Si, Sr, Ag co-doped hydroxyapatite/TiO<sub>2</sub>coating: enhancement of its antibacterial activity and osteoinductivity
Bibliographic record
Abstract
nanotubular (TNT) arrays with Sr-and-Si-substituted hydroxyapatite (SSHA) endowed with antibacterial characteristics using silver ions. This TNT layer coated with Ag-substituted SSHA (SSAgHA) formed a composite coating with an interconnected microporous structure and a homogeneous distribution of Sr, Si and Ag; such a coating promoted cell adhesion and osteogenic potential. The anchoring effect of the TNT layer improved the adhesion strength of the SSAgHA/TNT coating to 16.9 ± 3.1 MPa, which was higher than the 15 MPa set in the ISO standard 13 779-4:2002. Moreover, the bio-corrosion resistance of the underlying Ti substrate was greatly enhanced by the composite coating. Hydroxyapatite (HA) and SSAgHA coatings provided a suitable environment for the adhesion, spreading and proliferation of mouse osteoblasts. The SSAgHA coating excellently inhibited bacterial activity and enhanced osteoinductivity with higher osteogenic differentiation compared with the HA coating. Sr and Si dopants increased the expression levels of the genes related to osteogenesis and successfully offset the potential cytotoxicity of Ag ions. Super-osteoinductivity was attributed to the rough and superhydrophilic surface of the composite coating. Therefore, the present study demonstrated the potential of the electrodeposited SSAgHA/TNT composite coating as a promising metallic implant with great intrinsic antibacterial activity and osteointegration ability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".