Big returns from small fibers: A review of polymer/carbon nanotube composites
Bibliographic record
Abstract
Abstract This paper reviews recent studies conducted on carbon nanotube/polymer composites. Carbon nanotubes are promising new materials for blending with polymers with potential to obtain low‐weight nanocomposites of extraordinary mechanical, electrical, thermal and multifunctional properties. The size scale, aspect ratio and properties of nanotubes provide advantages in a variety of applications, including electrostatically dissipative materials; advanced materials with combined stiffness, strength and impact for aerospace or sporting goods; composite mirrors; automotive parts that require electrostatic painting and automotive components with enhanced mechanical properties. The various processing methods for producing these nanocomposites are discussed, in particular melt mixing, solution processing and in‐situ polymerization. Some key results are summarized, relating to the mechanical, electrical, thermal, optical and surface properties. Finally, the challenges for the future are discussed in terms of processing, characterization, nanotube availability, nanotube tailoring, and the mechanisms governing the behavior of these remarkable nanoscale composites. Polym. Compos. 25:630–645, 2004. © 2004 Society of Plastics Engineers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".