Green Synthesis of CuO and ZnO Nanoparticles using Eryngium foetidum Leaf Extract: Mechanistic Aspects, Antimicrobial, and Antioxidant Activities
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Bibliographic record
Abstract
Introduction: The green synthesis of nanomaterials offers notable advantages like environmental sustainability, low toxicity, and cost-effectiveness. Herein, Eryngium foetidum leaf extract was used for green synthesis of CuO and ZnO nanoparticles (NPs). Materials and methods: The synthesized NPs, yielded approximately 2 grams after being calcined for 6 hours at 400ºC. They were characterized by UV-vis spectroscopy, FTIR, XRD, FESEM, TEM, and EDX analysis. UV-V initially confirmed the formation of nanoparticles is spectroscopy, which showed λmax at 356 nm and 364 nm for CuO NPs, ZnO NPs, respectively. Results: The results of TEM analysis displayed that the prepared CuO and ZnO NPs were elliptical and rod-shaped, having particle sizes of 50.02 nm and 31.95 nm, respectively. Discussion: FTIR and HPLC analysis showed involvement of various polyphenols, including chlorogenic acid and quercetin, available in the leaf extract of E. foetidum, in the reduction and stabilization of Cu2+ to Cu0 and Zn2+ to Zn0. The synthesized nanoparticles exhibited strong anti- bacterial activities against four pathogenic bacterial strains, namely Enterbacter aerogenes, Staphylococcus aureus, Escherichia coli, and Bacillus subtilis; however, CuO NPs (E. coli, 38.5 mm>E. aerogenes, 29.25 mm>B. subtilis, 29.03 mm>S. aureus, 28.0 mm) exhibited higher antimicrobial activities than the ZnO NPs (B. subtilis, 22 mm>E. coli, 16 mm>S. aureus, 15 mm>E. aerogenes, 14.15 mm). Additionally, both the synthesized nanoparticles displayed good antioxidant activities with IC50 of 1.87 mg/mL for CuO NPs, and 0.985 mg/mL for ZnO NPs. Conclusion: The results showed that the synthesized CuO NPs and ZnO NPs can be used as promising antimicrobial agents, and antioxidants.
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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 | 0.001 |
| 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