Electrified single‐walled carbon nanotube/epoxy nanocomposite via vacuum shock technique: Effect of alignment on electrical conductivity and electromagnetic interference shielding
Bibliographic record
Abstract
Electrified and non‐electrified epoxy‐based nanocomposites holding highly and randomly aligned single‐walled carbon nanotube (SWCNT), respectively, were made by vacuum shock technique, where a DC electric field was used to align SWCNT. The alignment of SWCNTs in the electrified nanocomposites was verified via optical microscopy, SEM analysis, and Raman spectroscopy. Electrical characterization revealed that alignment of SWCNTs led to a significant improvement in electrical conductivity and electromagnetic interference shielding of the fabricated nanocomposites. For instance, the electrical conductivity of the electrified nanocomposites at 0.25 wt% and 0.60 wt% was 2.5 × 10−8 and 5.1 × 10−4 S·m−1, while the conductivity of non‐electrified nanocomposites was 1.1 × 10−11 and 5.6 × 10−5 S·m−1, respectively. With 3.0 mm thickness and 0.60 wt% SWCNT loading, the electrified and non‐electrified nanocomposites showed shielding effectiveness of 12.8 dB and 9.1 dB, respectively. These results revealed that electrification of SWCNT in epoxy‐based nanocomposites improved the level of conductive network formation, thereby enhancing electrical properties of the nanocomposites. POLYM. COMPOS., 39:E1139–E1148, 2018. © 2017 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 | 0.000 | 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".