Non-Isothermal Crystallization Behavior and Thermal Properties of Polyethylene Tuned by Polypropylene and Reinforced with Reduced Graphene Oxide
Bibliographic record
Abstract
This research work is the first to report thermal stability, heat deformation resistance, and crystallization behavior of a Polyethylene (PE)-based biphasic polyolefin system reinforced with Reduced Graphene Oxide (RGO), which was obtained through Graphene Oxide (GO) chemical reduction. Polypropylene (PP) represented the polymeric dispersed phase. A strategic PE/PP/RGO manufacturing procedure was employed to thermodynamically localize RGO at the PE/PP interface, as confirmed by Transmission Electron Microscopy (TEM), bringing a uniform micro phase dispersion into the macro phase. In addition, studies of PE non-isothermal crystallization kinetics indicated that the morphology tunable micro phase and the nanolayered RGO promoted a nucleation-controlled PE crystallization, which was supported by Polarized Light Optical Microscopy (PLOM). This, together with fine morphology, justified the remarkable enhancement registered for the ternary system's thermal stability and heat deformation resistance. Different filler loads were employed, with weight fractions of 2% and 4%. It was observed that the former, being better exfoliated and more homogeneously distributed at the PE/PP interface than the latter, led to a more improved PE crystallization, alongside a greater ternary system's thermal properties. Moreover, the thermal stability of PE/PP reinforced with 2% of RGO was even higher than that of virgin PP, while their heat deformation resistance values were found to be similar. Therefore, this unique outcome provides industries, such as the energy and automotive sectors, with the opportunity to substitute PP-rich products with those mostly comprised of a cheaper, more abundant, yet performant PE.
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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) | 0.000 | 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 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".