Redox-Active Magnetic Composites for Anionic Contaminant Removal from Water
Bibliographic record
Abstract
Global water security is jeopardized by the presence of anthropogenic contaminants, which can persist resiliently in the environment and adversely affect human health. Surface adsorption of polluting species is an effective technique for water purification. In this work, redox-active magnetic compounds were designed for the targeted removal of inorganic and organic anions in water via polymeric redox-active vinylferrocene (VFc) and pyrrole (Py) moieties. An Fe 3 O 4 @SiO 2 @PPy@P(VFc- co -HEMA) composite was prepared in a four-step process, with the outermost layer possessing heightened hydrophilicity as a result of the optimized incorporation of 2-hydroxyethylmethacrylate (HEMA) monomers into the backbone of the ferrocene macromolecule. The synthesized materials are able to separate carcinogenic hexavalent chromium oxyanions and other charged micropollutants, and exhibit a 2-fold or greater enhancement in adsorption uptake once the redox-active ferrocene groups are oxidized to ferrocenium cations, with capacities of 23, 49, 66, and 95 mg/g VFc for maleic acid, 2-(6-methoxy-2-naphthyl)propionic acid (Naproxen), (2,4-dichlorophenoxy)acetic acid (2,4-D), and (2-dodecylbenzene)sulfonic acid (DBS), respectively, and a > 99% extractability of chromium in the 1 ppm range. The application of redox-active components to a magnetic particulate scaffold improves maneuverability and phase contact, giving rise to new potential aqueous separation process frameworks for water or product purification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".