Enhanced Embedding of Cations into Titanium Surfaces by AC Plasma Electrolytic Oxidation for Osteointegrated Implants
Bibliographic record
Abstract
Titanium and its alloys represent the gold standard for osteointegrated implants, but their characteristic bioinertness still hinders their optimal integration within the host tissues. This limitation can be overcome by introducing osteoinductive functionalities on their surface. Plasma electrolytic oxidation (PEO) has emerged as a cost‐effective and rapid electrochemical method for generating bioactive titanium dioxide (TiO2) coatings, but the incorporation of pro‐osteogenic cations with this technique is typically passive and, in turn, characterized by a low efficiency. Here, alternate current (AC) PEO is investigated as a flexible solution to incorporate zinc into TiO2 coatings by exploiting the active transport of cations during the cathodic phase of the process. The resulting microporous surfaces show a greater zinc incorporation and an increased presence of rutile domains compared to conventional direct current (DC) PEO coatings, without, however, yielding significant morphological differences. In vitro assays with human mesenchymal stem cells (hMSCs) reveal an increased metabolic activity of cells adhering onto AC PEO surfaces. In addition, the increased expression of osteogenic differentiation markers (RUNX2 and osteocalcin) indicates significant surface‐driven osteoinductive effects, particularly for coatings grown by applying a short cathodic spike. Taken together, these aspects make Zn‐doped AC PEO surfaces a promising solution for osteoinductive orthopedic and dental applications.
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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".