Adsorption of <scp>Cu</scp> ( <scp>II</scp> ) ions from aqueous solutions using ion exchange resins with different functional groups
Bibliographic record
Abstract
Abstract The extraction of heavy metals from industrial effluents using efficient adsorbents is crucial for wastewater treatment and beneficial for metal recycling. In this study, the removal of Cu(II) from an acidic solution by commercial resins Dowex G‐26 and Puromet™ MTS9570 was investigated. The influences of contact time, solution concentration, pH, temperature, and a resin dosage on the adsorption process were studied with batch technique. The optimum adsorption conditions were obtained at a concentration of 1100 mg/L Cu, contact time of 30 min, pH 3.5, and resin dosage of 0.025 g/ml for the removal of 99.9% and 90% of copper ions by G‐26 and MTS9570, respectively. The experimental data of copper adsorption were analyzed using the Langmuir, Freundlich, and Temkin isotherm models. The highest metal uptakes of 41.67 and 37.70 mg/g were observed for Dowex G‐26 and MTS9570, respectively. It was found that both resins had higher adsorption capacities than the substances reported in the literature. The adsorption kinetic studies showed that the copper adsorption process could be better described by the pseudo‐second order model. Adsorption occurs spontaneously under endothermic conditions, which indicates the endothermic nature of the process.
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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) | 0.001 | 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".