Reed-Inspired Three-Dimensional Printed Microcolumn Array Reinforced Hierarchically Structured Composites for Efficient Noise Reduction

材料科学 复合数 复合材料 降噪系数 多孔性 隔音 吸收(声学) 气凝胶 声压 噪音(视频) 声学 计算机科学 物理 图像(数学) 人工智能
作者
Fanchao Liang,Lingjie Yu,Yinchong Peng,Yuyang Zhu,Meng Jia-guang,Haodong Ma,Wei He,Jianglong Chen,Yaming Liu,Yongzhen Wang,Yang Dai,Chao Zhi
出处
期刊:ACS applied polymer materials [American Chemical Society]
标识
DOI:10.1021/acsapm.4c01852
摘要

Against the background that noise pollution has become a global problem, it is a challenge to prepare acoustic functional materials that combine strong low-frequency sound absorption at low thicknesses with excellent mechanical and thermal insulation properties. Inspired by natural reed, a unique microcolumn array was three-dimensional printed by stereolithography (SLA) and combined with sodium alginate aerogel (SA) and polyurethane (PU) foam to design a highly efficient acoustic composite (PC-FMPPL composite), featuring both "cavity-like" and "filled microperforated plate-like" structures. The combination of multiple sound-absorption mechanisms including resonance and porous sound absorption, along with the cavity-like structure, contributes to the excellent sound-absorption performance of this composite material, even at low thickness. Specifically, the noise reduction coefficient per unit thickness of the PC-FMPPL composite exceeds that of most reported acoustic materials. Furthermore, the PC-FMPPL composite exhibits a low thermal conductivity of 0.036 W m–1·K–1 due to their intricate porous structure. Moreover, the microcolumn array provides support and resilience, resulting in excellent recovery and stability of the PC-FMPPL composite after 50 compression cycles. These favorable properties suggest promising applications for this highly efficient low-frequency acoustic composite in various fields, including architecture, transportation, and engineering. In addition, the proposed machine-learning-based sound-pressure prediction method for laminated composite offers the significant advantage of fast prediction speed (the trained machine-learning model predicts sound-pressure distribution of materials with different thickness ratios in just 0.4 s) while ensuring high accuracy, providing empirical support for predicting the acoustic performance of various types of laminated materials.
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