Enhanced Polarization from Hollow Cube-like ZnSnO3 Wrapped by Multiwalled Carbon Nanotubes: As a Lightweight and High-Performance Microwave Absorber.
Lei WangXiao LiQingqing LiYunhao ZhaoRenchao ChePublished in: ACS applied materials & interfaces (2018)
Polarization and conduction loss play fundamentally important roles in the nonmagnetic microwave absorption process. In this paper, a uniform and monodisperse hollow ZnSnO3 cube wrapped by multiwalled carbon nanotubes (ZSO@CNTs) was successfully synthesized via facile hydrothermal treatment. A reasonable mechanism related to Ostwald ripening was proposed to design the varied ZSO@CNTs for the special hollow conductive network. Scanning electron microscopy images clearly indicate that reaction temperature is the key factor for the composite structure, which has a significant effect on its electromagnetic properties. Electron holography proves the inhomogeneous distribution of charge density in the ZSO@CNT system, leading to the occurrence of interface polarization. Complex permittivity properties of ZSO@CNT composites under different reaction temperatures were investigated to optimize the morphology that can distinctly enhance microwave absorption performance. The maximum reflection loss that the ZSO@CNT-130 °C composite can reach is -52.1 dB at 13.5 GHz, and the absorption bandwidths range from 11.9 to 15.8 GHz with a thickness as thin as 1.6 mm. Adjusting the simulation thicknesses from 1 to 5 mm, the efficient absorption bandwidth (RL < -10 dB) that the ZSO@CNT composite could reach was 14.16 GHz (88.8% of 2-18 GHz). The excellent microwave absorption performance may be attributed to the synergistic effects of polarization, conduction loss, and special hollow cage structure. It is proposed that the specially controlled structure could provide an effective path for achieving a high-performance microwave absorber.
Keyphrases
- carbon nanotubes
- electron microscopy
- radiofrequency ablation
- metal organic framework
- molecularly imprinted
- reduced graphene oxide
- optical coherence tomography
- highly efficient
- risk assessment
- deep learning
- machine learning
- high resolution
- high frequency
- cancer therapy
- convolutional neural network
- drug delivery
- tissue engineering