Utilizing Fish scales as an bioadditive to enhance the thermal insulation and mechanical properties to coir-polypropylene biocomposites
Bibliographic record
Abstract
This study shows that the addition of fish scales into coir-polypropylene composites provides high thermal resistance and also substantially improves mechanical properties and acoustic absorption required for civil, automotive and other applications. Fish scales (FS) are inevitably generated as byproducts and are a sustainable and renewable resource with unique structure and properties. Coir-based composites lack the performance properties particularly, flame resistance, thermal and acoustic resistance required for acoustic panelling and false ceiling applications. In this study, fish scale powder was included as an additive (5–30% by weight) and changes in the mechanical properties, flame, thermal and acoustic resistance were investigated. A substantial increase in tensile strength of up to 83% and flexural strength by 30% was possible due to the addition of fish scale powder. The FS powder blocks the pores between the matrix and reinforcement leading to higher acoustic (sound absorption coefficient of up to 0.45) and thermal insulation (0.031W/mK). Composites obtained in this study have properties better than many biobased composites of similar density and are considered suitable for automotive, civil and other applications.Highlights Coir reinforced composites have poor performance propertiesFish scale have unique properties and easily available at low costFish scales as additive improved mechanical properties, thermal and noise insulationCoir-Fish scale-PP composites are suitable for various applications
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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".