Kinetics, equilibrium and thermodynamic studies on the adsorptive removal of nickel, cadmium and cobalt from wastewater by superparamagnetic iron oxide nanoadsorbents
Bibliographic record
Abstract
Abstract Because of its unique properties, such as specific functionality and large specific surface area, iron oxide nanoadsorbents had showed potential for energy and environmental applications. This work investigated the adsorptive removal of different metal ions from wastewater by superparamagnetic iron oxide nanoadsorbents (Fe3O4). Batch‐adsorption technique was employed to assess the kinetic behaviour and adsorption equilibrium of cadmium, cobalt and nickel. Accordingly, the effect of the following variables on the adsorption reaction was tested, namely: solution pH, contact time and temperature. Metal ion adsorption was found to be highly pH dependent with a maximum uptake achieved around pH 5.5. Kinetic studies showed that adsorption was fast and equilibrium was achieved in less than 60 min. The external mass transfer kinetic model was applied to the experimental results and provided reasonable overall volumetric mass transfer coefficients. Adsorption isotherms were determined and appropriately described by the Freundlich and Langmuir models, with a better fit to the Freundlich model. The amount of metal ion adsorbed increased as the temperature increased, suggesting an endothermic adsorption process. The thermodynamics studies indicated that the adsorption process was spontaneous and endothermic in nature. © 2011 Canadian Society for Chemical Engineering
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".