Hyperbolic graphene framework with optimum efficiency for conductive composites
Bibliographic record
Abstract
Abstract Constructing conductive filler networks with high efficiency is essential to fabricating functional polymer composites. Although two-dimensional (2D) sheets have prevailed in nanocomposites, their efficiency in enhancing conductive functions seems to reach the limit, as if merely addressing the dispersion homogeneity. Here, we exploit the unrecognized geometrical curvature of 2D sheets to break the efficiency limit of filler systems. The hyperbolic curvature meditates the incompatibility between 2D topology and 3D filler space and holds the efficient conductive path through face-to-face contact. The hyperbolic graphene framework exhibits the record efficiency in enhancing electrically and thermally conductive functions of nanocomposites. At volume loading of only 1.6%, the thermal and electrical conductivities reach 31.6 W/(mK) and 13,911 S/m, respectively. Nanocomposites with hyperbolic graphene framework exhibit great potentials in thermal management, sensing and electromagnetic shielding. Our work presents a geometrically optimal filler system to break the efficiency limit of multifunctional nanocomposites and broadens the structural design space of 2D sheets by curvature modulation to meet more applications.
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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) | 0.001 | 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".