Mechanistic Study of Methyl Orange Removal by Fe3O4@MIL-53(Fe Cu) Composite Material
Bibliographic record
Abstract
A novel magnetic composite, Fe3O4@MIL-53(Fe Cu), was successfully synthesized and applied for the efficient removal of methyl orange (MO) from aqueous solutions. The ad sorption performance was systematically evaluated under various conditions, including adsorbent dosage, solution pH, coexisting anions, and regeneration cycles. The results demonstrated that an optimal dosage of 20 mg achieved a removal efficiency exceeding 85%, with maximum adsorption observed at pH 3. The presence of common anions (Cl−, SO42−, CO32−, and PO43−) showed negligible effects on MO removal. Kinetic studies revealed that the adsorption process followed the pseudo-second-order model. Although minor chemisorption contributions were observed, the Dubinin–Radushkevich (D–R) model confirmed the predominance of physical adsorption. The Freundlich isotherm provided the best fit to the equilibrium data, indicating a maximum adsorption capacity of 193.65 mg/g and suggesting multilayer adsorption on a heterogeneous surface. Thermodynamic analysis confirmed the spontaneous and endothermic nature of the adsorption process. The primary mechanisms governing MO adsorption were identified as electrostatic attraction, π–π interactions, and hydrogen bonding. The composite exhibited excellent reusability over multiple cycles, demonstrating its potential for practical wastewater treatment applications.
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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".