材料科学
反射损耗
兴奋剂
电磁辐射
煅烧
电导率
化学工程
电介质
介孔材料
吸收(声学)
杂原子
介电损耗
电磁屏蔽
碳纤维
光电子学
聚合
介电常数
极化(电化学)
多孔性
退火(玻璃)
偶极子
纳米技术
电阻抗
热液循环
电磁学
电容
作者
Shijie Zhang,Jiajun Zheng,Xiaowei Liang,Di Lan,Liyong Niu,Xiaomiao Zhao,Zhiwei Zhao,Siyuan Zhang,Guanglei Wu,Xiaochen Li
出处
期刊:Small
[Wiley]
日期:2025-09-18
卷期号:21 (45): e09237-e09237
被引量:12
标识
DOI:10.1002/smll.202509237
摘要
Phosphorus (P) doping has emerged as an effective strategy for tailoring the electromagnetic properties of carbon-based materials by modulating defect density, dielectric polarization, and electrical conductivity. Herein, a series of porous phosphorus-doped carbon hollow platelets (PCHPs) with well-defined biconcave morphologies are synthesized through a template-assisted polymerization strategy followed by hydrothermal phosphorylation and calcination. By systematically adjusting the phosphorus content and calcination temperature, we achieved precise control is achieved over the defect concentration, graphitization degree, and electrical conductivity. Theoretical calculations combined with experimental characterizations revealed that P atoms induce local charge redistribution and form P─C/P─O/P═O bonds, generating abundant dipole centers and enhancing polarization relaxation. Notably, the PCHP-750-0.2 specimen demonstrated optimal electromagnetic waves absorption performance, achieving a minimum reflection loss of -34.05 dB at 1.8 mm and an effective absorption bandwidth of 5.08 GHz at 2.0 mm. The enhanced absorption arisen from the synergistic effects of defect-induced polarization, moderate conductivity for impedance matching, and multi-scattering pathways enabled by the hollow platelet structure. Furthermore, CST simulations confirmed superior radar cross-section reduction capability. This work not only proposes a versatile route for engineering phosphorus-doped carbon architectures, but also provides in-depth insight into the structure-property-performance relationships that govern high-efficiency electromagnetic waves attenuation.
科研通智能强力驱动
Strongly Powered by AbleSci AI