Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices-Reference-Cited by-同舟云学术

Machine learning-assisted ultrafast flash sintering of high-performance and flexible silver–selenide thermoelectric devices

Published:2022 Issue:12 Volume:15 Page:5093-5104
ISSN:1754-5692
Container-title:Energy & Environmental Science
language:en
Short-container-title:Energy Environ. Sci.

Author:

Saeidi-Javash Mortaza¹²^ORCID,Wang Ke³,Zeng Minxiang¹⁴,Luo Tengfei¹³,Dowling Alexander W.³^ORCID,Zhang Yanliang¹^ORCID

Affiliation:

1. Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

2. Department of Mechanical and Aerospace Engineering, California State University Long Beach, Long Beach, CA 90840, USA

3. Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

4. Department of Chemical Engineering, Texas Tech University, Lubbock, TX 79409, USA

Abstract

The first machine learning-assisted ultrafast flash sintering of flexible silver–selenide TE devices. Bayesian optimization of flash sintering variables led to a PF of 2205 μW m−1 K−2 and a zT of 1.1 at room temperature realized with a sintering time less than 1.0 second.

Funder

Division of Civil, Mechanical and Manufacturing Innovation

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Environmental Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/EE/D2EE01844F

Reference65 articles.

1. Flexible thermoelectrics: from silver chalcogenides to full-inorganic devices

2. High-performance compliant thermoelectric generators with magnetically self-assembled soft heat conductors for self-powered wearable electronics