Reduced Graphene Oxide/Barium Ferrite Ceramic Nanocomposite Synergism for High EMI Wave Absorption
Bibliographic record
Abstract
The developed nanocomposite exhibits significantly enhanced shielding performance due to the synergistic effect of high dielectric and magnetic loss materials, which modifies the material's impedance and improves its absorption ability. Different weight percentages (0, 1, 5, 10, 15, 20, and 25 wt %) of thermally treated chemically reduced graphene oxide (TCRGO) were combined with two types of magnets, barium hexaferrite (BF) and magnetite (MAG), using a dry powder compaction technique to produce binary ceramic nanocomposite sheets. The shielding performance of a 1 mm thick compressed nanoceramic sheet over the X-band was evaluated using a vector network analyzer. The 25% TCRGO showed high shielding performance for both BF and MAG, while BF had a total shielding efficiency (SET) that exceeded MAG by 130%. The SET of 25 wt % TCRGO/BF was 52 dB, with a 41 dB absorption shielding efficiency (SEA). Additionally, the effect of different levels of incident electromagnetic wave power (0.001-1000 mW) at various thicknesses (1, 2, and 5 mm) was explored. At 1000 mW, the 5 mm TCRGO/BF had an SET of 99 dB, an SEA of 91 dB, and a reflection shielding efficiency (SER) of 8 dB. The use of BF as a hard magnet paired with TCRGO exhibited excellent and stable electromagnetic shielding performance.
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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) | 0.001 | 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".