Corrosion Stability and Bioactivity in Simulated Body Fluid of Silver/Hydroxyapatite and Silver/Hydroxyapatite/Lignin Coatings on Titanium Obtained by Electrophoretic Deposition
Bibliographic record
Abstract
Hydroxyapatite is the most suitable biocompatible material for bone implant coatings. However, its brittleness is a major obstacle, and that is why, recently, research focused on creating composites with various biopolymers. In this study, hydroxyapatite coatings were modified with lignin in order to attain corrosion stability and surface porosity that enables osteogenesis. Incorporating silver, well known for its antimicrobial properties, seemed the best strategy for avoiding possible infections. The silver/hydroxyapatite (Ag/HAP) and silver/hydroxyapatite/lignin (Ag/HAP/Lig) coatings were cathaphoretically deposited on titanium from ethanol suspensions, sintered at 900 °C in Ar, and characterized by X-ray diffraction, scanning electron microscopy, field emission scanning electron microscopy, attenuated total reflection Fourier transform infrared, and X-ray photoelectron spectroscopy. The corrosion stability of electrodeposited coatings was evaluated in vitro in Kokubo's simulated body fluid (SBF) at 37 °C using electrochemical impedance spectroscopy. Bioactivity was estimated by immersion in SBF to evaluate the formation of hydroxyapatite on the coating surface. A microcrystalline structure of newly formed plate-shaped carbonate-hydroxyapatite was detected after only 7 days, indicating enhanced bioactive behavior. Both coatings had good corrosion stability during a prolonged immersion time. Among the two, the Ag/HAP/Lig coating had a homogeneous surface, less roughness, and low values of contact angle.
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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".