Green Synthesis of Silver nanoparticle and its application for removal of dye from aqueous solution
Bibliographic record
Abstract
The green synthesis of metallic nanoparticles paved the way to improve and protect the environment by decreasing the use of toxic chemicals and eliminating biological risks in biomedical applications. Plant mediated synthesis of metal nanoparticles is gaining more importance to its simplicity, rapid rate of synthesis of nanoparticles and eco-friendliness. In this paper, we report on biosynthesis of silver nanoparticles using aqueous shoot extract of P. hysterophorus at room temperature along with degradation of methyl orange dye. Uv-is spectral analysis showed peak at 430 nm. FT-IR studies reveal the presence of bioactive functional groups such as phenolic compounds, amine and aromatic ring are found to be capping and stabilizing agents of nanoparticles. The morphology of silver nanoparticles was found to be spherical as confirmed by SEM and TEM study. Fluorescent microscopy patterns also suggest the occurance of spherical shaped particles and XRF study suggest the presence of silver. Further, photocatalytic degradation of methylene blue was measured spectrophotometrically by using silver nanoparticles. Kinetics of dye degradation showed good agreement with pseudo-second-order rate equation. Thermodynamic parameters such as Gibbs free energy (∆G ◦ ), enthalpy (∆H ◦ ) and entropy (∆S ◦ ) were also calculated and it was found that the degradation of dye by AgNPs was a spontaneous, feasible and endothermic in nature. The results revealed the biosynthesized silver nanoparticles using P. hysterophorus was found to be impressive in degrading methylene blue.
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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".