Structural, Magnetic, and Optical Properties of Iron-Doped Polyvinyl Alcohol (PVA) Films for Electromagnetic Shielding and Sensor Applications
Bibliographic record
Abstract
Pure PVA and Fe-doped composite films were synthesized using the solution casting method in the present work. Doping with iron (Fe) microparticles at weight percentages of 2% and 6%. The morphological, structural, magnetic, and optical properties of the films were investigated using X-ray diffraction (XRD), vibrating sample magnetometry (VSM), and attenuated total reflection (ATR) spectroscopy. XRD analysis revealed that the crystallite size of the iron microparticles, calculated using the Scherrer equation, was approximately 65.5 nm. Magnetic measurements showed a significant enhancement in saturation magnetization (Ms) from 1.6 x10-2 emu/g (2 wt% Fe) to 8.1x 10-2 emu/g (6 wt% Fe), along with increases in coercivity magnetization. The findings indicate that incorporating iron (Fe) particles into the PVA matrix created a new composite material with typical magnetic behavior. The observations demonstrate the material's potentiality towards sophisticated functional uses, such as sensors, electromagnetic shielding, and specialized electronic devices.
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) | 0.000 | 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".