Top-down parametrization-design of orientation-reinforced SiOC-based perfect metamaterial microwave absorber with wide-temperature adaptability
Source abstract
Abstract
Conventional experientialism-inspired and intuition-inspired research on microwave-absorbing (MA) materials appears to lack efficiency. The present work aims to establish a scalable parametrization-design methodology for multifunctional coupled MA metamaterials involving up-front theoretical calculation and simulation predictions followed by experimental verification. A top-down parametrization-design methodology is proposed herein, which relies on the utilization of polymer-derived ceramics (PDCs) with flexible electromagnetic tunability as substrate materials, combined with tunable electromagnetic response via mathematical modeling of triply-periodic-minimal-surfaces shellular structures. In the process, preferred structural configuration and orientation are screened with actual requirements while final fabrication in a single step is enabled by 3D printing technology. The effect of structural configuration and orientation on electromagnetic response is scrutinized and a novel optimization method for deterministic high-temperature MA properties is proposed. As-fabricated [111]-oriented Gyroid shellular MA metamaterials exhibit superior overall performance in the X-Ku band with wide-temperature adaptability. At room temperature, the minimal reflection loss ( RL min) value is –58.05 dB, effective absorbing bandwidth (EAB) with RL ≤ –10 dB reaches 6.11 GHz, and the specific strength reaches 65.20 MPa/(g/cm 3) at the ultralow density of 0.550 g/cm 3. RL min improves to –72.38 dB at 100 °C while EAB increases to 6.77 GHz at 300 °C and retains 5.60 GHz at 600 °C.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Electromagnetic wave absorption materials · Advanced Antenna and Metasurface Technologies · Metamaterials and Metasurfaces Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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