Facile one step green synthesis of iron nanoparticles using grape leaves extract: textile dye decolorization and wastewater treatment
Bibliographic record
Abstract
The existing knowledge on the reactivity of green iron particles on textile dye and wastewater decolorization is very limited. In this study, the potential of green iron particles synthesized using grape leaves extract on reactive dye (reactive red 195, reactive yellow 145, reactive blue 4 and reactive black 5) decolorization were investigated. 95-98% of decolorization was achieved for all reactive dyes at 1.4-2.0 g/L of green iron. Maximum decolorization was attained at lower dye concentration and showed very little impact on decolorization when pH was increased from 3 to 11. The pseudo-first-order fit confirms the reaction between iron particles and dye molecules with rate constant 0.317-0.422 and it is followed by adsorption, data fit with pseudo-second-order model. Hence, not only adsorption but also the reduction process is involved in the reactive dye decolorization. Benzene, phenyl sodium, 2-chloro-1,3,5-triazine, naphthalene, sodium benzene sulfonate, benzene 1,2 di amine, anthracene-9,10 dione, aniline, phenol, benzene sulfonic acid were the major intermediates detected in dye decolorization and the respective reaction pathway is proposed. Green iron from grape leaves extract demonstrated better performance and it is recognized as the promising cost-effective material for textile 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".