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Fe-Al-Si Thermoelectric (FAST) Materials and Modules: Diffusion Couple and Machine-Learning-Assisted Materials
Yoshiki Takagiwa1, Zhufeng Hou2, Koji Tsuda3
1National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0047, Japan.
ACS Applied Materials & Interfaces
|May 21, 2021
Summary
Researchers developed low-cost Iron-Aluminum-Silicon (Fe-Al-Si) thermoelectric materials for Internet-of-Things (IoT) devices. Machine learning identified compositions, like Cobalt-doped Fe-Al-Si, to significantly improve power generation efficiency.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Energy Science
Background:
- Autonomous power supplies for Internet-of-Things (IoT) devices are costly to introduce and maintain.
- Conventional thermoelectric materials like Bismuth-Telluride (Bi-Te) have limitations in cost and performance.
- Iron-Aluminum-Silicon (Fe-Al-Si) based thermoelectric (FAST) materials offer a low-cost alternative with good mechanical properties and chemical stability.
Purpose of the Study:
- To develop cost-effective Fe-Al-Si-based thermoelectric (FAST) materials and power generation modules for IoT applications.
- To overcome challenges in enhancing the power factor (PF) and reducing thermal conductivity of FAST materials.
- To explore the potential of machine learning (ML) and computational science in accelerating materials discovery and optimization.
Main Methods:
- Combined computational science, experimental validation, mapping measurements, and machine learning (ML) for materials development.
- Utilized bulk combinatorial methods, diffusion couple, and mapping measurements to accelerate the search for enhanced PF.
- Employed ML prediction to identify optimal off-stoichiometric compositions and dopant concentrations.
Main Results:
- FAST materials demonstrate superior mechanical properties and chemical stability compared to Bi-Te-based materials.
- Identified Cobalt (Co) substitution for Iron (Fe) atoms as an effective strategy to enhance the power factor (PF) in FAST materials.
- Demonstrated the efficacy of ML in predicting novel compositions for improved thermoelectric performance.
Conclusions:
- Fe-Al-Si-based thermoelectric materials show significant promise for low-cost, reliable power generation in IoT devices.
- Machine learning is a powerful tool for accelerating the discovery and optimization of thermoelectric materials.
- Further development and fabrication of FAST material-based power generation modules are underway for commercialization.
Keywords:
Fe−Al−Sidiffusion couplemachine learningmapping measurementthermoelectric materialsthermoelectric modulesMore Related Videos
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