The effects of incorporation of three different fillers, i.e., rice husk ash (RHA), silica, and calcium carbonate (CaCO3), over a loading range of 0–60 phr on the curing characteristics, processability, mechanical properties, and morphology of 75 : 25 natural rubber (NR)/ethylene‐propylene‐diene monomer (EPDM) blends were studied using a conventional vulcanization system. Filler loading and type influence the processability of the blends in which RHA and CaCO3 offer better processing advantage over silica. The best improvement in the tensile and tear strength and abrasion resistance of the 75 : 25 NR/EPDM blends with additional fillers was achieved when filled with silica. However, RHA and CaCO3 were better in resilience property compared to that of silica. The RHA filled blends showed higher failure properties and abrasion resistance but lower ozone resistance than that containing CaCO3. Scanning electron micrographs revealed that the morphology of the blend filled with silica is finer and more homogenous compared to the blend filled with RHA and CaCO3. According to these observations, RHA can be used as a cheaper filler to replace CaCO3 in rubber blends where improved mechanical properties are not so critical. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".