Low-Concentration Graphene Nanoplatelet/HDPE Nanocomposites with Enhanced Dispersion and Interfacial Bonding for Improved CO<sub>2</sub> Barrier and Mechanical Performance at Elevated Temperatures
Bibliographic record
Abstract
High-density polyethylene (HDPE) is well known for its excellent moisture and chemical resistance, combined with a balance of ductility and strength, making it ideal for high-temperature, high-pressure applications. To enhance HDPE-based products under these conditions, improving barrier properties against greenhouse gases and increasing creep strength are essential. This study developed a graphene nanoplatelet (GNP)/HDPE nanocomposite with only 0.5 wt % GNP using a scalable melt-compounding method. By optimizing processing conditions and grafting GNP onto HDPE chains via maleic anhydride functional groups, significant improvements were achieved at 60 °C, including a 44.6% reduction in supercritical CO 2 permeability, a 40.7% reduction in creep strain, and a 68.1% decrease in creep strain rate. Additionally, the onset degradation temperature increased by 25 °C, and the maximum weight loss rate improved by 54% compared to neat HDPE. Notably, these enhancements were achieved while maintaining elongation at break, with the GNP/HDPE nanocomposite exhibiting nearly 20% higher elongation at break than neat HDPE. Fourier transform infrared spectroscopy (FTIR) confirmed strong ester linkages between GNP and the polymer chain, resulting from a nucleophilic addition reaction that improved interfacial bonding and overall properties. This study offers a significant step toward more resilient and environmentally friendly engineering materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".