Effect of conductive particles on the mechanical, electrical, and thermal properties of maleated polyethylene
Bibliographic record
Abstract
Inclusion of conductive particles is a convenient way for the enhancement of electrical and thermal conductivities of polymers. However, improvement of the mechanical properties of such composites has remained a challenge. In this work, maleated polyethylene is proposed as a novel matrix for the production of conductive metal–thermoplastic composites with enhanced mechanical properties. The effects of two conductive particles (iron and aluminum) on the morphological, mechanical, electrical, and thermal properties of maleated polyethylene were investigated. Morphological observations revealed that the matrix had excellent adhesion with both metal particles. Increase in particle concentration was shown to improve the tensile strength and modulus of the matrix significantly with iron being slightly more effective. Through‐plane electrical conductivity of maleated polyethylene was also substantially improved after adding iron particles, while percolation was observed at particle contents of around 20–30% vol. In the case of aluminum, no percolation was observed for particle contents of up to 50% vol., which was linked to the orientation of the particles in the in‐plane direction due to the squeezing flow. Inclusion of particles led to substantial increase (over 700%) in the thermal conductivities of both composites. The addition of high concentrations of metal particles to matrix led to the creation of two groups of materials: (i) composites with high electrical and thermal conductivities and (ii) composites with low electrical and high thermal conductivities. Such characteristics of the composites are expected to provide a unique opportunity for applications where a thermally conductive/electrically insulating material is desired. Copyright © 2015 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 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".