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ABSTRACT While silver selenide (Ag2Se) is considered a promisingn‐type thermoelectric material, the low throughput of conventional solid‐state processing for selenide‐based systems highlights the need for scalable solution‐based alternatives. However, most solution‐processing approaches require thermal annealing to form high‐quality films, whereas Ag2Se exhibits limited thermal stability (<350°C), posing a nontrivial issue for processing. To address this, we systematically investigate the incorporation of a controlled dual dopant (Sb‐Se) into Ag2Se through a compositionally tuned nano‐ink. Flexible SbxAgSe0.5+yfilms were fabricated using the direct ink writing (DIW) approach, and the doped films demonstrated enhanced thermal stability. The effect of dopants on microstructure was further studied using neutron diffraction at the VULCAN instrument and elemental analysis using SEM‐EDS. Results demonstrated that elemental doping of Sb and Se leads to stable/enhanced Seebeck coefficients after thermal exposure, in contrast to undoped Ag2Se films with decreased Seebeck due to thermal degradation, where the thermal behavior and possible degradation pathways are evaluated at 200, 400, and 600°C. A wearable thermoelectric generator (TEG) demonstrated a peak power density of ∼60 µW cm−2at 30°C temperature difference. Overall, this study suggests the possibility of printing‐based Ag2Se thermoelectric devices with high‐temperature processability for waste‐heat harvesting and harsh electronic applications.
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Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices
Flexible thermoelectric generators (TEGs) have shown immense potential for serving as a power source for wearable electronics and the Internet of Things. A key challenge preventing large-scale application of TEGs lies in the lack of a high-throughput processing method, which can sinter thermoelectric (TE) materials rapidly while maintaining their high thermoelectric properties. Herein, we integrate high-throughput experimentation and Bayesian optimization (BO) to accelerate the discovery of the optimum sintering conditions of silver–selenide TE films using an ultrafast intense pulsed light (flash) sintering technique. Due to the nature of the high-dimensional optimization problem of flash sintering processes, a Gaussian process regression (GPR) machine learning model is established to rapidly recommend the optimum flash sintering variables based on Bayesian expected improvement. For the first time, an ultrahigh-power factor flexible TE film (a power factor of 2205 μW m −1 K −2 with a zT of 1.1 at 300 K) is demonstrated with a sintering time less than 1.0 second, which is several orders of magnitude shorter than that of conventional thermal sintering techniques. The films also show excellent flexibility with 92% retention of the power factor (PF) after 10 3 bending cycles with a 5 mm bending radius. In addition, a wearable thermoelectric generator based on the flash-sintered films generates a very competitive power density of 0.5 mW cm −2 at a temperature difference of 10 K. This work not only shows the tremendous potential of high-performance and flexible silver–selenide TEGs but also demonstrates a machine learning-assisted flash sintering strategy that could be used for ultrafast, high-throughput and scalable processing of functional materials for a broad range of energy and electronic applications.
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- Award ID(s):
- 1747685
- PAR ID:
- 10377690
- Date Published:
- Journal Name:
- Energy & Environmental Science
- ISSN:
- 1754-5692
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1039/D2EE01844F
