Magnetic Zeolite: Synthesis and Copper Adsorption Followed by Magnetic Separation from Treated Water
Bibliographic record
Abstract
Zeolites are widely used in diverse applications, including the removal of heavy metals from wastewater. However, separating fine-sized zeolite particles from treated water is often a challenge. In this work, a novel method utilizing a colloidal polyvinyl alcohol (PVA) solution to bind iron oxide nanoparticles to a Linde Type A (LTA) zeolite was used to synthesize magnetic zeolite. Different zeolite–iron oxide nanoparticle loadings (10:1, 10:0.5, and 10:0.1) were used in batch adsorption experiments to investigate adsorption capacities and kinetics for Cu removal from an aqueous solution. The results showed that the magnetic zeolite maintained much of its adsorbent properties while facilitating a simplified process design. Thus, the adsorption capacity of pure LTA zeolite was found to be 262 mg/g for magnetic zeolite, with a 10:1 ratio—151 mg/g; 10:0.5—154 mg/g; and 10:0.1—170 mg/g. Magnetic separation was subsequently employed to remove the magnetic zeolite from the treated solution.
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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".