MSE 2022
Highlight Lecture
29.09.2022
Proof-of-concept thermoelectric modules based on thermoplastic polymer-carbon nanotube composites
PP

Dr.-Ing. Petra Pötschke

Pötschke, P. (Speaker)¹; Krause, B.¹; Żabnieńska-Góra, A.²; Jouhara, H.²
¹Leibniz-Institut für Polymerforschung Dresden e.V.; ²Brunel University London
Vorschau
26 Min. Untertitel (CC)

Electrically conductive polymer composites (CPCs) based on insulating polymers filled with carbon nanotubes (CNTs) are being studied as potential thermoelectric (TE) materials to recover waste heat into electrical energy. Even though such composites still have significantly lower thermoelectric performance than traditionally used materials and can only be used in temperature ranges below 240 °C for long time, their advantages lie in their availability and cost efficiency, but also in ease of processing, flexibility, low density and intrinsically low thermal conductivity.

In our study, composites with industrially used polymers such as polyether ether ketone (PEEK) were fabricated by melt processing in gram scale and compression molded to thin sheets. The singlewalled CNTs (SWCNTs) used as fillers formed an electrically conductive network in this insulating thermoplastic matrix at concentrations starting at 0.5 wt.%. While SWCNTs alone result in p-type composites, the composites can be converted to n-type behavior by adding additives. To prove the concept, modules were built with 3 and 4 pairs of legs in flat geometry, which generated a thermoelectric voltage of about 10 mV at 40 K temperature difference. Even though these TE values are far below industrial requirements, the results show that such polymer composites can in principle be used for TE applications.

Simulations using COMSOL Multiphysics software were carried out in parallel with the practical tests to determine the theoretically possible performance of the modules and to improve the TE design.

This work is part of the InComEss (INnovative polymer-based COmposite systeMs for high-efficient Energy Scavenging and Storage) project and receives funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement Number 862597.


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