Influences of Carbon Nanotube Networking on the Conductive, Crystallization, and Thermal Expansion Behaviors of PA610-Based Nanocomposites
Bibliographic record
Abstract
The electrical, crystallization and thermal expansion behaviors of polyamide 610 (PA610)/multi-walled carbon nanotube (CNT) nanocomposites prepared by melt mixing were investigated. Electron microscopy (Scanning Electron Microscopy and Transmission Electron Microscopy) revealed that a good dispersion of CNT and CNT network was obtained in the PA610 matrix. Addition of CNT to PA610 matrix led to polymer nanocomposites exhibiting higher electrical conductivity and lower thermal expansion. The network of CNT in the PA610 matrix, which can be tuned by the loading of CNT and the melt isothermal treatment, was found to play an important role in reducing thermal expansion and achieving higher conductivity. Furthermore, it was shown that significant reduction in thermal expansion in PA610/CNT nanocomposites was due to both thermally insensitive CNT and formation of CNT network.
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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 | 0.001 |
| 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".