Green Synthesis of CuO and ZnO Nanoparticles using Eryngium foetidum Leaf Extract: Mechanistic Aspects, Antimicrobial, and Antioxidant Activities
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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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".