Capture of Lead Ions from Aqueous Solution by Mg/Fe-LDH Alginate Beads Prepared from Schanginia aegyptiaca and Scrap Iron
Bibliographic record
Abstract
This study aims to develop a novel sorbent using solid waste from scrap iron and Schanginia aegyptica.Magnesium and iron ions can be efficiently extracted from solutions derived from Salsola aegyptiaca and scrap iron, respectively.Sodium alginate beads are used to immobilize magnesium-iron nanoparticles.Using batch adsorption experiments, the prepared sorbent, termed magnesium/iron-layered double hydroxidesodium alginate beads (Mg/Fe-LDH-Na alginate beads), was tested for its ability to remove lead (Pb) ions from simulated wastewater.The optimal conditions for synthesizing the Mg/Fe-LDH beads were determined to be 3, 10, and 5 g, corresponding to the molar ratio, pH, and dosage, respectively.The best operating parameters were 120 minutes, initial pH 6, 0.5 grams of beads per 100 mL, and 250 rpm for an initial concentration (Co) of 10 mg/L to remove more than 90% of Pb 2+ ions.The reuse performance of the sorbent was evaluated under the same batch test conditions and Pb removal efficiency was 95.9% in the first cycle, decreasing to approximately 84.7% by the sixth cycle, indicating a decline in removal efficiency with repeated use.The sorption process is well described by the Langmuir model, which suggests a maximum adsorption capacity of 2.312 mg/g.The results indicate that the produced beads are highly reusable and reliable, making them useful for removing lead ions from water, particularly in practical applications.
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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".