Enhanced Electrical and Electromagnetic Interference Shielding Properties of Polymer–Graphene Nanoplatelet Composites Fabricated via Supercritical-Fluid Treatment and Physical Foaming
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Bibliographic record
Abstract
Lightweight high-density polyethylene (HDPE)-graphene nanoplatelet (GnP) composite foams were fabricated via a supercritical-fluid (SCF) treatment and physical foaming in an injection-molding process. We demonstrated that the introduction of a microcellular structure can substantially increase the electrical conductivity and can decrease the percolation threshold of the polymer-GnP composites. The nanocomposite foams had a significantly higher electrical conductivity, a higher dielectric constant, a higher electromagnetic interference (EMI) shielding effectiveness (SE), and a lower percolation threshold compared to their regular injection-molded counterparts. The SCF treatment and foaming exfoliated the GnPs in situ during the fabrication process. This process also changed the GnP's flow-induced arrangement by reducing the melt viscosity and cellular growth. Moreover, the generation of a cellular structure rearranged the GnPs to be mainly perpendicular to the radial direction of the bubble growth. This enhanced the GnP's interconnectivity and produced a unique GnP arrangement around the cells. Therefore, the through-plane conductivity increased up to a maximum of 9 orders of magnitude and the percolation threshold decreased by up to 62%. The lightweight injection-molded nanocomposite foams of 9.8 vol % GnP exhibited a real permittivity of ε' = 106.4, which was superior to that of their regular injection-molded (ε' = 6.2). A maximum K-band EMI SE of 31.6 dB was achieved in HDPE-19 vol % GnP composite foams, which was 45% higher than that of the solid counterpart. In addition, the physical foaming reduced the density of the HDPE-GnP foams by up to 26%. Therefore, the fabricated polymer-GnP nanocomposite foams in this study pointed toward the further development of lightweight and conductive polymer-GnP composites with tailored properties.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it