Effect of mixing conditions and polymer particle size on the properties of polypropylene/graphite nanoplatelets micromoldings
Bibliographic record
Abstract
Abstract In this study, properties of polypropylene/graphite nanoplatelets (PP/GNP) composites and corresponding micromoldings were systematically studied in terms of filler loading concentrations and mixing methods. PP of different forms, i.e., PP pellets and powders, were adopted to fabricate PP/GNP composites. Additionally, a comparative study of precoating GNP and PP powders using solvent-based solution blending and ultrasonication-assisted mixing was performed. Results showed that PP/GNP composites prepared using powder form PP resulted in at least one order of magnitude higher electrical conductivity than using pellet form PP and further reduced the percolation threshold from 12.5 to 10 wt%, which was related to the state of filler distribution within corresponding moldings. Morphology observations revealed that microparts prepared with powder-PP/GNP composites exhibited less preferential alignment of GNP particles along the flow direction when compared with those molded using pellet-PP/GNP counterparts, which was helpful in improving the overall electrical conductivity for PP/GNP micromoldings.
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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 | 0.001 |
| 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".