The Role of Phase Migration of Carbon Nanotubes in Melt-Mixed PVDF/PE Polymer Blends for High Conductivity and EMI Shielding Applications
Bibliographic record
Abstract
In this work, the effects of blend ratio and mixing time on the migration of multi-walled carbon nanotubes (MWCNTs) within poly(vinylidene fluoride) (PVDF)/polyethylene (PE) blends are studied. A novel two-step mixing approach was used to pre-localize MWCNTs within the PE phase, and subsequently allow them to migrate into the thermodynamically favored PVDF phase. Light microscopy images confirm that MWCNTs migrate from PE to PVDF, and transmission electron microscopy (TEM) images show individual MWCNTs migrating fully into PVDF, while agglomerates remained trapped at the PVDF/PE interface. PVDF:PE 50:50 and 20:80 polymer blend nanocomposites with 2 vol% MWCNTs exhibit exceptional electromagnetic interference shielding effectiveness (EMI SE) at 10 min of mixing (13 and 16 dB, respectively-at a thickness of 0.45 mm), when compared to 30 s of mixing (11 and 12 dB, respectively), suggesting the formation of more interconnected MWCNT networks over time. TEM images show that these improved microstructures are concentrated on the PE side of the PVDF/PE interface. A modified version of the "Slim-Fast-Mechanism" is proposed to explain the migration behavior of MWCNTs within the PVDF/PE blend. In this theory, MWCNTs approaching perpendicular to the interface penetrate the PVDF/PE interface, while those approaching in parallel or as MWCNT agglomerates remain trapped. Trapped MWCNTs act as barriers to additional MWCNTs, regardless of geometry. This mechanism is verified via TEM and scanning electron microscopy and suggests the feasibility of localizing MWCNTs at the interface of PVDF/PE blends.
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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".