Novel Bioactive Antimicrobial Lignin Containing Coatings on Titanium Obtained by Electrophoretic Deposition
Bibliographic record
Abstract
Hydroxyapatite (HAP) is the most suitable biocompatible material for bone implant coatings; its brittleness, however, is a major obstacle, and the reason why research focuses on creating composites with biopolymers. Organosolv lignin (Lig) is used for the production of composite coatings, and these composites were examined in this study. Titanium substrate is a key biomedical material due to its well-known properties, but infections of the implantation site still impose a serious threat. One approach to prevent infection is to improve antimicrobial properties of the coating material. Silver doped hydroxyapatite (Ag/HAP) and HAP coatings on titanium were obtained by an electrophoretic deposition method in order to control deposited coating mass and morphology by varying applied voltage and deposition time. The effect of lignin on microstructure, morphology and thermal behavior of biocomposite coatings was investigated. The results showed that higher lignin concentrations protect the HAP lattice during sintering, improving coating stability. The corrosion stability was evaluated in simulated body fluid (SBF) at 37 °C. Newly formed plate-shaped carbonate-HAP was detected, indicating enhanced bioactive performance. The antimicrobial efficiency of Ag/HAP/Lig was confirmed by its higher reduction of bacteria Staphylococcus aureus TL (S. aureus TL) than of HAP/Lig coating. Cytotoxicity assay revealed that both coatings can be classified as non-toxic against healthy immunocompetent peripheral blood mononuclear cells (PBMC).
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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".