Session: 03-07-01: Material Processing of Flexible/Emerging Electronics, Sensors, and Devices I

Paper Number: 99978

99978 - Machine Learning-Assisted Ultrafast Flash Sintering of High-Performance and Wearable Silver-Selenide Thermoelectric Devices 

Flexible Thermoelectric Generators (TEGs) are promising candidates for developing self-powered wearable devices and industrial Internet of Things. Flexible TEGs are lightweight, compact, and maintenance-free solid-state energy convertors with no moving parts that directly convert heat into electricity, and they can easily conform to a variety of heat sources with curved surfaces (e.g., body heat). A key challenge preventing large-scale applications of TEG lies with the lack of high-throughput processing method which can sinter thermoelectric (TE) materials rapidly while maintaining their high thermoelectric properties.

Despite the significant progress in thermoelectric materials to date, Bi₂Te₃-based alloys remain as dominant materials for thermoelectric applications near room temperature, and the zT for n-type TE materials still remains below or around unity at room temperature. In addition, the scarcity of tellurium (Te) necessitates the development of new tellurium-free thermoelectric materials for use in widespread industrial and wearable applications. Silver-selenide (Ag₂Se) is a narrow band gap n-type chalcogenide and an ideal candidate for room temperature applications owing to its high power factor (PF) and low intrinsic thermal conductivity. Sintering is an essential step in materials processing to improve transport properties. Sintering transforms TE particles into a dense structure with improved thermoelectric properties. Conventional thermal sintering requires hours of processing time at elevated temperatures which hinders the widespread development of flexible TEGs on organic substrates of low melting point (e.g., polymers, fabrics).

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 high-dimension optimization problem of flash sintering processes, a Gaussian Process Regression 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 (power factor of 2205 μW/mK² with 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 conventional thermal sintering techniques. The films also show excellent flexibility with 92% retention of the power factor after 10³ 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/cm² at a temperature difference of 10 K. This work not only shows tremendous potential of high-performance and flexible silver-selenide thermoelectric generators, 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.

Presenting Author: Mortaza Saeidi-Javash University of Notre Dame

Presenting Author Biography: Mortaza Saeidi-Javash is a Postdoctoral Research Fellow at the University of Notre Dame. He received his PhD in Mechanical Engineering from the University of Notre Dame in 2022, and his M.S. and B.S. degrees in Mechanical Engineering from the University of Oklahoma and University of Tabriz in 2017 and 2012, respectively. His research interests include microscale additive manufacturing of functional devices for thermoelectric energy harvesting and sensing.

Authors:

Mortaza Saeidi-Javash University of Notre Dame
Ke Wang University of Notre Dame
Minxiang Zeng University of Notre Dame
Tengfei Luo University of Notre Dame
Alexander Dowling University of Notre Dame
Yanliang Zhang University of Notre Dame