Low-Loss Dual-Band Transparency Metamaterial with Toroidal Dipole.

low-loss metamaterial sensing slow light toroidal dipole

Journal

Materials (Basel, Switzerland)
ISSN: 1996-1944
Titre abrégé: Materials (Basel)
Pays: Switzerland
ID NLM: 101555929

Informations de publication

Date de publication:
19 Jul 2022
Historique:
received: 14 06 2022
revised: 14 07 2022
accepted: 15 07 2022
entrez: 27 7 2022
pubmed: 28 7 2022
medline: 28 7 2022
Statut: epublish

Résumé

In this paper, a low-loss toroidal dipole metamaterial composed of four metal split ring resonators is proposed and verified at microwave range. Dual-band Fano resonances could be excited by normal incident electromagnetic waves at 6 GHz and 7.23 GHz. Analysis of the current distribution at the resonance frequency and the scattered power of multipoles shows that both Fano resonances derive from the predominant novel toroidal dipole. The simulation results exhibit that the sensitivity to refractive index of the analyte is 1.56 GHz/RIU and 1.8 GHz/RIU. Meanwhile, the group delay at two Fano peaks can reach to 11.38 ns and 12.85 ns, which means the presented toroidal metamaterial has significant slow light effects. The proposed dual-band toroidal dipole metamaterial may offer a new path for designing ultra-sensitive sensors, filters, modulators, slow light devices, and so on.

Identifiants

pubmed: 35888479
pii: ma15145013
doi: 10.3390/ma15145013
pmc: PMC9317833
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : National Natural Science Foundation of China
ID : 61741104
Organisme : Science and technology Foundation of Guizhou Province
ID : ZK[2021]306
Organisme : Guizhou Province Education Department Projects
ID : KY[2020] 007

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Auteurs

Tianyu Xiang (T)

School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550003, China.

Tao Lei (T)

School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550003, China.
State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China.

Ting Chen (T)

School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550003, China.

Zhaoyang Shen (Z)

Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, College of Computer and Information Technology, China Three Gorges University, Yichang 443005, China.

Jing Zhang (J)

School of Big Data and Computer Science, Guizhou Normal University, Guiyang 550003, China.

Classifications MeSH