Effect of Alumina Particles on the Mechanical and Physical Properties of Polypropylene Whisker Reinforced Lamination 80:20 Resin Composite
Bibliographic record
Abstract
The low cost, relatively high fibre strength, and low density of polypropylene (PP) fibres make them an attractive candidate for reinforcing low-cost composite materials for various applications.The use of particles in fibre-reinforced composite structures usually improves the strength of the basic material.Alumina (Al2O3), with its unique properties, has been used to improve the composite material's properties.It is crucial to determine the appropriate concentration of Al2O3 particles that can be added to Polypropylene whisker/epoxy composites to get the required mechanical and physical properties.In this study lamination 80:20 resin was used as a matrix material to prepare a Polypropylene whisker (3 %wt.) reinforced lamination 80:20 resin (PP/resin) composite.The composite materials were manufactured by hand lay-up method.Different concentrations (1, 3, 5, 7 & 10 wt.%) of alumina (Al2O3) particles, were used to reinforce the composite.The composite materials are prepared with standard sizes according to the tests, the tests include mechanical tests (tensile, impact, and hardness test) and physical tests (water absorption and density).The tests results showed an improvement in tensile strength, elongation, toughness, fracture toughness, hardness and density, while the water absorption diminishes concerning an increase in the concentration of the Al2O3 particles.The tensile strength, elongation, elastic modulus, hardness and density improved from (17 MPa, 1.3%, 1.13GPa, 70 Shore D and 0.93gm/cm 3 ) respectively for neat lamina and reach the maximum value (34.5 MPa, 3.1%, 1.47 GPa, 83 Shore D and 1.01 gm/cm 3 ) respectively at (3%wt.PP and 10%wt.Al2O3), while the toughness and fracture toughness improved from (3.8 KJ/m 2 and 0.06 MPa.m 1/2 ) respectively for neat lamina and reach the maximum value (23.1 KJ/m 2 and 0.19 MPa.m 1/2 ) respectively at (3 %wt.PP and 7% wt.Al2O3).
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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".