Thermal conductivity prediction of magnetic composite sheet for near-field electromagnetic absorption
Bibliographic record
Abstract
The magnetic composite sheets were designed by using core-shell structured magnetic fillers instead of uncoated magnetic fillers to resolve concurrently the electromagnetic interference and thermal radiation problems. To predict the thermal conductivity of composite sheet, we calculated the thermal conductivity of the uncoated magnetic fillers and core-shell structured fillers. And then, the thermal conductivity of the magnetic composites sheet filled with core-shell structured magnetic fillers was calculated and compared with that of the uncoated magnetic fillers filled in composite sheet. The magnetic core and shell material are employed the typical Fe-Al-Si flake (60 μm × 60 μm × 1 μm) and 250 nm-thick AlN with high thermal conductivity, respectively. The longitudinal thermal conductivity of the core-shell structured magnetic composite sheet (2.45 W/m·K) enhanced about 33.4% in comparison with that of uncoated magnetic fillers (1.83 W/m·K) for the 50 vol. % magnetic filler in polymer matrix.
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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".