Ultralight Microcellular Polymer–Graphene Nanoplatelet Foams with Enhanced Dielectric Performance
Bibliographic record
Abstract
Dielectric polymer nanocomposites with high dielectric constant (ε′) and low dielectric loss (tan δ) are extremely desirable in the electronics industry. Percolative polymer–graphene nanoplatelet (GnP) composites have shown great promise as dielectric materials for high-performance capacitors. Herein, an industrially-viable technique for manufacturing a new class of ultralight polymer composite foams using commercial GnPs with excellent dielectric performance is presented. Using this method, the high-density polyethylene (HDPE)–GnPs composites with a microcellular structure were fabricated by melt-mixing. This was followed by supercritical fluid (SCF) treatment and physical foaming in an extrusion process, which added an extra layer of design flexibility. The SCF treatment effectively in situ exfoliated the GnPs in the polymer matrix. Moreover, the generation of a microcellular structure produced numerous parallel-plate nanocapacitors consisting of GnP pairs as electrodes with insulating polymer as nanodielectrics. This significantly increased the real permittivity and decreased the dielectric loss. The ultralight extruded HDPE-1.08 vol % GnP composite foams, with a 0.15 g·cm –3 density, had an excellent combination of dielectric properties (ε′ = 77.5, tan δ = 0.003 at 1 × 10 5 Hz), which were superior to their compression-molded counterparts (ε′ = 19.9, tan δ = 0.15 and density of = 1.2 g·cm –3 ) and to those reported in the literature. This dramatic improvement resulted from in situ GnP’s exfoliation and dispersion, as well as a unique GnP parallel-plate arrangement around the cells. Thus, this facile method provides a scalable method to produce ultralight dielectric polymer nanocomposites, with a microscopically tailored microstructure for use in electronic devices.
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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".