Compatibilized polypropylene nanocomposites containing expanded graphite and graphene nanoplatelets
Bibliographic record
Abstract
Abstract We present a non‐covalent compatibilization approach to prepare polypropylene (PP) composites containing expanded graphite (EG) and graphene nanoplatelets (GNPs) by melt compounding. This method involves PP matrix functionalization with pyridine (Py) moieties, which are capable of engaging in π‐π interactions with the surface of the EG and GNPs. The addition of 10 wt% of PP grafted with amino‐pyridine (PP‐g‐Py) to neat PP facilitated the break‐up of EG particles, by intercalating between their layers and facilitating their separation into smaller tactoids. GNPs were prepared starting from EG through a thermomechanical exfoliation method. Addition of GNPs to PP resulted in well‐dispersed platelets having aspect ratios as high as 40, whereas in the presence of the PP‐g‐Py compatibilizer the matrix contained sub‐micron scale platelets. The electrical percolation thresholds were in the vicinity of 6 and 10 vol% in the compatibilized PP‐EG and PP‐GNP composites, respectively, and the maximum value of the electrical conductivity achieved was 10−1 S/m for the compatibilized GNP composites. Addition of GNPs resulted in increases in the flexural moduli by as much as 95% compared to the unfilled PP, whereas the impact strength remained unaffected up to 10 wt% GNP content.
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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".