journal article·2024-12Open access

Synergy enhancement in low-frequency sound absorption by a nanofibre coating for the microperforated panels

Jingze Liu, R. Hugh Gong, Enza Migliore

Materials & Design· Volume 248

Abstract

• A method of coating nanofibre directly on MPP is presented. • The nanofibre coating increased the absorption coefficient and bandwidth of MPP and shift absorption to lower frequencies. • The synergy effect of nanofibre coating and MPP can be clearly observed. • The impact of different nanofibre coatings on the acoustic behaviour is analysed. The sound absorption coefficient of polyvinylidene fluoride (PVDF) and carbon nanotube (CNT) composite nanofibre-coated micro-perforated plate (MPP) is investigated. The MPP material is steel and is laser-drilled. The nanofibres are fabricated by electrospinning and directly coated onto the steel MPP collector. This makes it possible to attach extremely thin coatings of nanofibres. The sound absorption coefficient was measured using the impedance tube method. The results show that MPP absorbers with nanofibre coatings significantly widen the absorption bandwidth and increase the absorption coefficient. Because of the synergy enhancement effect, the performance of the absorber is much exceed the expect of the transfer matrix method (TMM) model. A very thin coating of nanofibres enables a significant increase in MPP performance. The effects of the nanofibre coatings were analyzed by finite element method (FEM) simulations. The coating enhances the end effect of MPP and extends along the MPP surface. In addition, different nanofibre coating thickness and impedance also have different effects on the sound absorption performance. This study provides a new method to enhance the performance of MPP with negligible increase in thickness and costs.
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Research context

OpenAlex topics: Acoustic Wave Phenomena Research · Aerosol Filtration and Electrostatic Precipitation · Cellular and Composite Structures

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

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