Synthesis of three-dimensional calcium carbonate nanofibrous structure from eggshell using femtosecond laser ablation
Bibliographic record
Abstract
BACKGROUND: Natural biomaterials from bone-like minerals derived from avian eggshells have been considered as promising bone substitutes owing to their biodegradability, abundance, and lower price in comparison with synthetic biomaterials. However, cell adhesion to bulk biomaterials is poor and surface modifications are required to improve biomaterial-cell interaction. Three-dimensional (3D) nanostructures are preferred to act as growth support platforms for bone and stem cells. Although there have been several studies on generating nanoparticles from eggshells, no research has been reported on synthesizing 3D nanofibrous structures. RESULTS: In this study, we propose a novel technique to synthesize 3D calcium carbonate interwoven nanofibrous platforms from eggshells using high repetition femtosecond laser irradiation. The eggshell waste is value engineered to calcium carbonate nanofibrous layer in a single step under ambient conditions. Our striking results demonstrate that by controlling the laser pulse repetition, nanostructures with different nanofiber density can be achieved. This approach presents an important step towards synthesizing 3D interwoven nanofibrous platforms from natural biomaterials. CONCLUSION: The synthesized 3D nanofibrous structures can promote biomaterial interfacial properties to improve cell-platform surface interaction and develop new functional biomaterials for a variety of biomedical 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".