Preparation and Characterization of Structural and Biological Properties of Nano-Hydroxyapatite Composite Films with Saffron Dye Based on Polymethylmethacrylate
Bibliographic record
Abstract
This study aimed to prepare and characterize nano-hydroxyapatite, combine it with PMMA, and incorporate saffron dye to enhance the functionality of the composites.A HAP powder was created using fish bone remains and added to PMMA, with saffron dye in varying proportions (0.2, 0.4, 0.6) g.The dip coating method was used to synthesize films and analyze their structural characteristics, while established protocols for microbial cultivation and measuring inhibition zones were followed.It was very small, with a crystalline size of 45-55 nanometers and a width of 20-25 nanometers.After the mixing and compound formation process, the compounds (PMMA\Nano-HAP + saffron dye) were examined by FTIR spectroscopy in third proportions (0.2, 0.4, 0.6) g.Adding 0.6 g of nanoparticles fills all voids in the chloroform-formed polymer, creating a dense crystalline structure with a balanced distribution and agglomeration of HAP, ensuring complete filling for PMMA.The study evaluated the antibacterial efficiency of four bacteria, revealing antifungal activity against Candida albicans.The highest effective inhibition was 15 at 0.6% against Candida albicans.The highest inhibition ratios were 17 for S. aureus, 18 for S. epidermidis, 27 for P. aeruginosa, and 28 for E. coli.The polymer PMMA/Nano-HAP-saffron dye significantly impacts its mechanical properties and exhibits antibacterial and antifungal properties.
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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".