Effects of microsized and nanosized carbon fillers on the thermal and electrical properties of polyphenylene sulfide based composites
Bibliographic record
Abstract
Multifunctional polymer matrix composites (PMCs) exhibiting increased thermal and electrical conductivity can be achieved by adding conductive carbon‐based fillers in the polymer matrix. Emerging markets for thermally and electrically conductive PMCs include heat management devices in electronics, light emitting diodes, and/or photovoltaics cells as well as bipolar plates in fuel cells. In this study, the effects of microsized and nanosized carbon fillers [e.g., pitch‐based carbon fibers (CF), multiwalled carbon nanotubes (MWNT), and graphene nanoplatelets (GNPs)] on the multifunctional properties of polyphenylene sulfide (PPS) matrix composites were investigated. Among the three carbon‐based fillers, experimental results revealed that GNPs were the most effective fillers to promote the PMC's effective thermal conductivity ( k eff ). The highest k eff of 1.94 W/m K was attained by adding 30.0 wt% (i.e., 22.4 vol%) of GNPs in the PPS matrix. The nano‐sized MWNTs and GNPs had lower percolation threshold than the micron sized CFs to suppress the composite's electrical impedance. However, the coefficient of thermal expansion and the compressive elastic modulus of the composites were only slightly improved with the filler addition. The different levels of improvement in the composite's multifunctional properties can be related to the filler geometry, filler size, and the composite's phase morphology. POLYM. ENG. SCI., 53:2398–2406, 2013. © 2013 Society of Plastics Engineers
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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".