Morphological, electrical and electromagnetic interference shielding characterization of vapor grown carbon nanofiber/polystyrene nanocomposites
Bibliographic record
Abstract
Abstract The influence of melt mixing conditions on the level of dispersion and the aspect ratio of vapor grown carbon nanofibers (VGCNFs) in a polystyrene (PS) matrix was studied. Final electrical and electromagnetic shielding capabilities in the 0.05–1.5 GHz frequency range are reported and discussed in the light of the composites' microstructure. The morphological study was based on analyzing scanning electron microscopy and optical microscopy micrographs and measuring the VGCNF length as a function of shear mixing conditions. The influence of mixing conditions on the microstructure was also indirectly studied by analyzing the dynamic mechanical behavior of the composites via rheology. Degradation of the VGCNF aspect ratio was found to be a function of the mixing energy. VGCNFs lost one‐third of their aspect ratio under gentle (low shear stress and mixing energy) mixing conditions. After VGCNFs had lost 40% of their aspect ratio, they had more resistance to breakage with increase in mixing energy. The dispersion of the VGCNFs was remarkably enhanced with increase in mixing energy. The percentage of area taken up by big agglomerates in the micrographs decreased from 14.1% to 5.5% when the mixing energy was increased from 100 J mL−1 to 453 J mL−1. The electrical and electromagnetic shielding properties of the 7.5 vol% VGCNF/PS composites were not affected by changing the processing energy because the enhancement of VGCNF dispersion with increasing mixing energy was accompanied by a loss in nanofiber aspect ratio. © 2012 Society of Chemical Industry
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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".