材料科学
陶瓷
电介质
极地的
微波食品加热
介电损耗
谐振器
钙钛矿(结构)
温度系数
光电子学
表征(材料科学)
放松(心理学)
理论(学习稳定性)
介电常数
介质谐振器
复合材料
电子工程
化学稳定性
熵(时间箭头)
作者
Zhihan Zhang,Hongyu Yang,Yihang Li,Linzhuang Xing,Dongyan Zhang,Zhimin Li,Yue Hao
摘要
ABSTRACT The deployment of 5G/6G communication systems demands microwave dielectric ceramics that simultaneously possess a near‐zero temperature coefficient of resonant frequency and an ultra‐low dielectric loss (tan δ ), which represents a long‑standing challenge that demands effective optimization strategies. Herein, the fundamental limitation is substantially alleviated by a strategy integrating interpretable machine learning with entropy engineering, enabling the reverse design of a medium‐entropy perovskite microwave ceramic composition (Ca 0.7 Sm 0.27 Nd 0.03 )[Ti 0.686 (Mg 1/3 Nb 2/3 ) 0.014 Al 0.3 ]O 3 . This ceramic material delivers a desired combination perfomance of τ f = −0.18 ppm/°C and tan δ = 1.19 × 10 −4 at 4.6 GHz. Multiscale characterization reveals that entropy‐induced chemical disorder creates isolated polar nanoregions that pin charged defects, transforming them from a source of relaxation loss into static structural units, which is different from the conventional defect–loss correlation mechanism. The optimized ceramic is implemented in a dielectric resonator antenna, which demonstrates superior performance: a radiation efficiency of 96.2% and a gain of 6.06 dBi. The findings establish a generalizable framework for navigating complex compositional spaces and resolving conflicting properties in perovskite materials.
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