Thermal insulation and structural performance of sandwich-structured SiC nanowire aerogels prepared via a one-step synthesis
Source abstract
Abstract
Silicon carbide (SiC) aerogels hold immense promise for extreme environment applications; however, conventional homogeneous architectures hinder their multifunctional integration. Herein, a sandwich-structured SiC nanowire aerogel with gradient porosity was engineered through a one-step in situ growth strategy, combining a dense sub-micropore (<1 μm) shell and a macroporous (10–360 μm) core. The nanoconfined pores of the shell suppress gas-phase thermal transport by limiting molecular collisions, while the air-entrapped macropores of the core minimize solid-phase conduction, synergistically yielding a low thermal conductivity of 0.05 W/(m·K), 33% lower than that of the homogeneous counterparts. The continuous gradient interface eliminates interfacial delamination and redistributes stress, achieving a strong mechanical resilience (11.2 kPa compressive strength) via shell-layer nanowire friction and elastic recovery (90% strain retention after 100 cycles) through core-layer dendritic flexibility. Single-crystal nanowires, stabilized by a self-passivating amorphous layer (∼20 nm), ensure structural integrity at 1400 °C with negligible oxidation. Furthermore, the hierarchical architecture facilitates broadband microwave absorption via gradient impedance matching and multiscale reflections. By integrating template-guided polymer conversion and catalyst-directed nanowire assembly, this work pioneers a scalable paradigm for multifunctional aerogels that combine extreme thermal insulation, mechanical durability, and microwave absorption properties, providing a transformative solution for next-generation aerospace thermal protection systems.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Aerogels and thermal insulation · Advanced ceramic materials synthesis · Silicon Carbide Semiconductor Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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