Unveiling the Significance of Graphene Nanoplatelet (GNP) Localization in Tuning the Performance of PP/HDPE Blends
Bibliographic record
Abstract
High-density polyethylene (HDPE) and polypropylene (PP) blends are widely used in industries requiring mechanically durable materials, yet the impact of processing parameters on blend performance remains underexplored. This study investigates the influence of blending sequence and screw speed on the properties of blends of HDPE and PP filled with 1.25 wt.% graphene nanoplatelets (GNPs). Changes in crystallization behaviour, tensile strength, and viscoelastic responses with blending sequence are studied. The addition of GNP increases the crystallization temperature (Tc) of PP in the PE/PP blend by 4 °C when GNP is pre-mixed with PE to form (PE+GNP)/PP blends. In contrast, when GNP is pre-mixed with PP to create (PP+GNP)/PE blends, the Tc of PP rises by approximately 11 °C, from 124 °C for the neat PE/PP blend to 135 °C. On the other hand, the Tc of PE remains unchanged regardless of the blending sequence. XRD patterns reveal the impact of blending regime on crystallinity, with GNP alignment affecting peak intensities confirming the more efficient interaction of GNPs with PP when premixed before blending with PE, (PP+GNP)/PE. Tensile moduli are less sensitive to the changes in processing, e.g., screw speed and blending sequence. In contrast, elongation at break and tensile toughness show distinct variations. The elongation at the break of the (PP+GNP)/PE blend decreases by 30% on increasing screw speed from 50 to 200 rpm. Moreover, the elongation at the break of (PE+GNP)/PP prepared at 100 rpm is ~40% higher than that of the (PP+GNP)/PE. (PE+GNP)/PP displays a ‘quasi-co-continuous’ morphology linked to its higher elastic modulus G′ compared to that of the (PP+GNP)/PE blend. This study highlights the importance and correlation between processing and blend properties, offering insights into fine-tuning polymer composite formulation for optimal performance.
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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".