Launaea cornuta (wild lettuce) leaf extract: phytochemical analysis and synthesis of silver-zinc oxide nanocomposite
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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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".