Synthesis of Hydroxyapatite from Egg Shell Bio-Waste for Use in Functionally Graded NiTi/HA Bone Implants
Bibliographic record
Abstract
Hydroxyapatite, a bio-ceramic material widely utilized in bioengineering, holds significant promise for bone implant applications due to its biocompatibility and nontoxic nature.This study focuses on the organic synthesis of hydroxyapatite with tailored nanoscale properties suitable for integration into functionally graded materials like NiTi/HA, intended for bone implants.Porous NiTi possesses desirable mechanical properties for such applications; however, its limited bioactivity poses a challenge for therapeutic use.Composite structures comprising porous NiTi and hydroxyapatite (HA) offer a viable solution to promote bone ingrowth and implant integration with surrounding tissue.Eggshells serve as the raw material in this research, subjected to calcination at 1000℃ for three hours to yield calcium oxide.Subsequent crushing and mixing with phosphoric acid, followed by milling using a planetary ball mill for twentytwo hours at 45 rpm, produces a homogeneous hydroxyapatite powder (HA).Comprehensive characterization using particle analysis, FTIR, SEM, and XRD confirms the desired properties of the synthesized powder.FTIR analysis verifies the presence of fundamental HA components, while XRD reveals a structure akin to traditional hydroxyapatite powder, featuring characteristic peaks corresponding to (PO4 3-), (CO3 2-), and bending OH -.Particle size analysis indicates a range of (0.301) µm to (4.759) µm, with a mean size of (1.088) µm.The findings of this study highlight that hydroxyapatite powder derived from eggshells exhibits favorable particle size, bioactivity, and porous nature, rendering it well-suited for incorporation into bone implant materials.
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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".