Mechanical Properties of a Bio-Composite Produced from Two Biomaterials: Polylactic Acid and Brown Eggshell Waste Fillers
Bibliographic record
Abstract
An option to reduce the exploitation and depletion of natural mineral resources is to repurpose current waste materials. Fillers are often added to polymers to improve the properties and lower the overall cost of the final product. Very few studies have assessed the use of waste brown eggshell powder (BESP) as filler in polylactic acid (PLA). The addition of mineral fillers in a polymer matrix can play an important role in the performance of a composite under load. Therefore, tailoring the amount of filler content can be a deciding factor as to which filler amount is best. The goal of this study was to investigate the effect of brown eggshells compared to conventional limestone (LS) powder on the mechanical properties of PLA composites. One-way analysis of variance (ANOVA) was used to carry out the statistical analysis on the average values of each composite mechanical property tested. Scanning electron microscopy (SEM) was used to view if there were any differences in the fractured surfaces. Overall, the LS performed marginally better than the BESP fillers. The highest ultimate tensile and ultimate flexural strengths for eggshell composites containing 32 µm fillers had values of 48 MPa (5–10 wt.% BESP) and 67 MPa (10 wt.%. BESP), respectively. Both the tensile and flexural modulus improved with filler contents and were highest at 20 wt.% with values of 4.5 GPa and 3.4 GPa, respectively. The Charpy impact strength decreased for all filler amounts. SEM micrographs identified changes in the fractured surfaces due to the additions of the filler materials. The ANOVA results showed statistically significant differences for the composite materials. After five weeks of soaking in distilled water, the composites containing 20 wt.% BESP fillers had the highest weight gain. The study demonstrated that waste brown eggshells in powdered form can be used as a filler in PLA composites.
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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".