Effect of palmyra sprout fiber and biosilica on mechanical, wear, thermal and hydrophobic behavior of epoxy resin composite
Bibliographic record
Abstract
In this study, natural fiber epoxy composites were prepared using palmyra sprout fiber and red matta rice husk ash(RHA) biosilica. This paper mainly aims to investigate the mechanical, wear resistance, thermal stability as well as water absorption behaviour of naturally obtained novel fiber with red matta biosilica in epoxy based composites. The fiber’s surface was treated by base, while the biosilica particles were treated by amino-silane. The composites were fabricated by hand lay-up process and characterized based on ASTM standards. According to the results the highest tensile and flexural strength observed for the composite is about 147 MPa and 211 MPa for 3 vol. % of biosilica with 30 vol. % of fiber. Izod impact toughness reveals the maximum impact resistance up to 5.82 J. Increment in reinforcement vol. % shows increased hardness. Wear properties represents the composite designations EPB3 retains good wear resistances for 3 vol. % of biosilica. Similarly, thermal stability improved by the addition of biosilica of 3 vol. %. Water absorption results reveal that, the addition of reinforcements marginally affects the contact angle. Such mechanically improved, wear resistible and thermally stable natural composites could be used in automotives, industrial and defense applications as well as in household appliances.
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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".