Mechanical Properties of Rice Husk Fiiled Cashew Nut Shell Liquid Resin Composites
Bibliographic record
Abstract
The need to have composite materials that are renewable has lead to materials scientists researching on natural fibres. To minimize environmental pollution research into rice husk has been on for a period of time now. In this research cashew nut resin reinforced rice husk composite was fabricated at different particle sizes and different filler loading. The present study reveals that both filler loading and particle size can affect the tensile strength, young modulus, strain at failure, flexural strength, and impact strength. As the % filler content increases all particle sizes (400µm, 600µm and 800µm) shows a quadratic relationship except % elongation at failure which tends to be linear at 400µm. As particle size increases all % filler content shows quadratic relationship which tends to be linear. As particle sizes increases impact strength for 10 and 30% shows a linear equation while 20% shows a quadratic relationship which tends to be linear. In general, RH/CNSR composite could be considered as a potential source of low cost, natural fibre for composites.
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) | 0.002 | 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".