Launaea cornuta (wild lettuce) leaf extract: phytochemical analysis and synthesis of silver-zinc oxide nanocomposite
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Access to quality drinking water is an essential human right and a fundamental aspect of human dignity, yet a challenge to many in developing countries. Over 2 billion people worldwide lack access to quality drinking water due to microbial contamination, among other factors. Silver-doped zinc oxide impregnated activated carbon nanocomposites, Ag-ZnO-AC NCs, a strong antimicrobial agent have been used at point-of-use to treat water treatment. Green synthesis of Ag-ZnO-AC NCs has played a vital role since it leads to the acquisition of non-toxic nanocomposites compared to chemical synthesis. In this study, Ag-ZnO-AC NCs were green-synthesized using Launaea cornuta leaf extract as a source of reducing and capping agents in place of synthetic chemicals. Antioxidants from Launaea cornuta (Wild Lettuce) leaves were extracted using 0, 50, and 100% EtOH solvents with different volumes and extraction circles. The highest phenolic (11044 ± 63 μg) and antioxidant (44112 ± 894 μg) contents were extracted using 50% EtOH and 20 ml of extract solvent with p < 0.05. The SEM and TEM images of the synthesized Ag-ZnO-AC NCs show the formation of highly porous AC with sheet-like structures and successful Ag-ZnO NCs impregnation within the pores and on the surface of the AC. Based on the inhibition zone, the antimicrobial activity of the Ag-ZnO AC NCs had significant results with 14.00 ± 0.37 for E. coli and 17.33 ± 0.36 mm for S. aureus , p < 0.05. These results confirm the significance of Launaea cornuta (Wild Lettuce) as a source of antioxidants that can be used as reducing and capping agents to synthesize Ag-ZnO-AC 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