Researchers develop super-fast new method for manufacturing high-performance thermoelectric devices
Yanliang Zhang, an associate professor of aerospace and mechanical engineering at the University of Notre Dame, and collaborators Alexander Dowling and Tengfei Luo have developed a new, super-fast machine learning-powered method to generate high-efficiency, energy-saving thermoelectric devices.
The novel process uses intense pulsed light to sinter thermoelectric materials in less than a second (conventional sintering in a thermal furnace can take hours). The team accelerated this method to turn nanoparticle ink into flexible device using machine learning to determine the optimal conditions for extremely fast but complex sintering.
The achievement has just been published in the magazine Energy and environmental science.
Flexible thermoelectric devices offer great opportunities for direct conversion of waste heat into electricity as well as solid-state cooling, Zhang said. They have additional benefits such as power and cooling – they emit no greenhouse gases, are durable and quiet because they have no moving parts.
Despite their potentially vast impact on energy and sustainable environment, thermoelectric equipment Large-scale application has not yet been achieved because of the lack of a fast and cost-effective automated production method. Now, super-fast flash sintering powered by machine learning will make it possible to produce high-performance, eco-friendly devices much faster and at a much lower cost.
“The results could be applied to power everything from wearable personal devices, to sensors and electronics, to the Internet of Things in the industry,” said Zhang.
“Successful integration of photonic flash processing and machine learning can be generalized to highly scalable and low-cost production of a wide range of power and electronic materials.”
Mortaza Saeidi-Javash et al., Machine learning-powered ultrafast flash sintering of high-performance and flexible silver-selenide thermoelectric devices, Energy & Environmental Science (In 2022). DOI: 10.1039 / D2EE01844F
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