Removal of Heavy Metal Water Pollutants (Co<sup>2+</sup> and Ni<sup>2+</sup>) Using Polyacrylamide/Sodium Montmorillonite (PAM/Na-MMT) Nanocomposites
Bibliographic record
Abstract
Abstract Nanocomposites composed of polyacrylamide and nanoclay were synthesized via free-radical cross-linking polymerization and used to adsorb Co2+ and Ni2+ ions from water. The polyacrylamide (PAM)/sodium montmorillonite (Na-MMT) nanocomposites were characterized by Fourier transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy to confirm the interaction between montmorillonite and the polymer matrix. The effects of pH and heavy metal ion concentration on the adsorption capacity of PAM/Na-MMT were evaluated to determine suitable operating conditions for further experiments. Batch adsorption experimental data were fitted to Langmuir and Freundlich models, which provided information about the adsorption mechanism and the adsorbent surface. The highest Ni2+ removal yield was found to be 99.3% using the 2:1 (w/w) nanocomposite at pH 6 in 100 ppm of Ni2+ solution. The Co2+ removal yield was 98.7% at pH 6 in 60 ppm of Co2+ solution using the 4:1 (w/w) nanocomposite. These results were higher than those obtained by polyacrylamide and nanoclay under the same conditions (removal yield between 87.40 and 94.50%), indicating that PAM/Na-MMT nanocomposites remove heavy metal water pollutants more efficiently and can be used as a novel adsorbent for further industrial 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) | 0.001 | 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".