Facile biosynthesis of Ag–ZnO nanocomposites using Launaea cornuta leaf extract and their antimicrobial activity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The quest to synthesize safe, non-hazardous Ag-ZnO nanoomposites (NCs) with improved physical and chemical properties has necessitated green synthesis approaches. In this research, Launaea cornuta leaf extract was proposed for the green synthesis of Ag-ZnO NCs, wherein the leaf extract was used as a reducing and capping agent. The antibacterial activity of the prepared nanoomposites was investigated against Escherichia coli and Staphylococcus aureus through the disc diffusion method. The influence of the synthesis temperature, pH, and precursor concentration on the synthesis of the Ag-ZnO NCs and antimicrobial efficacy were investigated. The nanoparticles were characterized by ATR-FTIR, XRD, UV-Vis, FESEM, and TEM. The FTIR results indicated the presence of secondary metabolites in Launaea cornuta which assisted the green synthesis of the nanoparticles. The XRD results confirmed the successful synthesis of crystalline Ag-ZnO NCs with an average particle size of 21.51 nm. The SEM and TEM images indicated the synthesized nanoparticles to be spherical in shape. The optimum synthesis conditions for Ag-ZnO NCs were at 70 °C, pH of 7, and 8% silver. Antibacterial activity results show Ag-ZnO NCs to have higher microbial inhibition on E. coli than on S. aureus with the zones of inhibition of 21 ± 1.08 and 19.67 ± 0.47 mm, respectively. Therefore, the results suggest that Launaea cornuta leaf extract can be used for the synthesis of Ag-ZnO NCs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it