Bioinspired gold-silver bimetallic nanoparticles from leaf and bark extract of Simarouba glauca and their antibacterial efficacy
Bibliographic record
Abstract
This work reports on using leaf and bark extracts from the Simarouba glauca as a natural reducing agent to synthesize gold-silver bimetallic nanoparticles (Ag-Au NPs). The leaf and bark extracts contain phytochemicals such as tannins, flavonoids, and others, confirmed by Fourier transform infrared spectroscopy (FTIR), responsible for the reduction of both Au and Ag ions. The Surface Plasmon Resonance (SPR) bands obtained at 540 and 543 nm, confirmed the formation Au-Ag alloy. The average crystallite size of Au-Ag NPs synthesized using leaf and bark extracts was 29 and 35 nm. TEM images show that the Au-Ag NPs were spherical, square, pentagonal, and hexagonal morphology. The bimetallic nanoparticles were tested against Staphylococcus aureus, Streptococcus mutans, Bacillus subtilis, and gram-negative bacteria Escherichia coli, Proteus vulgaris, and Klebsiella Pneumoniae showed effective zone of inhibition against the test bacteria. Among the two extracts, the leaf extract was found to be an effective reducing agent to form different shapes of bimetallic nanoparticles. The results indicate that Au-Ag NPs have effective antibacterial activity hence, these nanoparticles can be used for the development of antibacterial agents.
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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".