Mechanical, Electrical, and Glass Transition Behavior of Copper–PMMA Composites

Poblete, Víctor; Alvarez, M

doi:10.3390/cryst13030368

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…For the Cu and Cu@Si composites, similar values o Φcv,e were also reported in the literature [35]; however, the decrease in Φcv,e of the Cu@Ag composites was not expected. Comparable results for the establishment of an electrica percolation network in copper-based composites have also been reported in the literature The studies confirm that the establishment of an electrical percolation network depends on the nature of the polymer matrix, as well as the particle size, shape and processing technique [5,[47][48][49].…”

Section: Electrical Analysis (Effect Of Particle Interactions On Elec...supporting

confidence: 77%

“…Comparable results for the establishment of an electrical percolation network in copper-based composites have also been reported in the literature. The studies confirm that the establishment of an electrical percolation network depends on the nature of the polymer matrix, as well as the particle size, shape and processing technique [5,[47][48][49].…”

Section: Discussionsupporting

confidence: 58%

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Surface Modification of Copper-Based Flakes for Conductive Polymer Composites

Mihelčič,

Oseli,

Rojac

et al. 2024

Polymers

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The physical properties as well as thermal and electrical stability of copper particles can be improved by surface protection, which mainly depends on the coating material. Our study was, therefore, focused on the rheological, thermal, mechanical and electrical characterization of polymer composites by comparing uncoated (Cu), silver-coated (Cu@Ag) and silica-coated (Cu@Si) copper flakes in low-density polyethylene at various volume concentrations (up to 40%). Interactions among particles were investigated by rheological properties, as these indicate network formation (geometrical entanglement), which is important for mechanical reinforcement as well as establishing an electric pathway (electrical percolation). The results showed that geometrical and electrical percolation were the same for Cu and Cu@Si, ~15%, while, surprisingly, Cu@Ag exhibited much lower percolation, ~7.5%, indicating the fusion of the Ag coating material, which also decreased crystal growth (degree of crystallinity). Furthermore, the magnitude of the rheological and mechanical response remained the same for all investigated materials, indicating that the coating materials do not provide any load transfer capabilities. However, they profoundly affect electron transfer, in that, Cu@Ag exhibited superior conductivity (74.4 S/m) compared to Cu (1.7 × 10−4 S/m) and Cu@Si (1.5 × 10−10 S/m). The results obtained are important for the design of advanced polymer composites for various applications, particularly in electronics where enhanced electrical conductivity is desired.

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Section: Electrical Analysis (Effect Of Particle Interactions On Elec...supporting

confidence: 77%

Section: Discussionsupporting

confidence: 58%

Surface Modification of Copper-Based Flakes for Conductive Polymer Composites

Mihelčič,

Oseli,

Rojac

et al. 2024

Polymers

View full text Add to dashboard Cite

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The Properties of Conductive Polymer Composites: Polypropylene Reinforced Copper and Aluminum Powder

Memon,

Thipboonsri,

Pramoonmak

et al. 2023

2023 International Conference on Power, Energy and Innovations (ICPEI)

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Prediction of Tribological Behavior of Acrylonitrile Butadiene Styrene Polymer Matrix Composites Employing Copper Powders

Jatti,

Saiyathibrahim,

Murali Krishnan

et al. 2024

Materials and Manufacturing

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<div>This research examines the impact of different amounts of copper (Cu) powder on the wear characteristics of acrylonitrile butadiene styrene (ABS)–Cu composites. Various formulations of ABS–Cu composites have been produced using injection molding, with different amounts of surfactant. Wear properties were evaluated by conducting tribological testing in accordance with ASTM standards. The findings indicated a decrease in wear loss, particularly when using a mixture consisting of 23% ABS, 70% Cu, and 7% surfactant. Machine learning regression algorithms successfully forecasted wear behavior with R-squared values over 0.97. The models used in the analysis included linear, stepwise linear, tree, support vector machine (SVM), efficient linear, Gaussian progression, ensemble, and neural network regression models. This research emphasizes the significance of composite materials in fulfilling contemporary technical requirements. The acquired insights enable the development of materials with customized wear characteristics. These findings have important consequences for a range of industrial applications.</div>

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Mechanical, Electrical, and Glass Transition Behavior of Copper–PMMA Composites

Cited by 3 publications

References 46 publications

Surface Modification of Copper-Based Flakes for Conductive Polymer Composites

Surface Modification of Copper-Based Flakes for Conductive Polymer Composites

The Properties of Conductive Polymer Composites: Polypropylene Reinforced Copper and Aluminum Powder

Prediction of Tribological Behavior of Acrylonitrile Butadiene Styrene Polymer Matrix Composites Employing Copper Powders

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