Adsorptive removal of dyes from synthetic and real textile wastewater using magnetic iron oxide nanoparticles: Thermodynamic and mechanistic insights
Bibliographic record
Abstract
Abstract Magnetic iron‐oxide nanoparticles exhibit high efficiency in wastewater treatment for many important reasons, including that they can remove contaminants from wastewater rapidly owing to their high external surface area/unit mass and interstice reactivity. Additionally, this type of iron oxide can easily be separated using a magnet after finishing the treatment process, can be used as a catalyst for the decomposition of adsorbed contaminants and thus reduce sludge formation, and can cost‐effectively meet the environmental regulations for wastewater treatment since it can be prepared in situ where treatment is needed via various techniques. In this study, we use magnetic iron oxide nanoparticles for dye removal from synthetic and real textile wastewater for the first time. The effects of different experimental parameters on dye removal, such as contact time, initial concentration, solution pH, and coexisting ions, were investigated. Computational modelling of the interaction of different dye molecules with different surfaces of γ‐Fe 2 O 3 nanoparticles is performed to obtain more mechanistic insights on the adsorption behaviour. The results showed that dye adsorption was fast, as external adsorption was dominated. The adsorption equilibrium data fit very closely to the Langmuir adsorption isotherm model, confirming monolayer adsorption, which is supported by the adsorption computational calculations. The adsorption was spontaneous, endothermic, and physical in nature.
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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".