Reed-Inspired Three-Dimensional Printed Microcolumn Array Reinforced Hierarchically Structured Composites for Efficient Noise Reduction
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 mi...
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Veröffentlicht in: | ACS applied polymer materials 2024-09, Vol.6 (17), p.10706-10717 |
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Sprache: | eng |
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Zusammenfassung: | 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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ISSN: | 2637-6105 2637-6105 |
DOI: | 10.1021/acsapm.4c01852 |