Effect of Nano-Particle Filler Type on the Piezoresistance of Polyethylene Composites
Bibliographic record
Abstract
This study examines the piezoresistive behavior of polyethylene (PE) composites containing different types of nano-particle fillers. The fillers investigated are single-wall carbon nanotube (SWNT), multi-wall carbon nanotube (MWNT) and graphene nanoplatelets (GNP), which were dispersed in PE through melt blending in concentrations ranging between 0.5–10 wt%. The lowest percolation threshold and highest electrical conductivity was found for SWNT composites, followed by MWNT and GNP. The piezoresistance of the nano-particle-PE composites was investigated and a negative piezoresistance behavior was observed signifying a reducing mean inter-particulate distance in the composites. The highest sensitivity was exhibited by MWNT and SWNT composites at compositions closer to the percolation threshold. The time dependence of piezoresistivity was also investigated through stress relaxation and cyclic loading experiments.
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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 | 0.001 |
| 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".