Highly electro-conductive PEDOT based thermoplastic composites: effect of filler form factor on electrical percolation threshold
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Harris Mafueni Kuedituka
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Michel Bouquey
Abstract
In previous studies, the synthesis of conductive PEDOT particles was carried out to incorporate them into thermoplastic matrices without relying on commercial PEDOT:PSS solutions or carbon- or metal-based additives. While promising conductivities were achieved, a high filler content was required to reach the percolation threshold. To reduce this filler content, the particle form factor appears to play a crucial role. To investigate this effect, rod-like particles were synthesized via supported polymerization of EDOT. Subsequently, thermoplastic composites were produced through melt-extrusion, blending PEO as the thermoplastic matrix with PEDOT-based fillers, resulting in a conductive thermoplastic material strip. The present study focuses on the synthesis of silica nanoparticles using the Stöber method, followed by the supported polymerization of EDOT to obtain electrically conductive silica@PEDOT nanoparticles. A comparison of percolation curves and percolation thresholds for three different fillers, exhibiting low to high form factors, was performed. Additionally, relationships between processing conditions, morphology, and material properties were analysed using SEM, TEM, TGA, and four-probe resistivity measurements.
Acknowledgments
All authors acknowledge the MINAMEC platform (Antoine Egele) for X-ray tomography and data interpretation. Authors especially acknowledge Damien Favier for the realisation of the four probes conductivity measurement device. Authors acknowledge the PLAMICS microscopy facility of ICS. All authors acknowledge the CARMAC platform (Mélanie Legros) for the training and the use of the DLS equipment.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: This project named ELABELEC was supported by the Agence Nationale de la Recherche under grant number ANR-23-CE51-0044.
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Data availability: Not applicable.
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Articles in the same Issue
- Frontmatter
- Editorial
- PPS2024 Ferrol: advances and perspectives in polymer processing
- Research Articles
- Applying network theory to the modeling of multilayer flows in slot dies: a use case for symbolic regression-based co-extrusion prediction models
- Multiscale polyethylene fiber – bacterial nanocellulose composites through combined laser fusion and bacterial in situ synthesis
- Novel approach to produce reinforced plastic weld seams using an additive friction stir welding process
- Local thermal activation for a combined thermoforming and 3D-printing process
- A new recycling strategy for airbag waste
- Highly electro-conductive PEDOT based thermoplastic composites: effect of filler form factor on electrical percolation threshold
- Cavity balance improvement for injection molded parts via automated flow leader generation
- Application of artificial intelligence techniques to select the objectives in the multi-objective optimization of injection molding
- Modeling melt conveying and power consumption of conveying elements in co-rotating twin-screw extruders
Articles in the same Issue
- Frontmatter
- Editorial
- PPS2024 Ferrol: advances and perspectives in polymer processing
- Research Articles
- Applying network theory to the modeling of multilayer flows in slot dies: a use case for symbolic regression-based co-extrusion prediction models
- Multiscale polyethylene fiber – bacterial nanocellulose composites through combined laser fusion and bacterial in situ synthesis
- Novel approach to produce reinforced plastic weld seams using an additive friction stir welding process
- Local thermal activation for a combined thermoforming and 3D-printing process
- A new recycling strategy for airbag waste
- Highly electro-conductive PEDOT based thermoplastic composites: effect of filler form factor on electrical percolation threshold
- Cavity balance improvement for injection molded parts via automated flow leader generation
- Application of artificial intelligence techniques to select the objectives in the multi-objective optimization of injection molding
- Modeling melt conveying and power consumption of conveying elements in co-rotating twin-screw extruders
