Sustainable Bio-Inspired Limestone Eggshell Powder for Potential Industrialized Applications
Bibliographic record
Abstract
Biowaste chicken eggshells contain high amounts of calcium carbonate or calcite. Waste eggshells generated by processing industries have the potential to be used as limestone or lime in a variety of applications. Studies have shown removal of membrane from eggshells can be separated at the laboratory level, but mass production has not been industrialized. The aim of this study was to optimize two membrane removable techniques; heat treatment and bleach treatment. The as-received eggshell samples were first water ball milled into a 63 μm powder. In the first method, fine eggshell powders were submitted to elevated temperatures from 105 to 800 °C in air. The second method involved submitting the powder to a chemical treatment of 10% to 100% bleach concentrations and holding in the solutions for different soak times. The powdered samples were characterized for chemical composition and microstructural analysis. The results indicated pure calcite can be produced by heating to a temperature of 300 °C for a period of 2 h or a 10% bleach treatment for 48 h or a 50% bleach for 10 min. In addition, calcite from eggshells could be transformed into lime by heating to 750 °C for 1 h. The heat treatment method can easily be scaled up to mass production. This study signified eggshells can be used as a total or partial alternative replacement to mined limestone.
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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".