Improving the material extrusion processing of thermoplastic olefin/graphene nanoplatelet composites through control of the morphology
Bibliographic record
Abstract
The aim of this research is to develop thermoplastic olefin (TPO) composites containing polypropylene (PP), an elastomeric ethylene-octene copolymer (EOC) and graphene nanoplatelets (GNPs), suitable for material extrusion (MEX). A PP functionalized with amino-pyridine (PP-g-Py) was used as a compatibilizer. The composite blends had droplet-matrix morphology at compositions as high as 40 wt% EOC. Imaging by Transmission Electron Microscopy showed that the GNPs resided at the interface between the blend components. This microstructure promoted higher thermal conductivity of the TPO/GNP composite blends, as compared to the PP/GNP composite (1.54 W/m K, vs 1.3 W/m K respectively). PP/GNP composites processed by MEX exhibited inadequate interfacial fusion between the deposited strands, which resulted in severe delamination during tensile and flexural testing, and consequently poor mechanical properties. In the TPO/GNP composites containing 40 wt% EOC, the slower crystallization of the elongated EOC domains promoted interfacial adhesion between the strands, resulting in better part consolidation, more consistent mechanical properties and improved ductility compared to the PP/GNP composites.
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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".