Highly Ordered 3D Graphene-Based Polymer Composite Materials Fabricated by “Particle-Constructing” Method and Their Outstanding Conductivity
Bibliographic record
Abstract
The fabrication of graphene-based polymer composite materials is of interest and significance from an academic and an application viewpoint. The widely used method to obtain such composites was liquid-phase blend of graphene nanosheets (GNSs) and polymer solutions followed by casting or heat pressing. Until now, the challenge of dispersing the GNSs uniformly in the polymer matrix to form controllable and regular structure still remains. Here, we developed a unique “particle-constructing” method for fabricating highly ordered 3D graphene-based polymer composite materials, throughout which the GNSs formed intact, uniform and well-defined network structure. The strategy contains two steps: wrapping polymer microspheres with GNSs and mold-compressing them at room temperature, followed by an appropriate heat treatment. The composite materials exhibited outstanding electrical properties involving extremely low percolation threshold and much higher conductivity. The method can be easily extended to fabricate highly ordered GNS aerogels and more GNS-based composite materials. The results represent an important step toward developing GNS-based composite materials with high performance.
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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".