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 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) | 0.000 | 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 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".