Enhanced biological performance of Sr2+-doped nanorods on titanium implants by surface thermal-chemical treatment
Bibliographic record
Abstract
Abstract Titanium alloys, as artificial implants for orthopedic diseases, are prone to aseptic loosening and infection after surgery because their smooth surface restricts the attachment and movement of osteoblasts, resulting in a lack of osteogenic and antimicrobial properties. This study aimed to prepare SrTiO 3 nanostructures with varying Sr content on the surface of titanium through a thermal-chemical treatment, enhancing the osteogenic capacity of titanium while providing antibacterial properties. The results indicated that the SrTiO 3 nanostructures are primarily composed of pure titanium and SrTiO 3 phases, exhibiting a rod-like surface morphology. Sr is uniformly distributed across the surface of the samples, and increasing the Sr content does not alter the morphology of the nanostructures. Wettability tests demonstrated that the SrTiO 3 nanostructures exhibited superhydrophilicity, promoting cell adhesion. Electrochemical tests revealed that the SrTiO 3 nanostructures prepared on the titanium surface significantly enhanced its corrosion resistance. After 14 days of immersion in simulated body fluids, a significant amount of hydroxyapatite formed on the surface of STN3, indicating that the SrTiO 3 nanostructures possess good bioactivity. In vitro antimicrobial tests demonstrated that SrTiO 3 nanostructures were effective against both Escherichia coli and Staphylococcus aureus , with the antimicrobial rates increasing alongside the Sr content, reaching 48.1% and 38.6%, respectively. Graphical Abstract
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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".