Morphology, mechanical properties and electromagnetic shielding effectiveness of poly(styrene‐ <i>b</i> ‐ethylene‐ <i>ran</i> ‐butylene‐ <i>b</i> ‐styrene)/carbon nanotube nanocomposites: effects of maleic anhydride, carbon nanotube loading and processing method
Bibliographic record
Abstract
Abstract Nanocomposites based on poly(styrene‐ b ‐ethylene‐ ran ‐butylene‐ b ‐styrene) (SEBS) and carbon nanotubes (CNTs) (SEBS/CNT) as well as SEBS grafted with maleic anhydride (SEBS‐MA)/CNT were successfully prepared for electromagnetic shielding applications. Both SEBS/CNT and SEBS‐MA/CNT nanocomposites were prepared by melt compounding and were post‐processed using two different techniques: tape extrusion and compression moulding. The different nanocomposites were characterized by Raman spectroscopy and rheological analysis. Their mechanical properties, electrical properties (10 -2 –10 5 Hz) and electromagnetic shielding effectiveness (8.2–12.4 GHz) were also evaluated. The results showed that the CNT loading amount, the presence of MA in the matrix and the shaping technique used strongly influence the final morphologies and properties of the nanocomposites. Whilst the nanocomposite containing 8 wt% CNTs prepared by compression moulding presented the highest electromagnetic shielding effectiveness (with a value of 56.73 dB, which corresponds to an attenuation of 99.9996% of the incident radiation), the nanocomposite containing 5 wt% CNTs prepared by tape extrusion presented the best balance between electromagnetic and mechanical properties and was a good candidate to be used as an efficient flexible electromagnetic interference shielding material. © 2018 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".