.png){kind=icon}
.png){kind=icon}
Return
ZT Optimization: An Application Focus
.png){kind=icon}
OA
AI
DOI：10.3390/ma10030309.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Significant research has been performed on the challenge of improving thermoelectric materials, with maximum peak figure of merit, ZT, the most common target. We use an approximate thermoelectric material model, matched to real materials, to demonstrate that when an application is known, average ZT is a significantly better optimization target. We quantify this difference with some examples, with one scenario showing that changing the doping to increase peak ZT by 19% can lead to a performance drop of 16%. The importance of average ZT means that the temperature at which the ZT peak occurs should be given similar weight to the value of the peak. An ideal material for an application operates across the maximum peak ZT, otherwise maximum performance occurs when the peak value is reduced in order to improve the peak position.
Keywords:
thermoelectric
application
simulation
average ZT
optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.