Facile fabrication and characterization of chitosan-based zinc oxide nanoparticles and evaluation of their antimicrobial and antibiofilm activity
Bibliographic record
Abstract
The present investigation deals with the facile synthesis and characterization of chitosan (CTS)-based zinc oxide (ZnO) nanoparticles (NPs) and their antimicrobial activities against pathogenic microorganisms. ZnO–CTS NPs were synthesized through two different methods: nano spray drying and precipitation, using various organic compounds (citric acid, glycerol, starch and whey powder) as stabilizers. Both the synthesis methods were simple and were devoid of any chemical usage. The detailed characterization of the NPs was carried out using UV–Vis spectroscopy, dynamic light scattering particle size analysis, zeta potential measurements and scanning electron microscopy, which confirmed the fabrication of NPs with different shapes and sizes. Antimicrobial assay of synthesized ZnO–CTS NPs was carried out against different pathogenic microbial strains ( Candida albicans , Micrococcus luteus and Staphylococcus aureus ). The significant ( p < 0.05) inhibition of growth was observed for both M. luteus and S. aureus with ZnO–CTS NPs (with a concentration ranging from 0.625 to 0.156 mg/ml) as compared to control treatment. ZnO–CTS NPs also showed significant biofilm inhibition activity ( p < 0.05) against M. luteus and S. aureus . The study demonstrated the potential of ZnO–CTS NPs as antimicrobial and antibiofilm agents.
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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".