材料科学
微波食品加热
反射损耗
光电子学
碳化硅
介电损耗
吸收(声学)
石墨烯
复合材料
纳米流体
电介质
复合数
宽带
电阻抗
阻抗匹配
电导率
电阻率和电导率
硅
电磁辐射
反射(计算机编程)
衰减
回波损耗
微波加热
下降(电信)
表面粗糙度
电子工程
带宽(计算)
电极
作者
Hanjie Huang,Qianqian Niu,Ying Huang,H. Jiang,Xiaoxiao Zhao,Jiale Ma,Haiou Zhu,Meng Zong
标识
DOI:10.1021/acsanm.5c04786
摘要
Although traditional graphene-based absorbing materials exhibit excellent dielectric loss properties, they are prone to agglomeration and possess excessively high electrical conductivity, which can cause impedance mismatch and consequently degrade absorption performance. These limitations significantly constrain their application in electromagnetic wave absorption. Therefore, integrating graphene with other materials to reduce agglomeration, lower electrical conductivity, and enhance absorption performance is of great importance. This study proposes a low-solvent nanofluid composite strategy, where graphene oxide-coated silicon carbide (GO/SiC) serves as the core structure, with 3-Glycidoxypropyltrimethoxysilane (KH560) and Jeffamine M2070 (M2070) functioning as the corona and halo layers, respectively. Compared with graphene, GO induces additional surface defects, thereby enhancing dipole polarization; while SiC mitigates the excessive electrical conductivity of graphene, optimizes impedance matching, and creates heterogeneous interfaces to promote multiple internal reflections and interfacial effects. Moreover, the synergistic effect between KH560 and M2070 effectively suppresses GO agglomeration, enhances material fluidity, and facilitates interface heterogeneity. Compared with previously reported SiC-based composites (e.g., MWCNT/SiC with RLmin = −38.7 dB and EAB = 4.6 GHz), the GO/SiC-M2070 series composites in this study exhibit superior microwave absorption performance, with a minimum reflection loss (RLmin) as low as −47.0 dB and a maximum effective absorption bandwidth (EAB) of 7.94 GHz. This provides insights for the design of next-generation broadband and high-performance microwave absorbing materials.
科研通智能强力驱动
Strongly Powered by AbleSci AI