Atomic-scale elucidation of attenuation mechanisms underlying exceptional electromagnetic wave absorption of SiZrBCN ceramic nanocomposites
Source abstract
Abstract
To elucidate the atomic-scale mechanisms governing electromagnetic wave (EMW) attenuation in polymer-derived ceramics, a SiZrBCN ceramic nanocomposite was prepared via chemical modification of polyborosilazane using tetrakis(dimethylamino)zirconium (IV), followed by pyrolysis and annealing. Advanced characterization methods combined with first-principle calculations using density functional theory (DFT) were employed to investigate the structural evolution, dielectric properties and attenuation mechanisms of the nanocomposites. The results show that after pyrolysis at T≤1400 °C, the SiZrBCN is in amorphous state. As the annealing temperature increases, ZrCxN1-x, SiC, and β-Si3N4 initially precipitate at 1500 °C. When the temperature increases to 1800 °C, the ZrCxN1-x transforms into ZrB2 forming a SiC/ZrB2 multiphase ceramic nanocomposites. With the incorporation of Zr, the SiZrBCN-16 after annealing at 1600 °C exhibits excellent EMW absorbing performance, achieving a maximum effective absorption bandwidth of 6.03 GHz (thickness: 1.65 mm) and a minimum reflection loss of -44.1 dB (thickness: 1.9 mm). In addition to the conductive loss caused by free carbon network, DFT analysis identified two primary dielectric loss mechanisms which results in exceptional absorbing performance: (1) electronegativity-driven charge separation in ZrCxN1-x solid solutions facilitates the formation of electric dipoles; (2) interfacial lattice distortion and the atomic disparity across the interface at ZrC(001)/β-Si3N4 (001) and ZrN(100)/SiC(110) boundaries induce electronic reconstruction and charge separation.
Source: openalex_inverted_index · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Electromagnetic wave absorption materials · Microwave Dielectric Ceramics Synthesis · Advanced ceramic materials synthesis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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