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Optical and Quantum Electronics

Erschienen in:

01.03.2022

Comparative study on sensitivity enhancement of a graphene based nearly guided-wave surface plasmon resonance biosensor optimized using genetic algorithm in the visible region

verfasst von: Shujing Chen, Zhining Lin, Gaiyan Bai, Chengyou Lin

Erschienen in: Optical and Quantum Electronics | Ausgabe 3/2022

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Abstract

In this paper, we investigate sensitivity enhancement of a graphene based nearly guided-wave surface plasmon resonance (NGWSPR) biosensor in the visible region. The proposed biosensor is optimized using genetic algorithms to obtain the highest sensitivity for a specific wavelength of incident light. The optimized sensitivity of the NGWSPR sensor is compared with a traditional surface plasmon resonance (SPR) biosensor at the wavelength of incident light varied from 400 to 800 nm. The percentage of sensitivity increment of a NGWSPR sensor relative to a traditional SPR sensor is also calculated. As high as 393.3, 542.5, 388.9 or 132.1% sensitivity increment percentage has been demonstrated for an Au, Ag, Cu or Al based NGWSPR sensor. Furthermore, the maximum sensitivity of a NGWSPR biosensor at different wavelength is also investigated and compared. For a NGWSPR sensor based on Au, Ag, Cu or Al, the maximum sensitivity is 225.12, 302.26, 222.04, or 163.78 °/RIU showing at 720, 480, 680 or 400 nm respectively.

Vorheriger Artikel Functionalized electrospun nanofibers integrated with Ag/Au nanoparticles as a platform for enhanced nonlinearity

Nächster Artikel Non-Hermitian electronics multipods of electromagnetically induced transparency (EIT) and absorption (EIA)

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Titel: Comparative study on sensitivity enhancement of a graphene based nearly guided-wave surface plasmon resonance biosensor optimized using genetic algorithm in the visible region
verfasst von: Shujing Chen
Zhining Lin
Gaiyan Bai
Chengyou Lin
Publikationsdatum: 01.03.2022
Verlag: Springer US
Erschienen in: Optical and Quantum Electronics / Ausgabe 3/2022
Print ISSN: 0306-8919
Elektronische ISSN: 1572-817X
DOI: https://doi.org/10.1007/s11082-022-03584-0

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