Effects of metal oxide nanoparticles with plant extract on viability of foodborne pathogens
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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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) | 0.001 | 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) | 0.001 | 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".