Comparison of antimicrobial properties of inorganic peroxide polymer composites
Bibliographic record
Abstract
Abstract Wound healing and prevention of bacterial infections are critical aspects of modern medical care. In this work, antibacterial films were produced by creating composites of polycaprolactone with inorganic peroxides. Calcium, magnesium, and zinc peroxide were incorporated in a biocompatible polymeric film. Iron oxide, sodium bicarbonate, and calcium phosphate were added to reduce hydrogen peroxide and to maintain pH in a less alkaline range, allowing for optimization of the material's antibacterial efficacy while minimizing cytotoxicity toward human fibroblasts. Experiments with common wound pathogens, Staphylococcus aureus and Pseudomonas aerugonisa, confirmed significant and prolonged antibacterial effects of peroxide‐doped films. Findings showed that the addition of CaO2 and MgO2 within the film increased cytotoxicity toward human fibroblasts after 48 h (30%–40% decrease compared to control), whereas ZnO2‐based films exhibited a minimal cytotoxicity consistently maintaining over 70% cell viability throughout the course of the experiment. We examined the materials’ sustained release of reactive oxygen species and oxygen, and pH variation correlated with antibacterial activity. Given the unique combination of antibacterial efficacy and mammalian biocompatibility, these peroxides have value as components to sustain hydrogen peroxide release when appropriately compounded to reduce pH variation and avoid excessive hydrogen peroxide levels.
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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".