Photo‐Fenton‐peroxide process using FE (II)‐embedded composites based on activated carbon: Characterization of catalytic tests
Bibliographic record
Abstract
Abstract Cost‐efficient Fenton‐like catalysts for the removal of organic molecules in aqueous solutions were elaborated by a simple process. Porous activated carbons (ACs) were directly impregnated with a precursor solution using the wet impregnation method. Their efficiency as Fenton‐like catalysts was studied. Photo‐Fenton tests were performed to establish the performance of the prepared Fe‐impregnated activated carbons in relation to the degradation of an organic pollutant (Indigo Carmine) model in aqueous solution, under different conditions. Photo‐catalytic tests were carried out by means of a laboratory photo‐reactor (UV‐Consulting Peschl). The influence of several parameters such as solution pH value, initial concentration of the model pollutant, and hydrogen peroxide dose on the process performance was investigated. The ACs and prepared catalysts were characterized by nitrogen adsorption‐desorption isotherms at 77 K, SEM, and thermogravimetric analyses. The total Fe content of the synthesized composites was estimated by the phenanthroline method using UV‐vis spectrophotometry. The results show an increase in the degradation rate when the heterogeneous photo‐Fenton process is conducted with an ozone generating UV lamp.
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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".