Evaluation of process condition impact on copper and lead ions removal from water using goethite incorporated nanocomposite ultrafiltration adsorptive membranes
Bibliographic record
Abstract
Polyacrylonitrile (PAN) adsorptive membrane incorporated with nanosize-goethite (α-FeO(OH)) hydrous metal oxide particles (GNPs), prepared with optimal flux and Cu(II) removal in the previous study, was used to evaluate the process parameter on the Cu(II) removal. Box-Behnken Design (BBD) based on the Response Surface Methodology (RSM) was employed to evaluate the impact of Cu(II) feed solution characteristics such as pH, initial concentration of metal ion, and transmembrane pressure (TMP) on copper removal efficiency. The outcomes indicated that the RSM optimization technique could be utilized as an applicable method to find the optimum condition for the maximum Cu(II) removal with slight variance compared with the experimentally measured data. The effect of each process parameter and the coupling effect of parameters on the Cu(II) removal was assessed. Finally, the optimum condition of pH, Cu(II) concentration, and transmembrane pressure (TMP) to obtain high copper removal efficiency was decided. In the optimum condition of the Cu(II) removal, the removal of lead (Pb(II)) metal ion was evaluated by the same membrane.
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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".