Improved antibacterial properties of an Mg‐Zn‐Ca alloy coated with chitosan nanofibers incorporating silver sulfadiazine multiwall carbon nanotubes for bone implants
Bibliographic record
Abstract
Bacteria‐caused infection remains an issue in the treatment of bone defects by means of Mg‐Zn‐Ca alloy implants. This study aimed to improve the antibacterial properties of an Mg‐Zn‐Ca alloy by coating with chitosan‐based nanofibers with incorporated silver sulfadiazine (AgSD) and multiwall carbon nanotubes (MWCNTs). AgSD and MWCNTs were prepared at a weight ratio of 1:1 and then added to chitosan at varying concentrations (ie, 0, 0.25, 0.5, and 1.5 wt.%) to form composites. The obtained composites were ejected in nanofiber form using an electrospinning technique and coated on the surface of an Mg‐Zn‐Ca alloy to improve its antibacterial properties. A microstructural examination by scanning electron microscopy (SEM) revealed the diameter of chitosan nanofiber ejected increased with the concentration of AgSD‐MWCNTs. The incorporation of AgSD‐MWCNTs into the chitosan nanofibers was confirmed by Fourier transform infrared spectroscopy (FTIR). Examination of the antibacterial activity shows that chitosan nanofibers with AgSD‐MWCNTs can significantly inhibit the growth and infiltration of Escherichia coli and Staphylococcus aureus. Biocompatibility assay and cell morphology observations demonstrate that AgSD‐MWCNTs incorporated into nanofibers are cytocompatible. Taken together, the results of this study demonstrate the potential application of electrospun chitosan with AgSD‐MWCNTs as an antibacterial coating on Mg‐Zn‐Ca alloy implants for bone treatment.
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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".