Preparation and Characterization of TiO2-CaO-ZrO2/HDPE Hybrid Bio-Nanocomposites for Use in Orthopedic Applications
Bibliographic record
Abstract
In the domain of bone tissue engineering, the quest for suitable bone replacement materials that circumvent the limitations of metallic orthopedic implants is of paramount importance.Metallic implants, despite their wide application and success in orthopedic surgery, are often compromised by inadequate osteoconductive properties and risks of surface corrosion and infection, which can lead to tissue damage and fractures with consequent residual distortion.This study introduces novel bio-nano composite scaffolds fabricated utilizing nanosized fillers-8 mol% CaO-PSZ (partially stabilized zirconia) and TiO2 (titanium dioxide)-embedded within an HDPE (high-density polyethylene) matrix for potential orthopedic applications.The bio-nano composites were formed under varying compression pressures (29, 114 MPa) and a constant temperature of 150℃ for a duration of 15 minutes, resulting in disk-shaped specimens with a diameter of 14.7 mm and heights ranging from 7 to 10 mm.The aim was to determine the optimal thermal and physical properties of these hybrid composites (TiO2-CaO-PSZ/HDPE) for their use as bone substitutes.Characterization of the scaffolds was conducted via three distinct imaging modalities: Fourier transform infrared spectroscopy (FTIR) for chemical structure elucidation, atomic force microscopy (AFM) for topographical analysis, and differential scanning calorimetry (DSC) for thermal property assessment.The analysis confirmed that the incorporation of nanoparticle fillers into the HDPE matrix resulted in enhanced structural stability and mechanical interlocking at the atomic level.Furthermore, improvements in thermal behavior and crystallization degree were observed in direct correlation with the applied hot-press pressure and the presence of ceramic fillers.
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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".