Strong Anisotropy and Ultralow Percolation Threshold in Multiscale Composites Modified by Carbon Nanotubes Coated Hollow Glass Fiber
Bibliographic record
Abstract
Inspired by biological materials, the use of combined fillers of different types and sizes has led to multiscale, hierarchical composites which are considered to be the multifunctional materials of the next generation. However, the effects of hierarchical architecture on the electrical properties and percolation behavior remain poorly understood. Here, a multiscale polymer‐based micro‐/nano‐composite with hollow glass fibers coated by carbon nanotubes (CNTs) has been produced based on a simple dip‐coating approach. Besides a significant increase in electrical performance, the composites exhibit a very strong anisotropy of electrical properties with the difference of 2–5 orders of magnitude in different directions. In the longitudinal direction of composites, an ultralow percolation threshold is found. These unique properties are shown to be related to the hierarchical morphology, which gives rise to the existence of two percolation levels with different thresholds: a local threshold in the nanoscale 2D CNT networks at the fiber‐polymer interfaces and a global threshold in 3D network formed by the fibers. This study helps to deeper understand the macroscopic electrical performance of the hierarchical composites, potentially opening up new ways for designing novel materials via flexible tailoring the orientation of fiber and the morphology of interfaces.
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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".