Bio-Based White Eggshell as a Value-Added Filler in Poly(Lactic Acid) Composites
Bibliographic record
Abstract
Based on its positive environmental impact, poly(lactic acid) (PLA) has been a gradual substitute for synthetic plastics used in diverse applications. The use of industrial limestone (ILS) as a filler in polymers can have advantages of changing the properties of pure polymers. Waste eggshells (WE) can be seen as an alternative filler to ILS as they are also a source of calcium carbonate. To assess the feasibility of both filler types and sizes, PLA composites were manufactured by injection molding with filler contents of 5, 10, and 20 wt.%. Tensile, flexural, and impact mechanical properties were evaluated in addition to water absorption. One-way analysis of variance (ANOVA) was performed to determine whether statistically significant differences among the measured mechanical properties existed. Scanning electron microscopy (SEM) was used to view the morphology of the fillers and fractured surfaces. The composite tensile strengths and flexural strengths performed the best when filler loadings were 5 wt.% and 10 wt.%, respectively, for both filler types. The tensile and flexural modulus both increased with filler loadings. The impact strength for the composites was obtained at a threshold level of 5 wt.% filler loadings for both filler types and slightly better for smaller particles sizes. ANOVA identified statistically significant differences for the mean mechanical property values evaluated. SEM showed the fractured surfaces of the PLA composites were different from the pure PLA indicating some transformation occurred to the matrix. The weight gains due to water absorption were observed to increase with increase in content of both filler types while the smaller particles had slightly higher water weight gains. Although the composites containing ILS fillers had somewhat enhanced mechanical properties over the WE-filled composites, the end application will dictate which filler type to use in PLA.
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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) | 0.000 | 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".