Removal of Orange G by the Fenton process and<i>Ficus benjamina</i>nano-zerovalent iron
Bibliographic record
Abstract
Green chemistry using ficus leaf extract has become an ecological technique and was used for preparing zerovalent iron nanoparticles (nZVFe). The Ficus benjamina nano-zero-valent iron (FB-nZVFe) was characterised by scanning electron microscopy and Fourier transform infrared spectroscopy. The Orange G removal percentage (10 mg/l) reached 73% (0.3 g/l, 45 min and pH 4). The adsorption data agree more with the Langmuir model (R 2 = 0.9993), q max = 27.66 mg/g. The kinetic results showed that Orange G uptake follows a pseudo-second-order model. Langmuir isotherm and pseudo-second-order kinetic studies are more appropriate in linear and non-linear models. In general, F. benjamina nZVFe is a promising green material for Orange G removal. The IBM SPSS Statistics software was used to investigate the effect of different operational parameters, they were found to account for more than 98% of the variables affecting the removal procedure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".