Solar thermoelectric generators (STEGs) often require long thermoelectric (TE) legs and efficient cooling at the cold side to increase the temperature difference across TE legs and, thus, the power output. We investigate the effects of direct side-wall air cooling of TE legs on the power output of STEGs fabricated with high aspect-ratio as well as V-shaped p-type and n-type TE couples without additional heat sinks. Wire-type metallic TE materials are welded together to create V-shape TE leg arrays without additional electrodes and attached to a ceramic plate with a solar absorber on top to complete the STEG. The power generation performance of the STEG is investigated with varying wind speed under concentrated solar irradiation. Finite element simulation is performed to further analyze the heat transfer and thermoelectric performance. We find that although sidewall air cooling helps to keep the cold-side temperature cooler in both natural and forced convection regimes, it can also lower the hot-side temperature to reduce the net temperature difference and, thus, the power output and efficiency. Partial thermal insulation of TE couples can further enhance the power output under forced air convection by keeping the hot side temperature higher. The developed STEG achieves a maximum power density of 230 μW/cm2 and a system efficiency of 0.023% under 10 suns with natural convection. The low efficiency was mainly due to the low ZT of the metallic TE materials used and the unoptimized leg length. Our simulation shows that the system efficiency can be improved to 2.8% with state-of-the-art Bi2Te3 alloys at an optimal leg length.
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Performance Optimization of Thermoelectric Devices and its Dependence on Materials Properties
In this perspective, we discuss the optimized performance of thermoelectric cooling devices and how it is affected by materials properties. The discussion is based on simulations using a numerical method with one dimensional transport equations and the concept of relative current density. The coefficient of performance (COP), representing the efficiency of a device, is of key importance such that when designing a new type of device, it is the parameter to be maximized, whereas others such as the cooling power, can be set by adjusting the dimensions of the design. The COP of a single stage device under a given temperature difference, is only determined by the materials’ figure of merit zT (or z) and the Seebeck coefficient . While it is the higher the better for the former, the influence of is complicated. While higher zTs are always preferred, materials with comparably high zT and very different could be valuable in constructing graded legs that outperform uniform ones. Lastly, proper pairing of legs is important to ensure the materials properties are used to their full potential.
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- Award ID(s):
- 2213441
- PAR ID:
- 10422068
- Date Published:
- Journal Name:
- Materials Lab
- Volume:
- 1
- ISSN:
- 2653-4878
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Printing is a versatile method to transform semiconducting nanoparticle inks into functional and flexible devices. In particular, thermoelectric nanoparticles are attractive building blocks to fabricate flexible devices for energy harvesting and cooling applications. However, the performance of printed devices are plagued by poor interfacial connections between nanoparticles and resulting low carrier mobility. While many rigid bulk materials have shown a thermoelectric figure of merit
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Abstract Thermoelectric (TE) generators enable the direct and reversible conversion between heat and electricity, providing applications in both refrigeration and power generation. In the last decade, several TE materials with relatively high figures of merit (
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Solid-state thermionic devices based on van der Waals structures were proposed for nanoscale thermal to electrical energy conversion and integrated electronic cooling applications. We study thermionic cooling across gold-graphene-WSe 2 -graphene-gold structures computationally and experimentally. Graphene and WSe 2 layers were stacked, followed by deposition of gold contacts. The I - V curve of the structure suggests near-ohmic contact. A hybrid technique that combines thermoreflectance and cooling curve measurements is used to extract the device ZT . The measured Seebeck coefficient, thermal and electrical conductance, and ZT values at room temperatures are in agreement with the theoretical predictions using first-principles calculations combined with real-space Green’s function formalism. This work lays the foundation for development of efficient thermionic devices.more » « less
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Thermoelectric materials harvest waste heat and convert it into reusable electricity. Thermoelectrics are also widely used in inverse ways such as refrigerators and cooling electronics. However, most popular and known thermoelectric materials to date were proposed and found by intuition, mostly through experiments. Unfortunately, it is extremely time and resource consuming to synthesize and measure the thermoelectric properties through trial-and-error experiments. Here, we develop a convolutional neural network (CNN) classification model that utilizes the fused orbital field matrix and composition descriptors to screen a large pool of materials to discover new thermoelectric candidates with power factor higher than 10 μW/cm K2. The model used our own data generated by high-throughput density functional theory calculations coupled with ab initio scattering and transport package to obtain electronic transport properties without assuming constant relaxation time of electrons, which ensures more reliable electronic transport properties calculations than previous studies. The classification model was also compared to some traditional machine learning algorithms such as gradient boosting and random forest. We deployed the classification model on 3465 cubic dynamically stable structures with non-zero bandgap screened from Open Quantum Materials Database. We identified many high-performance thermoelectric materials with ZT > 1 or close to 1 across a wide temperature range from 300 to 700 K and for both n- and p-type doping with different doping concentrations. Moreover, our feature importance and maximal information coefficient analysis demonstrates two previously unreported material descriptors, namely, mean melting temperature and low average deviation of electronegativity, that are strongly correlated with power factor and thus provide a new route for quickly screening potential thermoelectrics with high success rate. Our deep CNN model with fused orbital field matrix and composition descriptors is very promising for screening high power factor thermoelectrics from large-scale hypothetical structures.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.54227/mlab.20220053
