Phononic crystal locally-resonant cavity for sensing metallic oxides nano-powders

Teymouri, Soha; Ahmadi, Hamzeh; Rostami, Ali; Matloub, Samiye

doi:10.1016/j.ijmecsci.2021.106658

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…This appendix discusses the variations in energy-localization performance due to damping effects [41][42][43]. In the transient analysis, Rayleigh damping should be considered if the materials used represent viscoelastic characteristics.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

Impact of Input Signal Characteristics on Energy-Localization Performance of a Phononic Crystal with a Defect: A Comparative Study of Burst and Continuous Wave Excitation

2023

Crystals

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This study examines the energy-localization performance of a one-dimensional phononic crystal (PnC) with a defect when exposed to burst waves of different cycle numbers under longitudinal waves. Using the finite element method, band structures of the defect-introduced PnC were calculated, revealing a phononic band-gap range, defect-band frequencies, and corresponding defect-mode shapes. The transient analysis examined the longitudinal displacement at the center of this defect in the time domain for various burst-wave scenarios. The results indicate that energy-localization performance inside the defect highly depended on the number of cycles. Energy-localization performance was better with larger cycles or continuous waves, although burst waves with a small number of cycles also showed some improvement, albeit limited. Moreover, burst waves with a small number of cycles did not clearly induce fixed-like boundary conditions (in other words, nodal points in standing waves) within the defect-introduced PnC, leading to obscure energy-localized behaviors. Key messages from this work can be summarized as follows. First, comparing the energy-localization performance under incident burst waves with different cycle numbers for different systems might not be appropriate. Second, the physically reasonable formation of defect-mode-enabled energy localization requires burst waves with a large (in the case study, over 500) number of cycles.

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Section: Conflicts Of Interestmentioning

confidence: 99%

Impact of Input Signal Characteristics on Energy-Localization Performance of a Phononic Crystal with a Defect: A Comparative Study of Burst and Continuous Wave Excitation

2023

Crystals

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“…These kinds of resonators usually have higher quality factors than traditional micro-mechanical resonator devices. In addition, some novel designs can be achieved by using phononic crystals, such as acoustic metamaterials based on local resonance, (13) the negative refraction effect, (14) and the topological phononic crystal structure. (15) The abovementioned applications, devices, and phenomena are all related to the phononic band structure, whose diagram represents the dispersion relations of acoustic waves propagating in all directions within the phononic crystal.…”

Section: Introductionmentioning

confidence: 99%

Predicting Band Structures of Two-Dimensional Phononic Crystal Slab for Sensor Predesigning Based on Artificial Neural Network

Chiang,

Tsai,

Chang

et al. 2023

Sensors and Materials

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A phononic crystal is an artificial material with spatially elastic modulus usually designed for sensing. By using Bloch's theorem and the concept of the Brillouin zone, the phononic band structure can be obtained. The phononic crystal slab is formed by arranging two-dimensional periodic structures in an elastic slab. The periodic structure can control the propagation direction of the elastic wave, which is parallel to the slab and has great potential for many applications. However, it is time-consuming to determine the proper design. The artificial neural network is a promising tool for solving complex problems. We aim to train a neural network to predict the phononic band structure of silicon phononic crystal slabs. The training data are obtained by the finite element method. Our results show that the proposed artificial neural network can rapidly predict the eigenfrequencies of the band structure with high accuracy.

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Exploring surface plasmon resonance ring resonator structure for high sensitivity and ultra-high-Q optical filter with FDTD method

Krishnamoorthy¹,

Soubache²,

Farmani

2022

Opt Quant Electron

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Phononic crystal locally-resonant cavity for sensing metallic oxides nano-powders

Cited by 7 publications

References 48 publications

Impact of Input Signal Characteristics on Energy-Localization Performance of a Phononic Crystal with a Defect: A Comparative Study of Burst and Continuous Wave Excitation

Impact of Input Signal Characteristics on Energy-Localization Performance of a Phononic Crystal with a Defect: A Comparative Study of Burst and Continuous Wave Excitation

Predicting Band Structures of Two-Dimensional Phononic Crystal Slab for Sensor Predesigning Based on Artificial Neural Network

Exploring surface plasmon resonance ring resonator structure for high sensitivity and ultra-high-Q optical filter with FDTD method

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