Development of Robust Chitosan–Silica Class II Hybrid Coatings with Antimicrobial Properties for Titanium Implants
Bibliographic record
Abstract
The purpose of this study was to develop robust class II organic–inorganic films as antibacterial coatings on titanium alloy (Ti6Al4V) implants. Coating materials were prepared from organic chitosan (20–80 wt.%) coupled by 3-glycydoxytrimethoxysilane (GPTMS) with inorganic tetraethoxysilane (TEOS). These hybrid networks were imbedded with antimicrobial silver nanoparticles (AgNPs) and coated onto polished and acid-etched Ti6Al4V substrates. Magic-angle spinning nuclear magnetic resonance (13CMAS-NMR), attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) and the ninhydrin assay, confirmed the presence and degree of covalent crosslinking (91%) between chitosan and GPTMS. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) identified surface roughness and microtopography on thin films and confirmed homogeneous distribution of elements throughout the coating. Cross-hatch and tensile adhesion testing demonstrated the robustness and adherence (15–20 MPa) of hybrid coatings to acid-etched titanium substrates. Staphylococcus aureus and Escherichia coli cultures and their biofilm formation were inhibited by all hybrid coatings. Antibacterial effects increased markedly for coatings loaded with AgNPs and appeared to increase with chitosan content in biofilm assays. These results are promising in the development of class II hybrid materials as robust and highly adherent antibacterial films on Ti6Al4V implants.
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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".