Enhanced Electrical and Electromagnetic Interference Shielding Properties of Polymer–Graphene Nanoplatelet Composites Fabricated via Supercritical-Fluid Treatment and Physical Foaming
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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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".