Evolution of the electrical resistivity at rest and during oscillatory shearing of <scp>co‐continuous</scp> morphology (<scp>PP</scp>/<scp>PMMA</scp>)/<scp>MWCNT</scp> systems
Bibliographic record
Abstract
Abstract In this work, a modified parallel‐disks configuration on a strain‐controlled ARES rheometer (TA Instrument) was used to study the evolution of the electrical resistivity at rest and during oscillatory shearing of a co‐continuous immiscible polymer blend morphology based on polypropylene and /polymethyl(methacrylate) (PP/PMMA) in which various amounts (0–3 wt%) of multiwall carbon nanotubes (MWCNT) were added. The co‐continuity of both PP and PMMA phases allowed the buildup of a conductive network due to the preferential localization of the conductive MWCNT at the interface between PP and PMMA. Under a stepwise increase of the oscillatory strain amplitude below a critical value (γc = 6.3%), a significant decrease in the electrical resistivity was observed for MWCNT concentrations above the percolation threshold (0.3 wt%) due to the conductive paths induced by both thermal (Brownian) motion and oscillatory shearing. However, for deformation amplitudes higher than γc, the resistivity increased due to the destruction of the MWCNT paths induced by the large deformation imposed on the PP/PMMA interface. These observations were also confirmed by the evolution of the storage modulus (G′) which remained constant for γc < 6.3% (linear viscoelastic regime), while the values decreased above γc due to the destruction of the system's morphology.
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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 | 0.001 |
| 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".