Synergistic Manipulation of Zero-Dimension and One-Dimension Hybrid Nanofillers in Multi-Layer Two-Dimension Thin Films to Construct Light Weight Electromagnetic Interference Material
Bibliographic record
Abstract
Nanocomposite foam with a large expansion ratio and thin cell walls is promising for electromagnetic interference (EMI) shielding materials, due to the low electromagnetic (EM) reflection and high EM absorption. To overcome the dimensional limitation from two-dimension (2D) thin walls on the construction of conductive network, a strategy combining hybrid conductive nanofillers in semi-crystalline matrix together with supercritical CO2 (scCO2) foaming was applied: (1) one-dimension (1D) CNTs with moderate aspect ratio was used to minimize the dimensional confinement from 2D thin walls while constructing the main EM absorbing network; (2) zero-dimension (0D) carbon black (CB) with no dimensional confinement was used to connect the separated CNTs in thin walls and to expand the EM absorbing network; (3) scCO2 foaming was applied to obtain a cellular structure with multi-layer thin walls and a large amount of air cells to reduce the reflected EM; (4) semi-crystalline polymer was selected so that the rheological behavior could be adjusted by optimizing crystallization and filler content to regulate the cellular structure. Consequently, an advanced material featured as lightweight, high EM absorption and low EM reflection was obtained at 0.48 vol.% hybrid nanofillers and a density of 0.067 g/cm3, whose specific EMI shielding performance was 183 dB cm3/g.
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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".