Magnetic nanocomposite beads: synthesis and uptake of Cu(II) ions from aqueous solutions
Bibliographic record
Abstract
Sodium alginate and magnetic iron oxide nanoparticles were combined to produce magnetic nanocomposite beads that were used for the removal of Cu(II) from aqueous solutions at a temperature of 25 °C and a stirring rate of 150 rpm. The different parameters affecting the adsorption capacity of the synthesized material such as contact time (30–270 min), pH (3–7), adsorbent dosage (5–10 g per 50 mL of wet beads), and initial Cu(II) concentration (50–450 mg/L) were investigated. Of all of the variables, the solution pH has the most significant effect on the adsorption capacity, particularly in the range of 4–6. Response surface methodology was used for modeling and optimizing the uptake process. While the experimental data were well described by the pseudo-second-order model, the adsorption isotherms were better fitted by the Langmuir equation. The results revealed that the maximum removal percentage was 92.6% from the initial Cu(II) concentration (150 mg/L) at pH 6, adsorbent dose (8.0 g/50 mL), and contact time (210 min). Therefore, the synthesized magnetic nanocomposite product could act as a highly effective nanoadsorbent in Cu(II) removal from the aqueous solutions.
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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 | 0.001 | 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 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".