Effect of carbon nanoparticle type, content, and stress on piezoresistive polyethylene nanocomposites
Bibliographic record
Abstract
This study examines the piezoresistive behavior of polyethylene (PE) composites containing different types of carbon nanoparticle fillers. The fillers investigated are single‐wall carbon nanotube (SWCNT), multi‐wall carbon nanotube (MWCNT), and graphene nanoplatelets (GNP), which were dispersed in PE through melt blending in concentrations ranging between 0.5 and 10 wt%. The dispersion and nanocomposite morphology were investigated using scanning electron microscopy and X‐ray diffraction with strong evidence found for shear‐induced orientation of GNP nanoparticles during the compression molding process. The conductivity and permittivity of the composite materials was investigated using impedance spectroscopy and the lowest percolation threshold and highest electrical conductivity was observed for SWCNT composites, followed by MWCNT and GNP. The compressive piezoresistance of the nanocomposites was measured and the initial, elastic, and plastic deformation regions were all identifiable by the resistance measurements. The main finding of this study is that the piezoresistance of MWCNT nanocomposites is more sensitive to the effects of varying stress and composition than SWCNT nanocomposites. This indicates an evolving filler network in the case for MWCNT, while a static network for SWCNT, which is explained by the higher aspect ratio and surface area of the latter. POLYM. ENG. SCI., 55:1643–1651, 2015. © 2014 Society of Plastics Engineers
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".