Impact of Pore Size on Fenton Oxidation of Methyl Orange Adsorbed on Magnetic Carbon Materials: Trade-Off between Capacity and Regenerability
Bibliographic record
Abstract
The economic cleanup of wastewater continues to be an active area of research. In this study, the influence of pore size on regeneration by Fenton oxidation for carbon materials with adsorbed methyl orange (MO) was investigated. More specifically three carbon supports, with pore sizes ranging from mainly microporous to half microporous-half mesoporous to mainly mesoporous, were impregnated with γ-Fe 2 O 3 to make them magnetic and easy to separate from solution. The carbon samples were characterized before adsorption and after regeneration with hydrogen peroxide at 20 °C. In addition, adsorption kinetics and isotherms were collected, and the Weber–Morris intraparticle diffusion model and Freundlich isotherm model fit to the data. The adsorption capacity increased with increasing microporosity while the regeneration efficiency increased with increasing mesoporosity. Further experiments with varying regeneration and adsorption conditions suggested that the regeneration process may be kinetically limited. The MO adsorbed in the micropores was strongly adsorbed and difficult to remove unlike the MO adsorbed in the mesopores, which could be reacted under relatively mild conditions. Thus, there was a trade-off between adsorption capacity and regeneration.
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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 | 0.001 |
| 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".