Synergistic Layered Design of Aerogel Nanocomposite of Graphene Nanoribbon/MXene with Tunable Absorption Dominated Electromagnetic Interference Shielding
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it