Enhancing the recyclability and thermal properties of a novel low-carbon engineering polymer, aliphatic polyketone, through graphene and glass fiber incorporation
Bibliographic record
Abstract
Aliphatic polyketone (PK) is an innovative thermoplastic with mechanical and barrier properties comparable to or superior to polyamide, a widely used engineering polymer, along with significantly enhanced moisture resistance. Notably, PK features also a lower environmental impact compared to other engineering polymers and a carbon footprint up to 60% lower than Polyamide 66 (PA66). However, its limited thermal stability restricts its application at high temperatures and makes its processability and recyclability challenging. The incorporation of graphene, known for its exceptional thermal, electrical, and mechanical properties, presents a promising approach to overcome these limitations while improving the functional properties and performance of PK. Additionally, graphene's lubricating effect has the potential to facilitate PK processing, improving its manufacturability.The objective of this study is to explore the potential of graphene in enhancing the thermal stability, recyclability of PK and PK/glass fiber composites while simultaneously optimizing the mechanical performance of PK/glass fiber composites and other functional properties.In this study, two graphene grades with different specific surface areas and aspect ratios were dispersed in PK and PK/glass fiber composites using a melt compounding process. The impact of graphene nanoplatelets on the thermal stability and crystallization behaviour of the resulting nanocomposites was analysed through thermogravimetric analysis and differential scanning calorimetry. Their morphology was examined using scanning electron microscopy, while polymer/graphene interfacial interactions were assessed via dielectric spectroscopy and dynamic mechanical analysis. Furthermore, mechanical testing was conducted to evaluate the reinforcement effect of graphene nanoplatelets on PK and PK/glass fiber composites. By fine-tuning graphene dispersion and spatial distribution as well as the nanocomposite formulations, we aim to develop materials with superior durability, reduced environmental impact, and enhanced performance comparable to or higher than polyamide composites.Key words:Aliphatic Polyketone (PK), Graphene Nanoplatelets, Thermal Stability, Recyclability, Polymer Nanocomposites, Sustainable Engineering Polymers, Low-Carbon Materials.
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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".