Sustainable synthesis of silver nanoparticles from Conocarpus seeds for removal of methylene blue
Bibliographic record
Abstract
This study introduces a sustainable biological approach for synthesizing silver nanoparticles (AgNPs) using Conocarpus seeds, aimed at improving the adsorption and photocatalytic degradation of methylene blue (MB) in wastewater treatment. The photocatalytic efficiency of AgNPs, synthesized under varying concentrations of silver nitrate (AgNO3) and pH levels, was evaluated, together with the effectiveness of a photocatalytic reactor. The synthesized samples were characterized using scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier-transform infrared (FT–IR) spectroscopy, and atomic force microscopy (AFM). Results showed that MB degradation occurred quickly within the first 50 min, achieving a 99.60% removal efficiency via adsorption and photocatalytic degradation under optimal conditions (pH = 3, 1 g sample) after 1 h. The maximum adsorption capacity reached 49.80 mg·g−1. Furthermore, the AgNPs demonstrated a significant degradation rate of 99.76% within 2 h under UV light, highlighting the synergistic effects of AgNPs in enhancing both adsorption and photocatalysis. This study not only accentuates the potential of Conocarpus seeds as an eco-friendly precursor for AgNP synthesis but also highlights the applicability of AgNPs in wastewater treatment.
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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".