Effect of chemical treatment on the properties of coir fiber reinforced polypropylene and polyethylene composites
Bibliographic record
Abstract
Chemical treatment of reinforcement material is one of the main ways of improving the mechanical properties of natural fiber reinforced polymer composites. In the present study, coir fiber was used as reinforcement material, while polypropylene (PP) and polyethylene (PE) polymer were used as matrix material. Before reinforcing with polymer, raw coir fiber was chemically treated with basic chromium sulfate and sodium bicarbonate in a sieve shaker. Hot‐pressed method was used for composite manufacturing during which the fiber loading was varied at 0, 5, 10, 15, and 20 wt%. Comparison of the properties of raw and chemically treated coir fiber reinforced PP and PE was conducted. Mechanical characteristics of the composites were evaluated using tensile, flexural, impact, and hardness tests. Water absorption test was conducted to know water uptake characteristics. Microstructural analysis using a scanning electron microscope was performed to observe the adhesiveness between the matrix and the fiber. Thermogravimetric analysis was done to observe the physical and chemical changes in fiber and composites. The results showed that chemical treatment improved the physical, mechanical, and thermal properties of the manufactured composites. PP composites had better properties as compared to PE composites, while higher fiber loading resulted in better mechanical properties of the resultant composites. POLYM. COMPOS., 38:1259–1265, 2017. © 2015 Society of Plastics Engineers
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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) | 0.001 | 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".