Rheological, thermal, and electrical characterization polyamide/polypropylene blend composites containing hybrid filler: Boron nitride and reduced graphene oxide
Bibliographic record
Abstract
Abstract In this study, the microstructural development and its effect on the thermal conductivity of polyamide6 (PA6)/polypropylene (PP) blends containing boron nitride (BN) and reduced graphene oxide (rGo) as hybrid fillers were investigated. Blend samples were prepared using the masterbatch method to localize BN and rGo in the matrix phase (PA6). Dynamic rheological results were consistent with selective localization of the fillers in PA6 as evidenced by nonterminal behavior (3D network) PP/PA‐BN at low frequencies. Compared with the case where the matrix phase (PA6) was only filled with BN particles, thermal conductivity measurements showed that replacing 10% and 15% BN particles with rGo nanoparticles yielded higher thermal conductivity. The hybrid fillers had a synergetic effect on the heat conductive network, forming a more efficient percolating network of BN and rGo in the matrix phase (PA6). A comparison between the BN‐filled PA6 blend and the BN‐rGo‐filled PA6 blend revealed higher thermal conductivity in the PP/PA6‐BN‐rGo sample with co‐continuous morphology than in the PP/PA6‐BN sample with matrix‐disperse morphology.
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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".