Featuring High Impact Polystyrene Composites Strengthened with Green Coconut Fiber Developed for Automotive Industry Application
Bibliographic record
Abstract
Studies focused on generating products able to reduce environmental impact have been put in place, and those aiming at finding polymeric composites strengthened with natural fibers stand out among them. A composite was strengthened with coconut fibers in the present study, since the generation of coconut residues has increased in Brazil due to coconut water industrialization. The aim of the present study is to process a high impact polystyrene (HIPS) composite strengthened with coconut fiber and to verify its possible application in the automotive industry. Strengthened samples were prepared using coconut fibers in the proportions 10, 20 and 30% fiber to develop such HIPS composite. Analyses were performed in order to set the density, Shore A hardness and impact resistance of the processed composites. It was found that the density and Shore A hardness results in the HIPS did not change due to the addition of coconut fibers. However, it was observed that the impact resistance was reduced because of it. It was noteworthy that the decreased impact resistance did not preclude the use of the HIPS/coconut fiber composite, since it showed that such material can be used in parts free from strong mechanical stress such as those inside the automobiles. The use of coir HIPS also aimed at reducing costs related to the polymer’s consumption and at reusing the waste (coconut husk) from coconut water industries.
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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 | 0.001 | 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".