Facile biosynthesis of Ag–ZnO nanocomposites using Launaea cornuta leaf extract and their antimicrobial activity
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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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".