Continuous packed bed column removal of Cr <sup>6+</sup> , Cd <sup>2+</sup> , and Pb <sup>2+</sup> ions from synthetic wastewater using polymeric ultra‐permeable and biodegradable ferromagnetic nanocomposite membrane
Bibliographic record
Abstract
Abstract Water purification techniques, including membrane technologies, ion exchange and adsorption, chemical/biochemical reduction, and electrochemical processes, have been developed to remove/recover metal ions species from polluted wastewater. This work assessed the efficiency of polymeric, biodegradable, ultra‐permeable and magnetic nanocomposite membrane (CNCs/N6@Fe 3 O 4 ‐CT) in a continuous packed bed column for the removal of Cd(II), Cr(VI), and Pb(II) metal ions from synthetic wastewaters. The eco‐compatibility of CNCs/N6@Fe 3 O 4 ‐CT was increased using chitosan biopolymer. Fe 3 O 4 nanoparticles increased the surface area and improved the separation process. CNCs and N6 polymeric materials enhanced their strength, porosity, and additional binding sites. The CNCs/N6@Fe 3 O 4 ‐CT nanocomposite membrane was employed as packing material in a fixed‐bed lab‐scale column (height 30 cm, diameter 1.5 cm) to constantly remove Cd(II), Cr(VI), and Pb(II) metal ions from synthetic wastewaters and actual hexavalent chromium tannery effluent. The studies were carried out with different initial metal ion concentrations (10, 20, and 30 mg/L), input flow rates (2, 4, and 6 mL/min), and solution pH values (2.0, 5.0, and 8.0). The obtained experimental data from the breakthrough curves was fitted to the traditional dynamic Thomas model, Yoon‐Nelson, and Bed Depth Service Time (BDST) model.
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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) | 0.000 | 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".