Combinatorial screening via high-throughput preparation: Thermoelectric performance optimization for n-type Bi-Te-Se film with high average ZT > 1

JOURNAL OF MATERIALS SCIENCE & TECHNOLOGY(2023)

引用 2|浏览0
暂无评分
摘要
Thermoelectric materials have drawn extensive interest due to the direct conversion between electricity and heat, however, it is usually a time-consuming process for applying traditional "sequential" methods to grow materials and investigate their properties, especially for thermoelectric films that typically require fine microstructure control. High-throughput experimental approaches can effectively accelerate materials development, but the methods for high-throughput screening of the microstructures require further study. In this work, a combinatorial high-throughput optimization solution of material properties is proposed for the parallel screening and optimizing of composition and microstructure, which involves two distinctive types of high-throughput fabrication approaches for thin films, along with a new portable multiple discrete masks based high-throughput preparation platform. Thus, Bi 2 Te 3-x Se x thin film library with 196 throughputs for locating the optimized composition is obtained in one growth cycle. In addition, another thin film library composed of 31 materials with traceable process parameters is built to further investigate the relationship between microstructure, process, and thermoelectric performance. Through high-throughput screening, the Bi 2 Te 2.9 Se 0.1 film with (0 0l) orientation is prepared with a peak zT value of 1.303 at 353 K along with a high average zT value of 1.047 in the interval from 313 to 523 K. This method can be also extended to the discovery of other functional thin films with a rapid combinatorial screening of the composition and structure to accelerate material optimization. (c) 2023 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
更多
查看译文
关键词
thermoelectric performance optimization,high-throughput average zt,n-type
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要