Effects of metal oxide nanoparticles with plant extract on viability of foodborne pathogens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The present study tested the antibacterial activity, expressed as minimum bactericidal concentration (MBC), of zinc oxide nanoparticles (ZnO‐NPs), copper oxide nanoparticles (CuO‐NPs) and their combination with or without rosemary, clove or cinnamon extract against Escherichia coli O157:H7 and Listeria monocytogenes . The NPs were characterized by scanning electron microscopy. The sizes of ZnO‐NPs and CuO‐NPs were in the range of 56–71 and 171–204 nm, respectively. Results showed that ZnO‐NPs had a greater inhibitory effect against both E. coli O157:H7 and L. monocytogenes than CuO‐NPs. The MBC of ZnO‐NPs against E. coli O157:H7 and L. monocytogenes was 300 and 350 μg/mL, respectively, while the MBC of CuO‐NPs was >1,000 and 400 μg/mL, respectively. When combined, ZnO‐NPs and CuO‐NPs had additional inhibitory effects against L. monocytogenes , but not against E. coli O157:H7. In general, the antibacterial activity of the NPs against E. coli O157:H7 and L. monocytogenes was enhanced by rosemary or cinnamon extract. Incorporation of clove extract into the NPs improved the antibacterial effect against E. coli O157:H7, but not against L. monocytogenes . Thus, plant extracts may be useful adjuncts for the synthesis of ZnO‐NPs or CuO‐NPs which can be used to control foodborne pathogens. Practical Application Incorporation of plant extracts in the synthesis of metal oxide nanoparticles (NPs) can be applied to improve the antimicrobial activity of NPs against foodborne pathogens.
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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