Application of rice husk ash as fillers in the natural rubber industry
Bibliographic record
Abstract
Abstract Rice husk ash (RHA) obtained from agricultural waste, by using rice husk as a power source, is mainly composed of silica and carbon black. A two‐stage conventional mixing procedure was used to incorporate rice husk ash into natural rubber. For comparison purposes, two commercial reinforcing fillers, silica and carbon black, were also used. The effect of these fillers on cure characteristics and mechanical properties of natural rubber materials at various loadings, ranging from 0 to 40 phr, was investigated. The results indicated that RHA filler resulted in lower Mooney viscosity and shorter cure time of the natural rubber materials. The incorporation of RHA into natural rubber improved hardness but decreased tensile strength and tear strength. Other properties, such as Young's modulus and abrasion loss, show no significant change. However, RHA is characterized by a better 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 a weak structure compared with that of carbon black and silica. Overall results indicate that RHA can be used as a cheaper filler for natural rubber materials where improved mechanical properties are not critical. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 98: 34–41, 2005
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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".