Green Nanoparticles Investigation to Remove Water Pollutants by Fenton Reaction Using Celery Leaves Extract
Bibliographic record
Abstract
Wastewater containing dyes is a major source of environmental pollution. The purpose of this study is to evaluate the use of celery leaves extract employed to prepare zerovalent Iron nanoparticles (C-nZVFe) as a catalyst in Fenton reaction. The getting nanoparticles are then applied to decolorization aqueous solutions containing orange (OG) dyes. The C-nZVFe catalyst has been characterized by Fourier transformed infrared (FTIR) spectroscopy, X-ray diffraction (XRD), Field emission scanning electron microscopy (FESEM) techniques for the investigation of structural and surface morphology properties. The size and surface area of synthesized C-nZVFe are observed around 40 -55 nm. Using ultraviolet-visible (UVvis) spectroscopy, the amount of dye in the aqueous sol is observed. Orange G removal percentage (100 mg L -1 ) reached 86% [35 mg L -1 , 60 min, and pH 4]. C-nZVFe nanoparticles demonstrated more efficient percentage capacity as a Fenton catalyst removal and are more economical, efficient, and recyclable than other conventional Fenton oxidation catalysts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".