Frequency-tunable acoustic absorption in anisotropic graphene aerogels via morphological engineering of internal barriers

Abstract

The development of high-efficiency acoustic attenuation media has been significantly advanced by the integration of graphene derivatives and carbon nanotubes, taking advantage of their superior chemical and mechanical attributes. Nevertheless, a primary obstacle in engineering these materials involves the precise morphological control of low-dimensional nanostructures—specifically regarding the prevention of random restacking and the achievement of uniform directional alignment within the porous framework. Furthermore, systematic investigations into the acoustic properties of pure graphene aerogels—specifically regarding how flake dimensions and loading influence the barrier effect and structural anisotropy—remain largely unexplored. To address these challenges, this research demonstrates a systematic approach to engineering anisotropic graphene-based aerogels featuring frequency-tunable acoustic absorption through synergistic modulation via bidirectional freeze-casting. By employing the Ice-Segregation-Induced Self-Assembly (ISISA) process, the study successfully fabricated highly ordered, vertically and horizontally aligned lamellar networks. Morphological investigations revealed a distinct structural divergence: aerogels based on large graphene flakes formed continuous, streamlined micro-channels with minimal structural resistance, whereas variants utilizing smaller flakes developed a high density of internal structural barriers, or septa. The superior performance of these structures is driven by intensified visco-thermal energy dissipation and multiple scattering effects, both of which are facilitated by increased tortuosity and internal air-flow resistance. Furthermore, by manipulating the spatial orientation of the framework relative to the incident sound vector, we proposed a mechanism to explain how the barrier effect sustains enhanced absorption in transverse modes compared to longitudinal ones. These findings establish a robust structure–property relationship between microscopic precursor dimensions and macroscopic hierarchical architecture, offering a new paradigm for high-performance, multifunctional carbon-based noise insulators.