Synergistic Layered Design of Aerogel Nanocomposite of Graphene Nanoribbon/MXene with Tunable Absorption Dominated Electromagnetic Interference Shielding
Bibliographic record
Abstract
Abstract Electromagnetic pollution presents growing challenges due to the rapid expansion of portable electronic and communication systems, necessitating lightweight materials with superior shielding capabilities. While prior studies focused on enhancing electromagnetic interference (EMI) shielding effectiveness (SE), less attention is given to absorption‐dominant shielding mechanisms, which mitigate secondary pollution. By leveraging material science and engineering design, a layered structure is developed comprising rGOnR/MXene‐PDMS nanocomposite and a MXene film, demonstrating exceptional EMI shielding and ultra‐high electromagnetic wave absorption. The 3D interconnected network of the nanocomposite, with lower conductivity (10 −3 –10 −2 S/cm), facilitates a tuned impedance matching layer with effective dielectric permittivity, and high attenuation capability through conduction loss, polarization loss at heterogeneous interfaces, and multiple scattering and reflections. Additionally, the higher conductivity MXene layer exhibits superior SE, reflecting passed electromagnetic waves back to the nanocomposite for further attenuation due to a π/2 phase shift between incident and back‐surface reflected electromagnetic waves. The synergistic effect of the layered structures markedly enhances total SE to 54.1 dB over the K u ‐band at a 2.5 mm thickness. Furthermore, the study investigates the impact of hybridized layered structure on reducing the minimum required thickness to achieve a peak absorption (A) power of 0.88 at a 2.5 mm thickness.
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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".