Efficacy of Antimicrobial and Larvicidal Activities of Green Synthesized Silver Nanoparticles Using Leaf Extract of Plumbago auriculata Lam
Bibliographic record
Abstract
This work reports the synthesis of silver nanoparticles (AgNPs) using aqueous extract of Plumbago auriculata, and evaluates their antibacterial and larvicidal activities. The synthesized silver nanoparticles were characterized by various spectroscopy techniques, such as FTIR, XRD, TEM, EDX, Zeta potential, and DLS. The synthesized AgNPs exhibited significant antibacterial activity against Gram-positive and Gram-negative bacteria, such as Bacillus subtilis, Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae. Furthermore, synthesized nanoparticles inhibited the fourth instars larvae of Aedes aegypti and Culex quinquefasciatus at the concentration of 45.1 and 41.1 µg/mL respectively. Results of dose-dependent studies showed that synthesized nanoparticles were also effective at low concentrations. Molecular docking studies performed with the salivary protein and odorant-binding protein of Aedes aegypti and Culex quinquefasciatus demonstrated that the naphthoquinone compound plumbagin exhibited reliable binding affinity towards the two enzymes. The findings thus reveal that the plant extract and its nanoparticles can be a better alternative to available chemicals to control mosquitos.
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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".