An Eco-Friendly Process for Removal of Lead from Aqueous Solution
Bibliographic record
Abstract
The adsorption of lead (Pb II) ion on different types of synthesized zeolite was investigated. The BET surface area, total pore volume & average pore size distribution of these synthesized zeolites were determined by adsorption isotherms for N2, the surface area & total pore volume of their sources were found by adsorption isothermN2.The adsorption equilibrium was measured after 24h at room temperature (RT) & concentration 10mg.L-1 of Pb (II) was used. The adsorption of heavy metal Pb (II) on four different prepared zeolites (LTA from Montmorillonite clay, FAU(Y)-B.H (G2) from Barley husk, Mordenite (G1) from Chert rock, FAU(X)-S.C (G3) from shale clay & modified Shale clay by oxalic acid (N1) & sodium hydroxide (N2)), were compared with the adsorption of their sources by using static batch experimental method. The major factors affecting the heavy metal ion sorption on different synthesized zeolites & their sources were investigated. The adsorption equilibrium capacity (Qm) of Pb (II) ion for different synthesized zeolites ordered from (N1>N2>LTA>G3>G2>G1&for their sources ordered Shale clay >Montmorilonite> Barley husk>Chert rock. The atomic absorption spectrometry was used for analysis of lead heavy metal ion, the obtained results in this study showed that the different synthesized zeolites were efficient ion exchanges for removing heavy metal, in particular, the modified zeolite from shale clay by oxalic acid.
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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) | 0.001 | 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".