Predicting the thermal conductivity of Bi2Te3-based thermoelectric energy materials: A machine learning approach-Reference-Cited by-同舟云学术

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Predicting the thermal conductivity of Bi2Te3-based thermoelectric energy materials: A machine learning approach

Published:2022-11 Issue: Volume:181 Page:107784
ISSN:1290-0729
Container-title:International Journal of Thermal Sciences
language:en
Short-container-title:International Journal of Thermal Sciences

Author:

Alrebdi T.A.,Wudil Y.S.,Ahmad U.F.,Yakasai F.A.,Mohammed J.,Kallas F.H.

Funder

Princess Nourah Bint Abdulrahman University

Publisher

Elsevier BV

Subject

General Engineering,Condensed Matter Physics

Reference47 articles.

1. Review of recent developments and persistent challenges in stability of perovskite solar cells;Salhi;Renew. Sustain. Energy Rev.,2018

2. High-thermoelectric performance of nanostructured bismuth antimony telluride bulk alloys;Poudel;Science,2008

3. Decoupled phononic-electronic transport in multi-phase n-type half-Heusler nanocomposites enabling efficient high temperature power generation;Kang;Mater. Today,2020

4. The multi-dimensional approach to synergistically improve the performance of inorganic thermoelectric materials: a critical review;Wudil;Arab. J. Chem.,2021

5. Substrate temperature-dependent thermoelectric figure of merit of nanocrystalline Bi2Te3 and Bi2Te2.7Se0.3 prepared using pulsed laser deposition supported by DFT study;Wudil;Ceram. Int.,2020

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