Improving the electrical conductivity of ethylene 1‐octene copolymer/cyclic olefin copolymer immiscible blends by interfacial localization of MWCNTs
Bibliographic record
Abstract
The localization of multiwall carbon nanotubes (MWCNTs) in the immiscible blends of ethylene–1‐octene copolymer (EOC) and cyclic olefin copolymer (COC) with the sea–island morphology and electrical conductivity of resulting nanocomposites were investigated. Depending on the feeding orders, as the MWCNTs were located in the COC droplet, the electrical conductivity was obtained as high as 5.71 × 10−7 S/cm, while the MWCNTs were located in EOC/COC interface, the electrical conductivity increased significantly up to 1.72 × 10−2 S/cm. The improved electrical conductivity in EOE/COC/MWCNTs nanocomposite is attributed to the interfacial localization of MWCNTs which is resulted from thermodynamic affinity of MWCNTs to COC, as well as an interconnected structure via deformed and swelled COC droplets. Thermodynamic affinity of MWCNTs to COC and established interconnected structure are confirmed by rheological characterization, microscopic observations, dynamic mechanical analysis, and electrical conductivity measurements. Therefore, as a result of selective localization of MWCNTs and well‐designed phase morphology, lower rheological and especially electrical percolation thresholds could be obtained in the ternary nanocomposites compared to the binary systems. POLYM. ENG. SCI., 59:447–456, 2019. © 2018 Society of Plastics Engineers
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".