Biotechnological Applications of Eggshell: Recent Advances
Bibliographic record
Abstract
The eggshell (ES) provides protection against pathogenic and physical insults while supplying essential metabolic and nutritional needs for the growing avian embryo. It is constituted mainly of calcium carbonate arranged as calcite crystals. The global chicken egg production in 2018 was over 76.7 million metric tons. In industrialized countries, about 30% of eggs are processed at breaker plants that produce liquid egg products and large quantities of solid ES waste. ES waste is utilized for a variety of low-value applications, or alternatively is disposed in landfill with associated economic and environmental burdens. The number of patents pertaining to ES applications has increased dramatically in recent years; of 673 patents granted in the last century, 536 (80%) were published in the last two decades. This review provides a snapshot of the most recent patents published between 2015 and 2020, with emphasis on different biotechnological applications of ES waste, and summarizes applications for biomedical, chemical, engineering, and environmental technologies. Biomedical technologies include the production of calcium lactate, calcium phosphate, and health-promoting products, while chemical technologies include plant growth promoters, food processing and production, and biodiesel oil catalysis along with active calcium, carbon, soluble proteins, organic calcium, and ultrafine calcium carbonate sources. Engineering technologies address material engineering and nanoparticle production, while environmental technologies pertain to production of biomass, solubilization of sludge as well as production of magnetic ES adsorbents and adsorption of heavy metals, organics, total nitrogen and fluoride, soil pollutants, and radioactive compounds. Although the number of ES-based patents has exponentially increased in the last decade, exploration of innovative top-down approaches and ES development as a physical platform are new endeavors that are expected to further increase the upscaling of ES waste exploitation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".