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Acoustic Metamaterials: A Review of Theories, Structures, Fabrication Approaches, and Applications
Abstract
Acoustic metamaterials (AMs), which are materials composed of subwavelength periodic artificial structures with specific designs, have drawn significant research attention. This is because of the extraordinary abilities of AMs to manipulate acoustic waves compared with those of traditional acoustic materials. In this review, current advances in AM research are comprehensively investigated and summarized. First, major theories regarding AMs, including the acoustic wave equation, crystal lattice and energy band theory, effective medium theory, and general Snell's law, are explained in detail. Existing AM structures are then described and divided into several categories based on their structural characteristics and responses to acoustic waves. Furthermore, to bridge the gap between design and practical application, both conventional and advanced AM manufacturing methods are summarized. The applications of AMs in the fields of acoustic cloaking, acoustic lensing, acoustic absorption, noise reduction, and supernormal sound transmission are particularly delineated. Finally, contemporary challenges, trends, and strategies relevant to AMs are discussed. This review aims to provide a comprehensive collection of AM‐related knowledge, including information regarding physical theories, structures, fabrication approaches, and applications, which will help promote the further development of AMs.
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Metadata
- Authors
- Liao, G., Luan, C., Wang, Z., Liu, J., Yao, X., & Fu, J.
- Venue
- Advanced Materials Technologies
- Publisher
- Wiley
- Published
- 2021-02-19
- Volume
- 6
- Issue
- 5
- ISSN
- 2365-709X (ISSN-L)
- OpenAlex
- W3131834934
- Cited by
- 267
- References
- 131
- FWCI
- 8.96
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
