Phytochemical Investigation and Characterization of <i>Azadirachta Indica</i>-Mediated Silver Nanoparticles and Their Potential as Antibacterial and Antidiabetic Agents
Bibliographic record
Abstract
In this study, we report the synthesis of silver nanoparticles (AgNP’s) by reducing silver ions from a solution of silver nitrate with an aqueous extract from Azadirachta indica. Using silver ions as the catalyst, nanoparticles were formed in 8[Formula: see text]min without the use of toxic chemicals. As evidenced by UV-vis spectroscopy, a broad surface plasmon resonance spectrum at 225[Formula: see text]nm was detected, which indicates the colloidal solution of silver nanoparticles is stable and produces silver nanoparticles. As revealed by the fourier transform-infrared spectrometer (FTIR) analysis, the flower extract contained a variety of biomolecules that acted as capping and reducing agents for the synthesis of AgNPs. As determined by X-ray diffraction (XRD), silver nanoparticles displayed a crystalline structure and ranged in size from 18 to 39[Formula: see text]nm. In addition to these findings, transmission electron microscopy (TEM) images showed that the silver nanoparticles were spherical and rod-shaped, as confirmed by scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDAX). A variety of pathogenic bacteria, such as Klebsiella pneumoniae, Escherichia coli, Bacillus subtilis and Staphylococcus aureus, were tested against AgNP’s antibacterial properties. A significant inhibition zone was observed for Escherichia coli when AgNPs were applied at different concentrations. Silver nanoparticles were shown to have antidiabetic effects through a diphtheria assay with inhibition rates ranging from 31.09% to 83.33% for concentrations of 50–250[Formula: see text][Formula: see text]g/mL. As researchers seek natural sources of compounds with potential health benefits, silver nanoparticles were also investigated for their antioxidant properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".