GROWTH AND CHARACTERIZATION OF POLYMERIC MEMBRANE MODIFIED BY MAGNETIC NANOPARTICLES
Bibliographic record
Abstract
In this study, we aimed to modify polymeric membranes by incorporating magnetic nanoparticles (NPs) to enhance their properties. The structural and chemical properties of magnetic NPs of iron oxide were prepared via a wet chemical method. Iron oxide nanoparticles (IONPs) were used as the core and were coated with polymers polyvinyle alcohol (PVA) and polyvinylpyrrolidone (PVP). The prepared samples were cast on a glass substrate using a casting knife. The aim of this study is the use of a specific type of magnetic NPs, coated with a polymer, and their application in membrane modification. We employed a facile synthesis method to coat the IONPs with the polymer and characterized the resulting material using various techniques, including X-ray Diffraction (XRD), scanning electron microscope (SEM), Fourier Transform Infrared (FTIR) Spectroscopy, and UV/Visible (UV–Vis) Spectroscopy for structural, morphological, chemical bonding, and optical properties studies. Our results show that the modified polymeric membranes exhibited improved properties, such as increased permeability and selectivity. We also observed that the magnetic NPs helped in the easy recovery of the modified membranes using an external magnetic field. Some agglomeration of IONPs was also observed, and the polymer membrane caused a decrease in crystallinity of IONPs. Overall, this study presents a promising approach for enhancing the properties of polymeric membranes using magnetic NPs and can potentially have practical applications in various fields, such as water treatment, food processing, and biotechnology.
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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".