The Effect of Coupling Agents and Graphene on the Mechanical Properties of Film-Based Post-Consumer Recycled Plastic
Bibliographic record
Abstract
This study aims to improve the mechanical properties of post-consumer recycled (PCR) plastic composed primarily of polypropylene (PP) and polyethylene (PE), which generally exhibit poor miscibility, by applying coupling agents and graphene. Here, we compare a commercially available coupling agent with a directly synthesized maleic anhydride (MA) coupling agent. When applied to a 5:5 blend of recycled PP and PE, an optimum tensile strength was achieved at a 3 wt% coupling agent concentration, with the MA coupling agent outperforming the commercial one. Characterization through Fourier transform infrared spectroscopy (FT-IR) and thermogravimetry analysis (TGA) revealed a PP:PE ratio of approximately 3:7 in the PCR plastics, with 4.86% heterogeneous materials present. Applying 3 wt% of the commercial and MA coupling agents to the PCR plastics resulted in a significant 53.9% increase in the tensile strength, reaching 11.25 MPa, and a remarkable 421.54% increase in the melt flow index (MFI), reaching 25.66 g/10 min. Furthermore, incorporating 5 wt% graphene led to a notable 64.84% increase in the tensile strength. In addition, the application of MA coupling agents and graphene improved the thermal stability of the PCR plastics. These findings show significant promise for addressing environmental concerns associated with plastic waste by facilitating the recycling of PCR plastics into new products. The utilization of coupling agents and graphene offers a viable approach to enhance the mechanical properties of PCR plastics, paving the way for sustainable and environmentally friendly solutions.
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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) | 0.001 | 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".