Green Synthesis of Silver Nanoparticles with Extracts from Kalanchoe fedtschenkoi: Characterization and Bioactivities
Bibliographic record
Abstract
In this work, the hexane, chloroform, and methanol extracts from Kalanchoe fedtschenkoi were utilized to green-synthesize silver nanoparticles (Kf1-, Kf2-, and Kf3-AgNPs). The Kf1-, Kf2-, and Kf3-AgNPs were characterized by spectroscopy and microscopy techniques. The antibacterial activity of AgNPs was studied against bacteria strains, utilizing the microdilution assay. The DPPH and H2O2 assays were considered to assess the antioxidant activity of AgNPs. The results revealed that Kf1-, Kf2-, and Kf3-AgNPs exhibit an average diameter of 39.9, 111, and 42 nm, respectively. The calculated ζ-potential of Kf1-, Kf2-, and Kf3-AgNPs were −20.5, −10.6, and −7.9 mV, respectively. The UV-vis analysis of the three samples demonstrated characteristic absorption bands within the range of 350–450 nm, which confirmed the formation of AgNPs. The FTIR analysis of AgNPs exhibited a series of bands from 3500 to 750 cm−1, related to the presence of extracts on their surfaces. SEM observations unveiled that Kf1- and Kf2-AgNPs adopted structural arrangements related to nano-popcorns and nanoflowers, whereas Kf3-AgNPs were spherical in shape. It was determined that treatment with Kf1-, Kf2-, and Kf3-AgNPs was demonstrated to inhibit the growth of E. coli, S. aureus, and P. aeruginosa in a dose-dependent manner (50–300 μg/mL). Within the same range, treatment with Kf1-, Kf2-, and Kf3-AgNPs decreased the generation of DPPH (IC50 57.02–2.09 μg/mL) and H2O2 (IC50 3.15–3.45 μg/mL) radicals. This study highlights the importance of using inorganic nanomaterials to improve the biological performance of plant extracts as an efficient nanotechnological approach.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".