Combined Effect of Ultrasound Stimulations and Autoclaving on the Enhancement of Antibacterial Activity of ZnO and SiO2/ZnO Nanoparticles
Bibliographic record
Abstract
This study investigates the antibacterial activity (ABA) of suspensions of pure ZnO nanoparticles (ZnO-NPs) and mesoporous silica doped with ZnO (ZnO-UVM7), as well as electrospun nanofibers containing those nanoparticles. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of these two materials were also determined under the same conditions. The results showed a concentration-dependent effect of antibacterial nanoparticles on the viability of Escherichia coli (E. coli). Moreover, the combination of the stimulations and sterilization considerably enhanced the antimicrobial activity (AMA) of the ZnO suspensions. Poly (lactic acid) (PLA) solutions in 2,2,2-trifluoroethanol (TFE) were mixed with different contents of nanoparticles and spun into nonwoven mats by the electrospinning process. The morphology of the mats was analyzed by scanning electron microscopy (SEM). The amount of nanoparticles contained in the mats was determined by thermogravimetric analysis (TGA). The obtained PLA-based mats showed a fibrous morphology, with an average diameter ranging from 350 to 450 nm, a porosity above 85%, but with the nanoparticles agglomeration on their surface. TGA analysis showed that the loss of ZnO-NPs increased with the increase of ZnO-NPs content in the PLA solutions and reached 79% for 1 wt % of ZnO-NPs, which was mainly due to the aggregation of nanoparticles in solution. The ABA of the obtained PLA mats was evaluated by the dynamic method according to the ASTM standard E2149. The results showed that, above an optimal concentration, the nanoparticle agglomeration reduced the antimicrobial efficiency of PLA mats. These mats have potential features for use as antimicrobial food packaging material.
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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) | 0.001 | 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".