Polymeric Composites with Embedded Nanocrystalline Cellulose for the Removal of Iron(II) from Contaminated Water
Bibliographic record
Abstract
The exponential increase in heavy metal usage for industrial applications has led to the limited supply of clean water for human needs. Iron is one of the examples of heavy metals, which is responsible for an unpleasant taste of water and its discoloration, and is also associated with elevated health risks if it persists in drinking water for a prolonged period of time. The adsorption of a soluble form of iron (Fe2+) from water resources is generally accomplished in the presence of natural or synthetic polymers or nanoparticles, followed by their filtration from treated water. The self-assembly of these colloidal carriers into macroarchitectures can help in achieving the facile removal of metal-chelated materials from treated water and hence can reduce the cost and improve the efficiency of the water purification process. In this study, we aim to develop a facile one-pot strategy for the synthesis of polymeric composites with embedded nanocrystalline cellulose (NCC) for the chelation of iron(II) from contaminated water. The synthesis of the polymeric composites with embedded nanoparticles was achieved by the facile coating of ionic monomers on the surface of NCC, followed by their polymerization, crosslinking, and self-assembly in the form of three-dimensional architectures at room temperature. The composites prepared were analyzed for their physiochemical properties, antifouling properties, and for their iron(II)-chelation efficacies in vitro. The results indicate that the embedded-NCC polymeric composites have antifouling properties and exhibit superior iron(II)-chelation properties at both acidic and basic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".