Povidone–Iodine-Based Polymeric Nanoparticles for Antibacterial Applications
Bibliographic record
Abstract
As microbial contamination is becoming more and more serious, antibacterial agents play an important role in preventing and removing bacterial pathogens from microbial pollution in our daily life. To solve the issues with water solubility and antibacterial stability of PVP–I 2 (povidone–iodine) as a strong antibacterial agent, we successfully obtain hydrophobic povidone–iodine nanoparticles (povidone–iodine NPs) by a two-step method related to the advantage of nanotechnology. First, the synthesis of poly( N -vinyl-2-pyrrolidone- co -methyl methacrylate) nanoparticles, i.e., P(NVP-MMA) NPs, was controlled by tuning a feed ratio of NVP to MMA. Then, the products P(NVP-MMA) NPs were allowed to undergo a complexation reaction with iodine, resulting in the formation of a water-insoluble antibacterial material, povidone–iodine NPs. It is found that the feed ratio of NVP to MMA has an active effect on morphology, chemical composition, molecular weight, and hydrophilic–hydrophobic properties of the P(NVP-MMA) copolymer after some technologies, such as SEM, DLS, elemental analysis, 1 H NMR, GPC, and the contact angle test, were used in the characterizations. The antibacterial property of povidone–iodine NPs was investigated by using Escherichia coli ( E. coli ), Staphylococcus aureus (S. aureus ), and Pseudomonas aeruginosa ( P. aeruginosa ) as model bacteria with the colony count method. Interestingly, three products, such as glue, ink, and dye, after the incorporation of povidone–iodine NPs, show significant antibacterial properties. It is believed that, with the advantage of nanoscale morphology, the final povidone–iodine NPs should have great potential for utilization in various fields where antifouling and antibacterial properties are highly required.
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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 | 0.001 | 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".