Biosynthesis, Characterization and Biological Applications of Silver Nanoparticles using Celosia trigyna and Solanum nigrum Extracts: Neglected Vegetables in Nigeria
Bibliographic record
Abstract
Plant-mediated synthesis is gaining acceptance in many fields i.e. biology and pharmaceutical fields. This aim of this study is synthesizing Ag nanoparticles using air-dried leaves of two (2) neglected vegetables i.e. Celosia trigyna and Solanum nigrum. Ultraviolet–visible spectroscopy, fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM) were used to characterize the formation of silver nanoparticles (AgNPs). The anti-inflammatory properties of these AgNPs were evaluated using Cell Stabilization Membrane (CSM) and lipoxidase assays, their antioxidant activity were established on DPPH and ABTS+ assays. The positive control employed are indomethacin and ascorbic acid for these activities. Nanoparticles synthesized were labelled for Celosia trigyna (CT-AgNPs) and Solanum nigrum (SN-AgNPs) were noticed through visual color change. The UV–Vis spectra of the synthesized nanoparticles displayed absorption bands at around 360-440 nm, which is a characteristic band for Ag and FTIR displayed possible functional groups responsible for Ag nanoparticles synthesized by these plants. The SEM image of the AgNPs formed displayed were spherical in morphology. CT-AgNPs exhibited the most significant inhibitory activity against HRBC (IC50: 32.2 µg/ml) while SN-AgNPs displayed the most significant inhibitory activity against lipoxygenases (IC50: 32.8 µg/ml) when compared to the positive control used indomethacin (IC50: 28.1 µg/ml). SN-AgNPs exhibited the most significant antioxidant effect against ABTS (IC50: 11.4 µg/ml) while CT-AgNPs displayed the most significant antioxidant activity against DPPH (IC50: 4.6 µg/ml) when compared to the positive control used ascorbic acid (IC50: 4.7 µg/ml). This work showed that the synthesized AgNPs from non-cultivated vegetable can find relevance and application in health, drugs, food and environmental science.
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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) | 0.000 | 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".