Enhancement of the capability of hydroxyapatite formation on Zr with anodic ZrO<sub>2</sub> nanotubular arrays via an effective dipping pretreatment
Bibliographic record
Abstract
Hydroxyapatite (HA) depositions on metallic biomedical implants are widely applied to generate bioactive surfaces in simulated biological environments. Highly ordered anodic ZrO₂ nanotubes have attracted increasing interest for biomedical applications. However, previous reports showed that at least 14-28 days were required to obtain HA coating on ZrO₂ nanotubular arrays under biomimetic condition, thus capability to grow HA coating on ZrO ₂nanotubular at room temperature needs to be enhanced. In the present work, we demonstrate that ZrO₂ nanotubular arrays are suitable for an effective dipping treatment to induce more rapid HA coating. A series of ZrO₂ nanotubular arrays having different dimensions were fabricated in fluoride containing electrolyte. Then, we used a dipping treatment for biomimetic formation of an adhesive HA coating on the nanotubular arrays. The coatings formed rapidly using this procedure under biomimetic conditions and did not require a high-temperature annealing process. The as-formed ZrO₂ nanotubular arrays were treated using several dip-and-dry steps, through which the nanotubular arrays were filled and covered with calcium phosphate (CaP) nucleation sites. The specimens readily grew HA once immersed in the simulated biological fluid after 2 days immersion. The carbonated HA coating had several micron thickness after 8 days of immersion while only a thin layer of CaP were observed on annealed ZrO₂ nanotubes immersed in the same solution for the same duration. Tensile testing showed that bonding strength between HA coating and substrate was 21.6 ± 1.6 MPa. This treatment dramatically improves efficiency for promoting HA formation on anodic ZrO₂ nanotubes at room temperature.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".