Mass‐produced graphene—HDPE nanocomposites: Thermal, rheological, electrical, and mechanical properties
Bibliographic record
Abstract
Economically viable high‐density polyethylene (HDPE)/graphene nanocomposites were produced using mass produced graphene powder and an industrial twin‐screw melt‐compounding machine. Rheological and electrical properties were investigated and scanning electron microscopy was carried out to investigate graphene dispersion and its network formation in the matrix. Mechanical properties of the nanocomposites were evaluated using tensile, flexural and impact tests. Differential scanning calorimetry analysis indicated that the crystalline structure of the polymer might be affected by high loadings of graphene. SEM evaluation revealed reasonable graphene dispersion in the matrix. In addition, the amount of graphene required to form a percolated network was similar for both rheological and electrical networks. The nanocomposites exhibited a significant increase in Young's and flexural moduli without a notable reduction in impact strength up to 14 wt% graphene loading. In these experiments, compounding graphene powder with HDPE produced a clear and distinct improvement in mechanical properties at an industrially suitable low cost. POLYM. ENG. SCI., 59:675–682, 2019. © 2018 Society of Plastics Engineers
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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".