Flexible Poly(ether-block-amide)/Carbon Nanotube Composites for Electromagnetic Interference Shielding
Bibliographic record
Abstract
Conductive nanofillers usually act as nucleating agents in the semicrystalline polymer matrix, and the crystals formed on the filler surface can impede electrical percolation development. In this work, flexible poly(ether-block-amide)/carbon nanotube (PEBA/CNT) nanocomposites were fabricated for electromagnetic interference (EMI) shielding applications. It was demonstrated that CNTs can nucleate the crystallization of the polyamide (PA) block of PEBA and induce the transformation of the crystals from the γ-form to the α-form. Reducing the content of the PA block in PEBA from 50 to 20 wt % decreased the crystallinity of the nanocomposite and thus resulted in a higher electrical conductivity and an increment in EMI shielding performances. However, when ionically conductive polyethylene oxide was used as the polyether block, the hindrance effect of PA crystallization on electrical percolation was effectively mitigated, allowing for both high conductivity and enhanced amide dipole moment with a high PA content (e.g., 50 wt %), which is favorable for electromagnetic wave absorption. As a result, a high EMI shielding effectiveness with increased absorption can be achieved in PEBA/CNT nanocomposites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".