Antibacterial activities of nano-crystalline cao, mgo and zno on lactic bacteria
Bibliographic record
Abstract
The antibacterial activities of nano-crystalline metallic oxides (CaO, MgO and ZnO), prepared by sonication-thermal decomposition method, and micrometric metal oxides were evaluated on three strains of lactic bacteria, Lactobacillus plantarum, Lactobacillus helviticus and Leuconostoc mesenteroides, and on spores of Alicyclobacillus acidoterresris involved in fruit juice spoilage. The effects of particle size, pH, concentration and exposure time on the viability were examined in physiological solution as well as culture broth. The tests were performed by adding the bacterial or spore suspensions in flasks containing metal oxides. The results showed that CaO nanoparticle was the most effective in killing all the three strains of lactic bacteria exposed for 24 h at 100 ppm, but its antibacterial activity is attributable in part to its pH effect. MgO was lethal against L. helveticus, but exhibited little effect against either L. mesenteroides or L. plantarum. The nanoparticular ZnO clearly showed a bactericidal effect on all lactic bacteria tested, but less so against L. helveticus, and it was also more effective in inhibiting their growth compared with alkaline metal oxides. No antibacterial effect of the metal oxides was observed against the spores of A. acidoterrestris. This investigation showed that, in general, higher concentration, longer exposure and smaller particles size of metal oxides tend to increase their antibacterial effect. However, the aggregation of nanoparticles at high concentrations of metal oxides in the slurry tends to lower the antimicrobial action. It was also evident that the antibacterial activity of metal oxides depends on the type of metal oxide (alkaline or amphoteric) and on the morphological and physiological characteristics of the bacteria.
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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".