Advancements in distributed transport property thermoelectrics: Enhancing performance through material selection and innovative manufacturing

Abstract

Distributed Transport Properties (DTP) play a pivotal role in optimizing the performance of thermoelectric (TE) materials, by optimizing the spatial variation of the magnitude of Seebeck coefficient, electrical resistivity, and thermal conductivity within the TE element. This approach partially offsets the adverse effects of internal Joule heating, creating an optimal temperature profile within the TE element. In this paper, we describe promising results from prototype DTP TE couples we have produced and tested, which exhibit an 8–10K increase in maximum temperature differential, a twofold increase in Coefficient of Performance (COP), and a more than threefold increase in heat pumping capacity at a 70K temperature differential. We also describe the results of our validated model, which predicts the possibility of up to a tenfold improvement in COP and heat pumping capacity at elevated temperature differences.

This paper introduces the utilization of machine learning to expedite the search for ideal and feasible materials to improve the performance of TE solid state thermal management.

We also introduce the utilization of additive manufacturing to fabricate DTP elements with a range of compositions, allowing for the creation of TE material systems approaching the theoretical upper limit for COP and heat pumping density. The application of these novel methodologies outlines a pathway toward unlocking the full potential of DTP in thermoelectric materials, aiming to achieve for the first time a maximum temperature difference of 100K in a single stage TE device.