Comprehensive analysis of radiative cooling enabled thermoelectric energy harvesting

The market for Internet-of-Things (IoT) with integrated wireless sensor networks (WSN) is expanding at a rate never seen before. The thriving of IoT also brings an unprecedented demand for sustainable micro-Watt-scale power supplies. Radiative cooling (RC) can provide a continuous temperature difference which can be converted by a thermoelectric generator (TEG) into electrical power. This novel combination of radiative cooling with TEG expands the category of sustainable energy sources for energy harvesting. However, the further application of RC-TEG requires a holistic investigation of its RC-TEG performance which is dependent on many different parameters. Using 3D finite element method simulation, this works provides a comprehensive analysis of the concept of RC-TEG by investigating the impact of radiative cooler properties, TEG parameters, and environmental conditions, to provide a full picture of the performance of RC-TEG devices. The capability of RC-TEG to provide continuous power supply is tested using real-time environmental data from both Singapore and London on two different days of the year, demonstrating continuous power supply sufficient for a wide range of physical devices

Segmented thermoelectric generator modelling and optimization using artificial neural networks by iterative training

Renewable energy technologies are central to emissions reduction and essential to achieve net-zero emission. Segmented thermoelectric generators (STEG) facilitate more efficient thermal energy recovery over a large temperature gradient. However, the additional design complexity has introduced challenges in the modelling and optimization of its performance. In this work, an artificial neural network (ANN) has been applied to build accurate and fast forward modelling of the STEG. More importantly, we adopt an iterative method in the ANN training process to improve accuracy without increasing the dataset size. This approach strengthens the pro- portion of the high-power performance in the STEG training dataset. Without increasing the size of the training dataset, the relative prediction error over high-power STEG designs decreases from 0.06 to 0.02, representing a threefold improvement. Coupling with a genetic algorithm, the trained artificial neural networks can perform design optimization within 10 s for each operating condition. It is over 5,000 times faster than the optimization performed by the conventional finite element method. Such an accurate and fast modeller also allows mapping of the STEG power against different parameters. The modelling approach demonstrated in this work indicates its future application in designing and optimizing complex energy harvesting technologies

Thermoelectric Properties of Bismuth Telluride Thin Films Electrodeposited from a Nonaqueous Solution

We report the thermoelectric properties of Bi2Te3 thin films electrodeposited from the weakly coordinating solvent dichloromethane (CH2Cl2). It was found that the oxidation of porous films is significant, causing the degradation of its thermoelectric properties. We show that the morphology of the film can be improved drastically by applying a short initial nucleation pulse, which generates a large number of nuclei, and then growing the nuclei by pulsed electrodeposition at a much lower overpotential. This significantly reduces the oxidation of the films as smooth films have a smaller surface-to-volume ratio and are less prone to oxidation. X-ray photoelectron spectroscopy (XPS) shows that those films with Te(O) termination show a complete absence of oxygen below the surface layer. A thin film transfer process was developed using polystyrene as a carrier polymer to transfer the films from the conductive TiN to an insulating layer for thermoelectrical characterization. Temperature-dependent Seebeck measurements revealed a room-temperature coefficient of −51.7 μV/K growing to nearly −100 μV/K at 520 °C. The corresponding power factor reaches a value of 88.2 μW/mK2 at that temperature