Effect of iron oxide nanoparticle size on electromagnetic properties of composite nanofibers
Bibliographic record
Abstract
Electrically conductive and magnetically permeable carbon nanofiber-based composites were developed using the electrospinning with subsequent heat treatment. The composite nanofiber contains a variable composition of magnetite nanoparticles with two different size regimes, ranging from superparamagnetic (10–20 nm) to ferromagnetic (20–30 nm). The composite nanofibers are then characterized using Scanning/Transmission Electron Microscopy, X-Ray Diffractometry, Raman Spectroscopy, four-point probe, and a Superconducting Quantum Interference Device. Electromagnetic Interference Shielding Effectiveness of pristine carbon nanofibers as well as electromagnetic composite nanofibers are examined in the X-band frequency region. Higher degree of graphitization, electrical conductivity, and magnetic strength are obtained for nanocomposites containing larger magnetite nanoparticles (20–30 nm). A transition from superpartamagnetic to ferromagnetic characteristics is observed during nanocomposite processing. Electromagnetic Interference Shielding Effectiveness of as high as 68 dB (in the working frequency of 10.4 GHz) is observed for composite nanofibers fabricated with larger magnetite nanoparticles carbonized at 900℃.
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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 | 0.001 |
| 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".