Optimization of Synthesis of Silver Nanoparticles Conjugated with Lepechinia meyenii (Salvia) Using Plackett-Burman Design and Response Surface Methodology—Preliminary Antibacterial Activity
Bibliographic record
Abstract
In the present investigation, an ethanolic fraction (EF) of Lepechinia meyenii (salvia) was prepared and fractionated by gradient column chromatography, and the main secondary metabolites present in the EF were identified by HPLC-MS. Silver nanoparticles (AgNPs) were synthesized and conjugated with the EF of Lepechinia meyenii (salvia). The AgNPs synthesis was optimized using Plackett-Burman design and response surface methodology (RSM), considering the following independent variables: stirring speed, synthesis pH, synthesis time, synthesis temperature and EF volume. The AgNPs synthesized under the optimized conditions were characterized by UV visible spectroscopy (UV-VIS), Fourier Transform Infrared Spectroscopy (FT-IR), Dynamic Light Scattering (DLS) and Scanning Transmission Electron Microscopy (STEM). The antibacterial activity of the AgNPs against Staphylococcus aureus (ATCC® 25923) was evaluated. The following flavonoids were identified: rosmarinic acid, diosmin and hesperetin-7-O-rutinoside. The optimized conditions for the synthesis of nanoparticles were pH 9.45, temperature 49.8 °C, volume of ethanolic fraction 152.6 µL and a reaction time of 213.2 min. The obtained AgNPs exhibited an average size of 43.71 nm and a resonance plasmon of 410–420 nm. Using FT-IR spectroscopy, the disappearance of the peaks between 626.50 and 1379.54 cm−1 was evident with the AgNPs, which would indicate the participation of these functional groups in the synthesis and protection of the nanoparticles. A hydrodynamic size of 47.6 nm was obtained by DLS, while a size of 40–60 nm was determined by STEM. The synthesized AgNPs conjugated with the EF showed a higher antibacterial activity than the EF alone. These results demonstrate that the AgNPs synthesized under optimized conditions conjugated with the EF of the Lepechinia meyenii (salvia) presented an increased antibacterial activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".