Mixing rules for high density polyethylene‐polypropylene blends

Stewart, Katherine M. E.; Stonecipher, Ethan; Ning, Haibin; Pillay, Selvum Brian

doi:10.1002/cjce.25011

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…The authors of the articles in this issue join us from many countries and institutions across the globe (Figure 1 and Table 1) to thank Archie for his enduring legacy in polymerization reaction engineering, a field of academic and industrial interest that he established practically single‐handedly. [ 1–45 ]…”

Section: Figurementioning

confidence: 99%

Archie E. Hamielec memorial issue

et al. 2023

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Section: Figurementioning

confidence: 99%

Archie E. Hamielec memorial issue

et al. 2023

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Improved thermal conductivity of high‐density polyethylene by incorporating hybrid fillers of copper powders and silicon carbide whiskers

Yang,

Li,

Liang

et al. 2024

J of Applied Polymer Sci

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In this paper, highly electrically and thermally conductive copper powders (Cu) and electrically insulative yet thermally conductive silicon carbide whiskers (SiCw) were selected as functional fillers and high‐density polyethylene (HDPE) was used as the matrix to prepare thermal conductive composites. By controlling the amount of Cu at 30 vol% which is below percolation threshold, the volume resistivity of the composites is maintained above 1014 Ω cm. The results showed that SiCws entered the area which was not occupied by Cu particles and they played a bridging effect among Cu particles, thereby promoting the formation of intact thermal conductive pathways that contributed to improving the thermal conductivity of corresponding composites. The combination of SiCw:Cu = 3:1 at a filler content of 30 vol% resulted in a thermal conductivity as high as 1.921 W/mK, which is 380% higher than pure HDPE.

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Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of the Melt Flow Rate and Shear Viscosity

Seifert,

Leuchtenberger-Engel,

Hopmann

2024

Polymers

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The extensive use of polypropylene (PP) in various industries has heightened interest in developing efficient methods for recycling and optimising its mixtures. This study focuses on formulating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The investigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, attaining R2 values over 0.99. Symbolic regression further enhanced these models, offering significant improvements. To mitigate overfitting, empirical noise and variable swapping were introduced, increasing the models’ robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the development of recycling technologies and sustainable practices in the polymer industry by optimising processing and enhancing the use of recycled materials.

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Mixing rules for high density polyethylene‐polypropylene blends

Cited by 4 publications

References 52 publications

Archie E. Hamielec memorial issue

Archie E. Hamielec memorial issue

Improved thermal conductivity of high‐density polyethylene by incorporating hybrid fillers of copper powders and silicon carbide whiskers

Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of the Melt Flow Rate and Shear Viscosity

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