Comparative Bioactivity Analysis of Green-Synthesized Metal (Cobalt, Copper, and Selenium) Nanoparticles
Bibliographic record
Abstract
Aim This study involves synthesizing metal nanoparticles (NPs) via the green synthesis method using Millettia pinnata leaf, Acacia auriculiformis bark, and Citrus sinensis peel and comparatively evaluating their antibacterial activity in vitro through the analysis of cobalt oxide NPs (CoNPs), copper NPs (CuNPs), and selenium NPs (SeNPs). This research contributes to eco-friendly approaches for producing functional nanomaterials with potential applications in medicine and environmental remediation. Materials and methods The metal NPs were synthesized using M. pinnata leaf, A. auriculiformis bark, and C. sinensis peel. These leaf extracts act as self-reducing and stabilizing agents. The antibacterial activity was assessed by the well diffusion method. Cultures of pathogenic bacteria species such as Staphylococcus aureus, Escherichia coli, Bacillus subtilis, and Pseudomonas aeruginosa were prepared. NPs were applied to the culture, and zones of inhibition (ZOIs) were measured. The data were statistically analyzed to compare the antibacterial efficacy of the different NPs. Results The successfully synthesized CoNPs, CuNPs, and SeNPs showed distinctive phytochemical properties. CoNPs exhibited the highest ZOI against most bacterial strains, with CuNPs and SeNPs following. CoNPs consistently showed superior performance compared to CuNPs and SeNPs. Conclusion Our study analyzed the bioactivity of metal NPs produced using green synthesis with plant extracts. CoNPs have shown superior antibacterial effectiveness against both Gram-positive and Gram-negative bacteria when compared to CuNPs and SeNPs. This may be due to their larger surface area, smaller size, unique electrical, magnetic, and catalytic properties, as well as their improved contact with the bacterial cell wall and membrane.
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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".