Properties of microinjection‐molded polypropylene/graphite composites
Bibliographic record
Abstract
In this study, polypropylene (PP) composites filled with two different types of graphite particles, that is, flake‐shaped synthetic graphite (SG) and low‐temperature expandable graphite (LTEG), were prepared by melt blending, followed by microinjection molding (μIM). The microparts had three consecutive zones with decreasing thickness along the flow direction (FD). Results showed that, in addition to the larger particle size, the in situ exfoliation of LTEG during melt processing is crucial to the overall enhancement of electrical conductivity when compared with their SG‐containing counterparts, as corroborated by morphology observations. Moreover, the preferential alignment of conductive particles favors the construction of conductive pathways along the FD. The melting and crystallization behavior for PP, PP/LTEG, and PP/SG materials, and samples from each section of the corresponding microparts were evaluated by differential scanning calorimetry. Results indicated that both the addition of graphite particles and the typical thermomechanical history of μIM (i.e., high shearing and cooling rates) experienced in different sections of the three‐step microparts influence the melting and crystallization behavior of the composites. POLYM. ENG. SCI., 59:1560–1569 2019. © 2019 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".