Surface characteristics and bioactivity of mussel-inspired coating for implant
Bibliographic record
Abstract
As a robust structural element for spontaneous deposition of bone-stimulating agents, tightly adherent polydopamine (PDA) layer was coated with titanium (Ti) by self-polymerisation to achieve a facile surface-modified orthopedic implant in this paper. The surface characteristics and bioactivity of the self-polymerised dopamine-coated titanium implant were investigated by spectroscopic ellipsometry, atomic force microscopy (AFM), contact angle test, X-ray photoelectron spectroscopy, electrochemical measurements and in vitro cell experiment. In the results, the thickness of self-polymerised dopamine film increased with time as measured by spectroscopic ellipsometry. A concavo-convex morphology of PDA film was observed by AFM, and the contact angle analysis showed an increase in surface hydrophilicity. Both the polarisation curves and electrochemical impedance spectroscopy demonstrated that the PDA film acts as a passive barrier, along with the spontaneously formed compact oxide layer, to further strengthen the corrosion resistance of titanium. In vitro MC3T3-E1 cell adhesion and alkaline phosphate activity on PDA-coated titanium were both significantly improved compared with those on the uncoated titanium. The self-polymerised PDA-coated titanium implant showed better hydrophilicity, higher corrosion resistance and enhanced bioactivity and was demonstrated as a reliable surface modification method.
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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) | 0.000 | 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".