Hydroxyapatite–TiO<sub>2</sub>-based Nanocomposites Synthesized in Supercritical CO<sub>2</sub> for Bone Tissue Engineering: Physical and Mechanical Properties
Bibliographic record
Abstract
Calcium phosphate-based nanocomposites offer a unique solution toward producing scaffolds for orthopedic and dental implants. However, despite attractive bioactivity and biocompatibility, hydroxyapatite (HAp) has been limited in heavy load-bearing applications due to its intrinsically low mechanical strength. In this work, to improve the mechanical properties of HAp, we grew HAp nanoplates from the surface of one-dimensional titania nanorod structures by combining a coprecipitation and sol-gel methodology using supercritical fluid processing with carbon dioxide (scCO2). The effects of metal alkoxide concentration (1.1-1.5 mol/L), reaction temperature (60-80 °C), and pressure (6000-8000 psi) on the morphology, crystallinity, and surface area of the resulting nanostructured composites were examined using scanning electron microscopy (SEM), transmission electron microscopy (TEM), powder X-ray diffraction (XRD), and Brunauer-Emmet-Teller (BET) method. Chemical composition of the products was characterized using Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and X-ray absorption near-edge structure (XANES) analyses. HAp nanoplates and HAp-TiO2 nanocomposites were homogeneously mixed within poly(ε-caprolactone) (PCL) to develop scaffolds with enhanced physical and mechanical properties for bone regeneration. Mechanical behavior analysis demonstrated that the Young's and flexural moduli of the PCL/HAp-TiO2 composites were substantially higher than the PCL/HAp composites. Therefore, this new synthesis methodology in scCO2 holds promise for bone tissue engineering with improved mechanical properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".