Synthesis of Chitosan Capped Zinc Sulphide Nanoparticle Composites as an Antibacterial Agent for Liquid Handwash Disinfectant Applications
Bibliographic record
Abstract
There is a need to develop alternative disinfectants that differ from conventional antibiotics to address antibacterial resistance, along with specialized materials for biomedical applications. Herein, we report on the synthesis of zinc sulfide (ZnS) capped with chitosan (CS) to produce CS-ZnS nanocomposites (NCs), which were assayed for antibacterial activity in liquid handwash formulations. The CS-ZnS NCs were prepared using the bottom-up wet-chemical method. The role of CS as the capping agent was investigated by varying the ratio of CS with respect to the ZnS precursor. The prepared CS-ZnS NCs were characterized using complementary spectral methods: scanning electron microscopy–energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and X-ray diffraction. The antibacterial activities of liquid handwash (LH) formulations containing 1% (w/w) CS-ZnS NCs were tested against Staphylococcus aureus and Escherichia coli using the agar diffusion test method. This LH formulation displayed antibacterial activity against S. aureus with an average inhibition zone diameter in the range of 16.9–19.1 mm, and met the quality standards set by the National Standardization Agency. The formulated LH solutions containing CS-ZnS NCs showed antibacterial activity, which suggests that the CS-ZnS NCs have potential as an alternative active ingredient for tailored and non-irritant antibacterial LH detergents.
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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".