Evaluation of antimicrobial, anti-inflammatory and cytotoxic effects of silver nanoparticles synthesised from <i>Cynodon dactylon</i>
Bibliographic record
Abstract
Plant mediated synthesis of metal nanoparticles (MNPs) has been considered as a reliable green technique for mitigating the involvement of toxic chemicals and which is widely used for desired applications. In the present study, a simple and environment friendly approach for the synthesis of silver nanoparticles (AgNPs) using the aqueous extract of Cynodon dactylon was proposed. The phytochemicals present in C. dactylon acted as the reducing as well as the capping agents during the nanoparticle synthesis. The aqueous extract of C. dactylon added to AgNO3 solution showed a colour change from brown to black at room temperature which confirmed the formation of AgNPs. UV-Vis spectral analysis revealed the surface plasmon resonance band of synthesised AgNPs at around 380 nm, while FT-IR spectroscopy confirmed the role of biomolecules present in the plant extract in the reduction and efficient stabilisation of AgNPs. The X-ray diffraction (XRD) patterns confirmed distinctive peaks corresponding to the crystalline planes of cubic silver. Shape and surface morphology of green AgNPs were examined by SEM. Biosynthesized AgNPs were predominantly cubical and spherical with an average particle size of 30.5 nm approximately as observed through SEM and DLS analysis respectively. The EDS analysis displayed intense signals of silver element. The stability of AgNPs was confirmed by zeta potential analysis. A negative zeta potential value of −17.1 mV indicated the stability and good dispersion of AgNPs. Antimicrobial and anti-inflammatory potentials of green synthesised AgNPs were analysed through in vitro techniques. The cytotoxic effect of green AgNPs on normal fibroblast cells (L929) was studied to analyse its effect on normal cells.
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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".