Antimicrobial Assessment of Zinc Oxide Nanoparticles Synthesized from <i>Psidium guajava</i> Stem Extract
Bibliographic record
Abstract
Bio-synthesizing metal nanoparticles have gained global attention and interest because of their fast, non-toxic, cost-effective, one-step process, and environmentally friendly alternative. For its chemical, physical characteristics, nanoparticles have attracted a lot of interest in different areas, such as materials science and medicine, as well as electronics. The research is centered on the investigation of phytochemicals, and the subsequent characterization of these particles. The study also delves into the potential biological applications of zinc nanoparticles (ZnONPs). The Psidium guajava stem extract was utilized for ZnONPs synthesis. A comprehensive phytochemical analysis was performed on the extracted stem extract, revealing the presence of a diverse range of phytochemicals such as phenols, saponins, triterpenoids, steroids, tannin, catechin, and glycosides. The application of zinc ions to the bark extract resulted in the biosynthesis ZnONPs, which were then analyzed by UV-Vis spectral studies. The ZnONPs exhibited a surface plasmon resonance band at 368 nm and had a crystalline structure according to the X-ray diffraction spectrum. The scanning electron microscope revealed that the nanoparticles had a polydisperse distribution and a particle size ranging from 30 to 35 nm. The identification of the biomolecule group involved in the synthesis was made possible through the recording of the FTIR spectrum. The antibacterial activity of the ZnONPs was tested using the disc diffusion method against gram positive and negative bacteria. The results showed that the maximum zone of inhibition of ZnONP’s was 16±0.1 mm against P. aeruginosa and minimum zone of inhibition was against S. aureus 8±0.3 mm.
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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".