journal article·2026-07

Hierarchical cellulose nanocrystals/graphene aerogels with engineered microstructures for high-efficiency electromagnetic absorption and thermal management

Jie Zha, Daili Feng, Wenjun Li, Jianyin Miao, Yanhui Feng

Carbon· Volume 258
Publisher page

Abstract

To address the urgent demand for high-performance, multifunctional microwave absorbing materials in complex electromagnetic environments for modern electronic devices and aerospace equipment, this study successfully fabricated cellulose nanocrystals/graphene composite aerogels with a hierarchical porous structure. This was achieved by combining cellulose nanocrystals doping with supercritical CO2 drying or freeze-drying processes. The results indicate that the skeletons constructed via the supercritical drying process possess a more uniform micro-nano pore structure and higher structural stability. Appropriate doping with cellulose nanocrystals can optimize the arrangement of graphene sheets, significantly enhancing the material's thermal insulation performance and mechanical resilience. Application tests demonstrate that the composite aerogels exhibit excellent performance in multiple areas: thermal insulation (with a backside temperature rise of only 49.2°C under a 130°C heat source), electromagnetic wave absorption (achieving a reflection loss of -64.5 dB and a maximum effective absorption bandwidth of 5.18 GHz), and electrothermal conversion (reaching 113.7°C under a driving voltage of 6.0 V). This work provides an effective material solution for developing lightweight, high-strength, and integrated multifunctional materials for electromagnetic protection and thermal management, tailored to meet the requirements of applications in extreme environments.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Cellulose Research Studies · Aerogels and thermal insulation · Electromagnetic wave absorption materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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