Synthesis of Biogenic Silver Nanocatalyst and their Antibacterial and Organic Pollutants Reduction Ability
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Plant-mediated nanoparticles are gaining popularity due to biologically active secondary metabolites that aid in green synthesis. This study describes a simple, environmentally friendly, dependable, and cost-effective production of silver nanoparticles utilizing Cucumis sativus and Aloe vera aqueous leaf extracts. The aqueous leaf extracts of Cucumis sativus and Aloe vera, which worked as a reducing and capping agent, were used to biosynthesize silver nanoparticles (AgNPs). The formation of surface plasmon resonance peaks at 403 and 405 nm corresponds to the formation of colloidal Ag nanoparticles. Similarly, the Bragg reflection peaks in X-ray diffraction patterns observed at 2θ values of 38.01°, 43.98°, 64.24°, and 77.12° representing the planes of [111], [200], [220], and [311] correspond to the face-centered cubic crystal structure of silver nanoparticles. Fourier transform infrared spectroscopy confirms that bioactive chemicals are responsible for the capping of biogenic silver nanoparticles. The size, structure, and morphology of AgNPs with diameters ranging from 8 to 15 nm were examined using transmission electron microscopy. Water contamination by azo dyes and nitrophenols is becoming a more significant threat every day. The catalytic breakdown of organic azo dye methyl orange (MO) and the conversion of para -nitrophenol (PNP) into para -aminophenol using sodium borohydride was evaluated using the prepared biogenic nanoparticles. Our nanoparticles showed excellent reduction ability against PNP and MO with rate constants of 1.51 × 10 –3 and 6.03 × 10 –4 s –1, respectively. The antibacterial activity of the nanomaterials was also tested against four bacteria: Staphylococcus aureus, Klebsiella pneumoniae, Enterobacter, and Streptococcus pneumoniae . These biogenic AgNPs displayed effective catalytic and antibacterial characteristics by reducing MO and PNP and decreasing bacterial growth.
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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".